# Health Risk and Exposure Assessment for Ozone Final Report Chapter 5 Appendices

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# 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 a nd 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 Ca rolina 27711 (email: hubbell.bryan@epa.gov).

# CHAPTER 5 APPENDICES

Title Pages APPENDIX 5A: Description of th e 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 Expos ure Results (5F-1 to 5F-55) APPENDIX 5G: Targeted Evalua tion of Exposure Model Input and Output Data (5G-1 to 5G-62)

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# 5A-i 

# APPENDIX 5A

Description of the Air Pollut ants Exposure Model (APEX)

Table of Contents

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

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

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

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

5A-5. CONSTRUCTION OF LONGITUDI NAL DIARY SEQUENCE ............................ 5A-5

5A-6. KEY PHYSIOLOGICAL PR OCESSES MODELED ............................................... 5A-7

5A-7. ESTIMATING MICROENVIRONMEN TAL 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-2 1

# 5A-ii 

# List of Tables

Table 5A-1. Ventilation coefficient parameter estimates ( b i 

# ) and residuals distributions ( e i 

# ) 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 micr oenvironmental and exposure con centrations 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 percen t of simulated children (ages 5-18) at or above 8-hour average O 3 

# 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 m odel 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 O 3 

# NAAQS review (Johnson et al., 1996a ; 1996b; 1996c). Since that time the model has been restructured, improved, and expa nded 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 a pproach with a probabilistic sampling approach focused on individuals, accounting for fatigue an d oxygen debt after exercise in the calculation of ventilation rates (Isaacs et al., 2008), and ne w approaches for construction of longitudinal activity patterns for simulated persons (Gle n et al., 2008; Rosenbaum et al., 2008). Major improvements to data input to the model incl ude updated air exchange rates (AERs), population census and commuting data, and the daily tim e-location-activities database. These improvements are described later in this and other Chapter 5 Appendices. APEX estimates human exposure to criteria a nd toxic air pollutants at local, urban, or regional scales using a stochastic, microenvironm ental approach. That is, the model randomly selects data on a sample of hypothetical indi viduals in an actual population database and simulates each individual’s movements through ti me 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 ambien t air quality is the same at hom e and at work) or optionally can model commuting and thus exposure at the work location for individuals who work.

The APEX model is a micr oenvironmental, longitudinal human exposure model for airborne pollutants. It is applie d 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 indivi duals. It then assembles a compos ite 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 informa tion 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 we ll-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 microenvi ronment, the user specifies the method of concentration calculation (either mass balance or regression factors, de scribed later in this paper), the relationship of the microenvironment to the ambient air, and the strength of any pollutant sources specific to that microenvironm ent. 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 a pplicability of a given

ambient location is called an air district. Any number of air di stricts can be accommodated in a model run, subject only to computer hardware li mitations. In principle, any microenvironment could be found within a given air district. Ther efore, to estimate exposures as an individual engages in activities thro ughout 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 chan ge 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 cha nges microenvironments or activities performed

within the hour. An APEX simulation includes the following steps:

1. Characterize the study area - APEX selects se ctors (e.g., census tracts) within a study area based on user-defined criteria and thus iden tifies the potentially exposed population and defines the air quality and weather input data required for the area.

2. Generate simulated individuals - APEX stochastically gene rates a sample of simulated individuals based on the census data for the st udy area and human prof ile distribution data (such as age-specific employment probabilities). The user must specify the size of the sample. The larger the sample, the more repr esentative it is of the population in the study area and the more stable the model results ar e (but also the longer the computing time). 3. Construct a long-term sequence of activity ev ents and determine br eathing rates - APEX constructs an event sequence (activity pattern ) spanning the period of simulation for each simulated person. The model then stochastically assigns breathi ng rates to each event, based on the type of activity and the physical ch aracteristics of th e simulated person. 4. Calculate pollutant concentrati ons 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 si mulated individual - Microenvironmental concentrations are time weighted based on i ndividuals’ events (i.e., time spent in the microenvironment) to produce a sequence of time-averaged exposures (or minute by minute time series) spanning th e simulation period.

6. Estimate dose - APEX can also calculate th e dose time series for each of the simulated individuals based on the exposures and br eathing rates for each event. For O 3 

# , the adverse health metric of interest is decrement in forced expiratory volum e occurring in one second (FEV 1 

# ). This algorithm responsible for combining the time series of APEX estimated exposure and breathing rates for individuals is di scussed 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 en tire simulation period. Other hourly meteorological data (humidity, wind speed, wind direction, precipita tion) can be used by

the model to estimate microenvironmenta l concentrations, but are optional. In addition, most variables used in the model algorithms are re presented by user-specified probability distributions which capture population vari ability. APEX provides great flexibility in defining model inputs and parameters, includi ng options for the frequency of selecting new values from the probability distributions. The m odel 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 va lue, gamma, logistic, lognormal, loguniform, normal, off/on, Pareto, point (constant), tr iangle, uniform, Weibull, and nonparametric

distributions. Minimum and maximum bounds can be specified for each distribution if a truncated distribution is appr opriate. There are two options for handling truncation. The generated samples outside the truncation points ca n be set to the truncation limit; in this case, samples “stack up” at the truncation points. Alte rnatively, 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 th eoretical untruncated

distribution.

# 5A-4 5A-3. DEMOGRAPHIC CHARACTERISTICS The starting point for constr ucting a simulated individua l 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 represen ted by a sector, but the de fault 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 fo r 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 sele cted first. The sector becomes the home sector for the individual, and the correspondi ng 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 si mulated individuals constitutes a random sample from the study

area. The second step in constructing a simulated individual is to dete rmine their employment status. This is determined by a probability which is a function of age, gender, and home sector. An input file is provided wh ich contains employment probabilities from the 2000 census for every combination of age (16 a nd over), gender, and census tract . APEX assumes that persons under age 16 do not commute. For persons who ar e 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 differe nt 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 ha s been prepared. This permits the entire list of non-zero flows to be specifi ed on one input file. Given a home sector, the number of destinations to which people commute varies anyw here from one to several hundred other tracts.

5A-4. ATTRIBUTES OF INDIVIDUALS In addition to the above demographic inform ation, each individual is assigned status and physiological attributes. The status variable s are factors deemed important in estimating microenvironmental concentrations, and are specif ied by the user. Status variables can include, but are not limited to, people’s housing type, whet her their home has ai r conditioning, whether they use a gas stove at home, whether the stove ha s a gas pilot light, and wh ether their car has air conditioning. Physiological variables are importa nt when estimating pollutant specific dose. These variables could include height, weight, blood volume, pulmonary diffusion rate, resting metabolic rate, energy conversion factor (lit ers of oxygen per kilocalorie energy expended),

# 5A-5 hemoglobin density in blood, maximum limit on meta bolic equivalents of work (MET) ratios (see below), and endogenous CO production rate . All of these va riables are treated probabilistically taking into account interdependenc ies 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 di stributions 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 provide d 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 relations hips developed by Burmaster (1998) that use body mass as an independent variable as follows:

6821

. 0 2781

. 2 

# BM e BSA

 

#  Equation (5A-1)

where,

BSA = body surface area (m 2 

# )

BM = body mass (kg)

5A-5. CONSTRUCTION OF LONGITUDINAL DIARY SEQUENCE The activity diary determines the sequen ce of microenvironments visited by the simulated person. A longitudinal sequence of da ily diaries must be constructed for each

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

are derived from those in the EPA's Consolid ated Human Activity Database (CHAD) (US EPA, 2000; 2002), although the user could pr ovide area specific diaries if available. There are over 53,000 CHAD diaries, each coveri ng a 24 hour period, that have been compiled from several studies. CHAD is essentially a cross-sectional da tabase that, for the most part, only has one diary per person. Therefore, APEX must a ssemble each longitudinal diary sequence for a simulated individual from many si ngle-day diaries selected from a pool of similar people.

APEX selects diaries from CHAD by matching gender and em ployment status, and by requiring that age falls within a user-specified range on either si de of the age of the simulated individual. For example, if the user specifies plus or mi nus 20%, then for a 40 year old simulated individual, the availa ble 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/nhane s/nhanes_questionnaires.htm.

# 5A-6 simulated individual therefore has an age wi ndow of acceptable diaries; these windows can partially overlap those for othe r simulated individuals. This differs from a cohort-based approach, where the age windows are fixed a nd 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 diar ies from persons of missing gender, age, or employment status. These options allow the mode l 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 diar ies from the appropriate pool for days in the simulation having matching temperature and day t ype 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 Sunda y diaries in another class. Alternatively, the user could instead create mo re than two classes of weekdays, combine all seven days into one class, or split al l seven days into separate classes. The temperature classification can be based either on daily maximum temperature, daily average temperature, or both. The user specifie s 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. Fo r example, if there are four temperature classes and two day type classe s, then there will be eight diary pools. APEX then determines the day-type and th e applicable temperature for each person’s simulated day. APEX allows multiple temperat ure 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 se ctors 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 individua l. APEX contains three algorith ms for stochastically selecting diaries from the pools to create the longitudinal diary. The first method selects diaries at random after stratification by age, gende r, and diary pool; the second method selects diaries based on metrics related to exposure (e.g., time spent outdo ors) 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 be havioral patterns (Rosenbaum 2008).

# 5A-7 The final step in processing the activity diar y is to map the CHAD location codes into the set of APEX microenvironments, s upplied by the user as an input f ile. 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 m ovement 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 perfo rm 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 ( 

I 

# V ) is slightly greater than the volume expired per

minute ( E 

## V  

# ). Clinically, however, this differen ce is not important, and by convention, the ventilation rate is always m easured by the expired volume.

The rate of oxygen consumption ( 2 O 

## V  

# ) is related to the rate of energy usage in performing activities as follows:

ECF EE V O 

#   

2 

# Equation (5A-2)

where,

2 O 

## V  

# = Oxygen consumption rate (liters O 2 

# /minute)

EE = Energy expenditu re (kcal/minute)

ECF = Energy conversion factor (liters O 2 

# /kcal).

The ECF shows little variati on and typically, commonly a value between 0.20 and 0.21 is used to represent the conversion from energy un its to oxygen consumption. APEX can randomly sample from a uniform distribution defined by th ese lower and upper bounds to estimate an ECF for each simulated individual. The activity-speci fic energy expenditure is highly variable and can be estimated using metabolic equivalents (M ETs), or the ratios of the rate of energy consumption for non-rest activities to the res ting rate of energy consumption, as follows



# RMR MET EE   Equation (5A-3)

where,

EE = Energy expenditu re (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 in dividual. 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



# F BM b b RMR ] ) ( [ 1

0 

#     Equation (5A-4)

where,

RMR = Resting metabolic rate (kcal/min)

b o 

# = Regression intercept (MJ/day)

b 1 

# = 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) de scribe an approach to estimate E 

## V  

# using 2 O 

## V 



# . 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 consump tion, the variability within age groups, and both inter- and intra-personal a nd variability. The basic algorithm is

w b O E 

## e e gender b age b BM V b b BM V         

3 2 2 1 0 

## ) 1 ( ln ) / ln( ) / ln( 

# Equation (5A-5)

where,

ln = natural logarithm of variable

BM V E 

## /  

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

b i 

# = 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 BM V O 

## / 2  

# = activity specific oxygen consum ption rate, body mass normalized (liter/O 2 

# /kg)

age = the age of the individual (years) gender = gender value (-1 for males and +1 for females)

e b 

# = randomly sampled error term for between persons N{0, se), (liter air/kg)

e w 

# = randomly sampled error term for within persons N{0, se), (liter air/kg) As indicated above, the random error ( ε ) is allocated to two va riance components used to estimate the between-person (inter-indivi dual variability) residuals distribution ( e b 

# ) and withinperson (intra-individual variabil ity) residuals distribution ( e w 

# ). The regression parameters b 0 

# , b 1 

# , b 2 

# , and b 3 

# are assumed to be constant over time for all simulated persons, e b 

# is sampled once per person, while whereas e w 

# varies from event to event. Point estimates of the regression

coefficients and standard errors of the resi duals distributions are given in Table 5A-1.

Table 5A-1. Ventilation coefficient parameter estimates ( b i 

# ) and residuals distributions ( e i 

# ) from Graham and McCurdy (2005).

Age group Regression Coefficients 1 

## Random Error 1



## b 0 

## b 1 

## b 2 

## b 3 

## e b 

## e w 

## <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 MICROENVIRO NMENTAL CONCENTRATIONS The user provides rules for determini ng the pollutant concentration in each

microenvironment. There are two available models for calculating microenvironmental concentrations: mass balance and regression fact ors. 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 microe nvironment due to deposition, filtration, and chemical degradation; and

 Emissions from sources of a polluta nt 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: 

#  

source removal out in 

# C C C C dt t dC

    

   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 un its as the ambient air quality data, e.g., ppm, ppb, ppt, or μg/m 3 

# . In the following equations con centration is shown only in μg/m 3 

# for brevity. The change in microenvironmental concentration due to influx of air, 

# C  in 

# , is given by:

exchange air n penetratio outdoor in 

# R f C C     Equation (5A-7)

where,



# C outdoor 

# = Ambient concentration at an outdoo r microenvironment or outside an indoor microenvironment (μg/m 3 

# ) 

# f

penetration 

# = Penetration factor (unitless)



# R air exchange 

# = Air exchange rate (hr -1 

# )

Since the air pressure is approximately cons tant in microenvironments that are modeled in practice, the flow of outside air into the microenvironment is e qual 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 th e 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 give n by the penetration factor 

# f penetration



# . A proximity factor ( 

# f

proximity 

# ) and a local outdoor source term are used to account for

differences in ambient concentrations between the geographic locati on 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 lo cation may differ systematically from the concentration input to the model representing the air quali ty district. For example, a playground or house might be located next to a bu sy 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 ai r at an outdoor location or directly outside an indoor microenvironment ( 

# C outdoor 

# ) is calculated as:

orSources LocalOutdo ambient proximity outdoor 

# C C f C   Equation (5A-8)

where,



# C ambient 

# = Ambient air district concentration (μg/m 3 

# )

# 5A-12 

# f

proximity 

# = Proximity factor (unitless)



# C LocalOutdoorSources 

# = the contribution to the concentration at this location from local sources not represented by the ambient air district concentration (μg/m 3 

# )

During exploratory analyses, the user ma y 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:



#  

t C R C exchange air out 

#    Equation (5A-9)

The third term ( 

# C  removal 

# ) in the mass balance calcula tion (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 O 3 

# , for example, but not for carbon monoxide. The amount lost to chemical reactions will generally be proportional to the amount present, which in the ab sence 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 remova l process is filtration, usually as part of a forced air circulation or HVAC system. Filtra tion will normally be more effective at removing particles than gases. In any case, filtrati on rates are also approximately proportional to concentration. Change in concentration due to deposition, filtration, and ch emical degradation in a microenvironment is simulated based on the first-order equation: 

#      

t C R t C R R R C

removal chemical filtration deposition removal



#        Equation (5A-10)

where,



# C 

#  removal 

# = Change in microenvironmental concentration due to removal processes (μg/m 3 

# /hr)



# R deposition 

# = Removal rate of a pollutant from a microenvironment due to deposition (hr -1 

# )

# 5A-13 

# R

filtration 

# = Removal rate of a pollutant from a microenvironment due to filtration (hr -1 

# )



# R chemical 

# = Removal rate of a pollutant from a microenvironment due to chemical degradation (hr -1 

# )



# R removal 

# = 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 sp ecifying 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 so urce strength values are used to calculate the

term 

# C 

# 

source 

# (μg/m 3 

# /hr).

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

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

emission source 

# S E 

# , it is divided by the volume of the microenvironment:



# V S C E SE source, 

#   Equation (5A-11)

where,



# C  source,SE 

# = Rate of change in 

# C(t) due to the emission source 

# S E



# (μg/m 3 

# /hr)



# S

E



# = The emission rate (μg/hr) V = The volume of the microenvironment (m 3 

# )

Concentration sources ( 

# S C 

# ) however, are expressed in units of concentration. These must

be the same units as used for the ambient concentration (e.g., μg/m 3 

# ). Concentration sources are normally used as additive terms for microenviro nments using the factor s 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 

# S C 

# 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 

# S C 

# . The mean source strength expressed in ppm/hr or μg/m 3 

# /hr is the rate of change in concentration ( 

# C  source,SC 

# ). In equilibrium,

removal exchange air SC source, S 

## R R C C    

# Equation (5A-12)

where, 

# Cs is the mean increase in concentration ove r time in the microenvironment due to the

source 

# C 

#  source,SC 

# . Thus, 

# C 

#  source,SC 

# can be expressed as

mean S SC source, 

## R C C    

# Equation (5A-13)

where 

# R mean 

# is the chemical removal rate. From Equation (5A-13), 

# R mean 

# is the sum of the air exchange rate and the removal rate ( 

# R air exchange 

# + 

# R removal 

# ) 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 

# R mean 

# = 

# R air exchange 

# + 

# R removal



# . However, the user is given the option of specifically specifying 

# R mean 

# (see discussion below). This may be used to generate a truly constant source strength 

# C  source,SC 

# by making 

# S C 

# and 

# R mean 

# both constant in time. If this is not done, then 

# R mean 

# is simply set to the sum of ( 

# R air exchange 

# +



# R removal 

# ). 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 con centration over the base conditions, then the source emission rate would have to rise if the air excha nge 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):



#  

  

#     c e 

### n

1 i i S mean n

1 i i S SC source, SE source, source 

# C R E V 1 C C C    Equation (5A-14)

where,



# S Ei 

# = Emission source strength for emission source 

# i (μg/hr, irrespective of the concentration units)



# S Ci 

# = Emission source strength for concentration source 

# i (μg/m 3 

# )



# n e 

# = Number of emission sour ces in the microenvironment



# n c 

# = 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/m 3



# , 

# 1/V is replaced by 

# f/V , where 

# f = ppm / μg/m 3 

# = 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 microenviro nmental 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:



#      

t C R

- C t C R t C R

- C dt t dC

mean combined removal exchange air combined



#  

   Equation (5A-15)

Solving this differential equation leads to: 

#     ) ( 0

t t 

## t  

##        

   mean R

-

mean combined 0

mean combined 

## e R C C R C t C   

# Equation (5A-16)

where, 

# C(t 0 

# ) = Concentration of a pollutant in a microenvironment at the beginning of a time step (μg/m 3 

# ) 

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

# t within the time step (μg/m 3 

# ).

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

are calculated:



#  

removal exchange air in source

mean combined equil 

# R R C C

R C t C C  

        Equation (5A-17)



#   

#   T mean 

### R equil 0 equil 0 

# e C t C C T C(t 



#     ) Equation (5A-18)



#   T R

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

mean R

equil 0 equil mean mean T T t

t 





# 

    

#  ) 1 0

0 

# Equation (5A-19)

# 5A-16 where,



# C equil 

# = Concentration in a microenvironment (μg/m 3 

# ) if t 

#   (equilibrium state). 

# C(t 0 

# ) = Concentration in a microenvironmen t at the beginning of the time step (μg/m 3 

# )



# C (t0+T) 

# = Concentration in a microenvironm ent at the end of the time step (μg/m 3 

# )



# C mean 

# = Mean concentration over the time step in a microenvironment (μg/m 3 

# )



# R mean 

# = 

# R air exchange 

# + 

# R removal 

# (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(t 0 

# +T) for the previous hour as 

# C(t 0 

# )

.

5A-7.2. Factors Model

The factors model is simpler than the mass ba lance model. In this method, the value of

the concentration in a microe nvironment is not dependent on the concentration during the previous time step. Rather, this model uses th e following equation to calculate the concentration

in a microenvironment from the user -provided hourly air quality data:



# 

 

#   c n

1 i Ci n penetratio proximity ambient mean 

# S f f C C Equation (5A-20)

where,



# C mean 

# = Mean concentration over the time step in a microenvironment (μg/m 3 

# )



# C

ambient 

# = The concentration in the am bient (outdoor) e nvironment (μg/m 3 

# ) 

# f

proximity 

# = Proximity factor (unitless) 

# f

penetration 

# = Penetration factor (unitless)



# S Ci 

# = Mean air concentration re sulting from source i (μg/m 3 

# )



# n c 

# = Number of concentration sources in the microenvironment

The user may specify distributions for pr oximity, penetration, and any concentration source terms. All of the parameters in Equa tion (5A-20) are evaluated for each time step,

# 5A-17 although these values might remain constant fo r several time steps or even for the entire

simulation. The ambient air quality data are supplied as time series over th e 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 ra ndom draws. Within a single day, the user selects the number

of random draws to be made and the hours to whic h 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 di fferent distributions th at apply if specific conditions are met. The air exchange rate is ty pically 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 fr om a gas stove may be given by the product of three random variables: a binary on/off variable that indicates if the stov e is used at all during that hour, a usage duration sampled from a c ontinuous 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 seque nce of microenvironments visited by the simulated individual and the activities perf ormed by each individual. The pollutant

concentration in the air in each microenvironmen t 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 ar e encountered in the microenvironments visited. Figure 5A-2 illustrate s the exposures for one simulated 12-year old child over a 2-day period. On bot h 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 br eathing 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 corr esponding normalized metabolic equivalent of work or METs (McCurdy, 2000). METs are derived by dividing the metabolic energy requirements for the specific activ ity by a person’s resting, or ba sal, 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 variab les is determined for each individual as part of their anthropometric characteristics. Because the MET ratios are sampled independently from distributions for e ach diary event, it would be possible to produce time-series of MET ratios that are physio logically unrealistic. APEX employs a MET adjustment algorithm based on a modeled oxygen de ficit to prevent such overestimation of MET and breathing rates (Isaacs et al., 2008). Th e 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 combinati on of microenvironmental concentration and breathing ventilation rates provide s 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 pe r 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 fo r a variety of exposure and dose measures. These include tables of pers on-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 modera te or greater exertion. These are results from

a simulation of O 3 

# exposures for the greater Washington, D.C. metropolitan area for the year 2002. From this graph ones sees, for example, th at APEX estimates 30 per cent 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 O 3 

# 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 ve ntilation rate (VE) equations for use in inhalation-oriented exposure models. Re port no. EPA/600/X-05/008 is Appendix A of US EPA (2009). Metabolically Deri ved 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/nc ea/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 Exposur e Assessment to Support the Review of the SO 2 

# Primary National Ambient Air Quality Standard. EPA-452/R-09-007. August 2009. Available at http://www.epa.gov/ttn/naaqs/standard s/so2/data/200908SO2REAFinalReport.pdf. Johnson, T., Capel, J., McCoy, M. (1996a). Es timation of Ozone Exposures Experienced by Urban Residents Using a Pr obabilistic 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, Re search Triangle Park, North Carolina, September. Johnson, T., Capel, J., Mozier, J., McCoy, M. (1996b). Estimation of Ozone Exposures Experienced by Outdoor Childre n in Nine Urban Areas Usin g a Probabilistic Version of NEM. Prepared for the Air Quality Mana gement Division under Contract No. 68-DO30094, April. Johnson, T., Capel, J., McCoy, M., Mozier, J. (1996c). Estimation of Ozone Exposures Experienced by Outdoor Worker s in Nine Urban Areas Usin g a Probabilistic Version of NEM. Prepared for the Air Quality Mana gement Division under Contract No. 68-DO30094, April. Johnson, T. (2002). A Guide to Selected Algorit hms, Distributions, and Databases Used in Exposure Models Developed By the Office of Air Quality Planning and Standards. Revised Draft. Prepared for U.S. Environmenta l Protection Agency under EPA Grant No. CR827033. McCurdy, T. (2000). Conceptual basis for mu lti-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. US EPA OAQPS by ICF International. Schofield, W. N. (1985). Predicting basal metabolic rate, new standards, a nd review of previous work. 

# Hum Nutr Clin Nutr . 39C(S1):5-41. US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Gu ide. Database and documentation available at: h ttp://www.epa.gov/chadnet1/. US EPA. (2012a). Total Risk Integrated Met hodology (TRIM) - Air Poll utants 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 Method ology (TRIM) - Air Pollut ants Exposure Model Documentation (TRIM.Expo / APEX, Vers ion 4.4) Volume II: Technical Support Document. Office of Air Quality Planning a nd 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 Pa ttern Methodology ..................................................... 5B-8

5B-5 PHYSIOLOGICAL AND METABOLI C 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 I ndoor 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 fo r Outdoor Microenvironments ............. 5B-21

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

5B-7 AMBIENT OZONE CONCEN TRATIONS ............................................................ 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 modele d and calculation method used. ........................... 5B-13 Table 5B-3. AERs for indoor residential mi croenvironments (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 pr evalence 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 microenvir onmental concentrations. ............................ 5B-20 Table 5B-7. VMT fractions of interstate, urba n, 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 AP EX air districts included each study area. ....................................................................................................... 5B-25 Table 5B-9. Ambient monitors used to defi ne exposure modeling domain and the population modeled in each study area. ............................................................................ 5B-26 Table 5B-10. Study area meteorological stati ons, 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, Baltimo re and Chicago study areas. .............. 5B-28 Figure 5B-2. Illustration of APEX exposure modeling domains (2000 US Census tract centroids) for Cleveland, Dallas, Denv er 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 prep aration to ensure the model outputs are appropriate as intended, reasonable, and relevant. This Appendix describes the preparation and the sources of da ta for the APEX input files.

5B-1 POPULATION DEMOGRAPHICS APEX accounts for important population char acteristics in representing study area demographics. Population counts and employment probabilities by age and gender are used to develop representative profiles of hypothetical individuals for th e simulation. For the main-body

results of the O 3 

# Health Risk and Exposure Assessment (HREA), we estimated population-based exposures using US Census tract-level popul ation 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 aske d of all people and a bout every housing unit. Three standard APEX input f iles are used for the current O 3 

# 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 de veloped 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 employ ment 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 O 3 

# 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 O 3 

## 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 simula te human behavior, APEX inco rporates workplace patterns into the assessment by use of home-to-work commuting data. By design, commuting is only

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

data were derived from the 2000 Census Tran sportation 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 cr eate 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 assu mptions 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 sepa rated flows commute dail y, 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 repres ented workers who either worked outside of the U.S. (9,631 tract pairs w ith 107,595 workers) or worked in an unknown location (120,830 tract pairs wi th 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 simulate d population to those who do not commute to destinations outside the study ar ea because we have not estimated ambient concentrations of O 3 

# in counties outside of the modeled areas.

4 

## Files downloaded from http://transtats.bts.gov/

. 5 

## Plotting log(flows) versus log(distance) indicates a nea r-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 ma jority 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-leve l 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 tim e bins to create a cumulative probability distribution of commuting times for each tract, which it then uses in conjunction with the

distribution of commuti ng distances to assign a profile-level one-way commuting time variable to each employed person in the population. This co mmuting 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 O 3 

# 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 e xposure assessment is asthmatic school-age children (ages 5-18). Modeling exposures fo r 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 Hea lth Interview Survey (NHIS). Briefly, 2000 US census tract level asthma prevalence was es timated for children (by single age years) and adults (by age groups), also st ratified by gender and family inco me/poverty ratio (i.e., whether the family income was considered below or at/ab ove the US Census estimate of poverty level for the given year). Given the signi ficant differences in asthma prevalence by age, gender, region, and poverty status, the variability in the spatial distribu tion of poverty status across census tracts (and also stratified by age), and th e spatial variability in local s cale ambient concentrations of many air pollutants, the goal was to better represen t the variability in population-based exposures when accounting for and modeling these newly refine d attributes of this study group. A detailed description of how the NHIS data were processe d for input to APEX is provided in Appendix

5C. One standard APEX input file is used for the current O 3 

# 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 e xposure media consumption rates lead to higher doses received. To accurately model individuals a nd their exposure to pollutants, it is critical to have a firm understanding of the locations wher e 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 syst em 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 ac tivities performed for each survey day. Personal attribute data for thes e surveyed individuals, such as age and gender,

are included in CHAD as well. The latest vers ion 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 appropriat e 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 loca tion and/or activity information are not used by APEX. For the la tter case, CHAD diaries were evaluated for instances where a diary may contain enough info rmation 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 inform ation 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 re sidence and persons were engage d in a sleep activity. This substitution was judged by staff as a reasonable approximation based on the limited likelihood of a person’s highest O 3 

# exposures occurring at this time of day, while still retaining the relevant activity pattern data of intere st (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 pr esent 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 occu rring indoors were set to ‘personal care’;

 ISR missing activity events o ccurring when attending sc hool were set to either ‘attend K-12’ (ages 5-18) or ‘attend day-care’ (ages <5);

 NSA (an adults study) missing activity ev ents at 8PM - 12AM occurring indoor residences were set to ‘leisure, general’;

 Locations missing for a number of staff judged outdoor activities 6 

# were set to ‘outdoor, general’;

 Locations missing for a number of sta ff judged indoor residential activities 7 

# were set to “indoor, residence”; and

 Locations missing for a number of st aff judged general indoor activities 8 

# were set to “indoor, other”. Three standard APEX input file s are used for the current O 3 

# assessment:

 CHADQuest_013013B.txt : personal (e.g., age, gender, employment status, county of residence, etc.) and day (e.g., dail y 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 stud ies 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 O 3 

# 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 stud ies. The data from these eight additional studies incorporated in CHAD a nd available for use by APEX have more than doubled the total activity pattern data used for O 3 

# exposure modeling in 2007 and ha s increased the number of children diaries by a factor of fi ve. The studies from which these new data were derived are briefly described below.

 DEA. The diaries are from 2 seasons of th e 6-season sampling period (2004-2007) used by EPA in the Detroit Exposure and Aeroso l 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 partic ipant located in 6 areas in Wayne County, Michigan (in and around Detroit). A 15-minute block diary appro ach was used to collect activity data. Participants were all adu lts 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 longitudina l 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 da ughter 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 subj ects 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), pha se II (2002-03), and pha se III (2007-08) of the University of Michigan's Panel Study of Income Dynamics (PSID), respectively (University of Michigan, 2012). Nationally re presentative activity pattern data from nearly 11,000 children ages 0-13 (phase I), ag es 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 th e 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 fr om about 1,200 adults aged 35-92 in seven metropolitan areas (Atlanta, St. Louis, Sacr amento, 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 O 3 

# pollution were studie d, and of whom, about

# 5B-7 half of were asthmatics. Activity data we re collected on 6 nonconsecutive days from each subject, with some subjects providi ng fewer days, providing nearly 2,200 persons days of data to APEX.

 SEA. The diaries are from a particulate matte r (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 adu lt 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 consecuti ve 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 Universi ty 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 Ca lifornia 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. Tw o days of activity data were obtained via the internet for each particip ant—a weekday and a weekend day. Approximately 2,500 diary-days from SUPERB met appr opriate criteria for use in APEX.

 RTP . The diaries were collected in a pane l study of PM exposure in the Research Triangle Park (RTP), NC area (Williams et al ., 2003a, b). Two olde r adult cohorts (ages 55-85) were studied: a cohort ha ving 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 c onsecutive calendar seasons in 2000-2001. Approximately 900 diary-days we re 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 O 3 

# -season or year-long activity sequence for each simulated i ndividual based on a larg ely cross-sectional activity database of 24-hour records. The typical subject in the time loca tion activity studies in CHAD provided about two days of diary data. For this reason, the construction of a season-long activity sequence for each i ndividual 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 repetiti on of activities common to individuals (though recognizing even these diary sequ ences are not entirely correlated) while maintaining realistic variability among individuals comprising each study group. The method currently used in APEX for creating longitudi nal diaries was designed to capture the tendency of individuals to repeat ac tivities, based on reproducing realistic variation in a key diary variable, which is a user selected function of di ary variables. For this O 3 

# analysis, the key variable selected is th e amount of time an individual spends outdoors each day, one of the most important determinants of exposure to high levels of O 3 

# . 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 detail s regarding the longitudinal methodology can be found in US EPA (2013a, b).

Desired D and A values for the key variable are sele cted by the user and set in the APEX parameters file, and the method algorithm construc ts longitudinal diaries that preserve these parameters. Longitudinal diary data from a limite d 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 ot hers outside the study age range, and since APEX appears to underestim ate repeated activities, values of 0.5 for D and

0.2 for A are used for all ages.5B-9Table 5B-1. Consolidated Human Activity Database (CHAD) study information and diary-days used by APEX.Study Name (CHADAbbreviation)GeographicCoverageStudy DatesStudySubjectAgesAPEXDiary-days(ages 4-94)APEXDiary-days(ages 4-18)Diary Type,Time Format,Survey DesignStudy ReferenceBaltimore RetirementHome Study (BAL)One buildingin Baltimore,MD1/1997 to 2/1997;7/1998 to 8/199872 - 93 304 0 Diary, 15 MinuteBlock, PanelWilliams et al. (2000)California YouthActivity Patterns Study(CAY)California 10/1987 to 9/1988 12 - 17 182 182 Recall, Event,RandomRobinson et al. (1989),Wiley et al. (1991a)California AdultsActivity Patterns Study(CAA)California 10/1987 to 9/1988 18 - 94 1,555 36 Recall, Event,RandomRobinson et al. (1989),Wiley et al. (1991a)California ChildrenActivity Patterns Study(CAC)California 4/1989 to 2/1990 <1 - 11 1,195 771 Recall, Event,RandomWiley et al. (1991b)Cincinnati ActivityPatterns Study (CIN)Cincinnati, OHmetro. area3/1985 to 4/1985;8/1985<1 - 86 2,449 727 Diary, Event,RandomJohnson (1989)Detroit Exposure andAerosol ResearchStudy (DEA)Detroit, MImetro. area7/2005 to 8/2005;7/2006 to 8/200618 - 74 331 5 Recall, 15 MinuteBlock, PanelWilliams et al. (2008)Denver CO PersonalExposure Study (DEN)Denver, COmetro. area11/1982 to2/198318 - 70 714 7 Diary, Event,RandomJohnson (1984), Aklandet al. (1985)EPA LongitudinalStudies (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,PanelIsaacs et al. (2012)Los Angeles OzoneExposure Study:Elementary School(LAE)Los Angeles,CA10/1989 10 - 12 50 50 Diary, Event,PanelSpier et al. (1992)Los Angeles OzoneExposure Study: HighSchool (LAH)Los Angeles,CA9/1990 to 10/1990 13 - 17 42 42 Diary, Event,PanelSpier et al. (1992)5B-10Study Name (CHADAbbreviation)GeographicCoverageStudy DatesStudySubjectAgesAPEXDiary-days(ages 4-94)APEXDiary-days(ages 4-18)Diary Type,Time Format,Survey DesignStudy ReferenceNational HumanActivity Pattern Study:Air (NHA)National 9/1992 to 10/1994 <1 - 93 4,329 693 Recall, Event,RandomKlepeis et al. (1996),Tsang and Klepeis(1996)National HumanActivity Pattern Study:Water (NHW)National 9/1992 to 10/1994 <1 - 93 4,329 745 Recall, Event,RandomKlepeis et al. (1996),Tsang and Klepeis(1996)National-Scale ActivitySurvey (NSA)7 US metro.areas6/2009 to 9/2009 35 - 92 6,825 0 Recall, 15 MinuteBlock, RandomKnowledge Networks(2009)Population Study ofIncome DynamicsPSID I (ISR)National 2/1997 to 12/1997 <1 - 13 4,978 3,507 Recall, 15 MinuteBlock,Random/PanelUniversity of Michigan(2012)Population Study ofIncome DynamicsPSID II (ISR)National 1/2002 to 12/2003 5 - 19 4,800 4,793 Recall, 15 MinuteBlock,Random/PanelUniversity of Michigan(2012)Population Study ofIncome DynamicsPSID III (ISR)National 10/2007 to 4/2008 10 - 19 2,650 2,614 Recall, 15 MinuteBlock,Random/PanelUniversity of Michigan(2012)RTI Ozone AvertingBehavior (OAB)35 US metro.areas7/2002 to 8/2003 2 - 12 2,872 2,187 Recall, 15 MinuteBlock, RandomMansfield et al. (2006,2009)RTP Panel (RTP) RTP, NC 6/2000 to 5/2001 55 - 85 871 0 Diary, 15 MinuteBlock, PanelWilliams et al. (2003a,b)Seattle (SEA) Seattle, WA 10/1999 to 3/2002 6 - 91 1,624 317 Diary, 15 MinuteBlock, PanelLiu et al. (2003)Study of Use ofProducts andExposure RelatedBehavior (SUP)BroaderSacramento &SanFrancisco, CACounties7/2006 to 3/2010 1 - 88 3,456 994 Recall, 15 MinuteBlock, PanelBennett et al. (2012a),Hertz-Picciotto et al.(2010)Washington, D.C.(WAS)Wash. DCmetro. area11/1982 to 2/1983 18 - 71 686 10 Diary, Event,RandomHartwell et al. (1984),Akland et al. (1985)Totals 1982 - 2010 <1 - 94 41,474 17,680

# 5B-11 5B-5 PHYSIOLOGICAL AND METABO LIC EQUIVALENTS DATA APEX requires several physiological pa rameters to accurately model processes that affect pollutant intake rate for individuals . This is because diffe rences in physiology may cause people with the same expos ure and activity scenarios to ha ve 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, NVO 2 

# max), 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 O 3 

# exposure and dose). APEX also uses an input file containing th e metabolic equivalents for work (METS) to estimate the specific energy expended for each activ ity listed in the diary file. These METS values are commonly in the form of distributions and were origin ally derived as relative to an

individual’s RMR. Some activit ies are specified as a single po int value (for instance, sleep), while others, such as athletic endeavors or ma nual 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 physiolo gical variables are ultimately used to estimate ventilation rate (VE) and decrements in forced expiratory volume, in one second (dFEV 1 

# ). Three standard APEX input file s are used for the current O 3 

# assessment:

 Physiology010213_threshold.txt : NVO2max, BM, RMR, BSA, MOXD, RECTIME, height, ECF, and dFEV 1 

# distributions and equa tion coefficients, by sex and age groups

 MET_Distributions_030612.txt : statistical form and parameters for METS distributions associated with each ac tivity 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 individua ls occurs in microenvironments. For exposures to be accurately estimated, it is importa nt maintain the spatial and temporal sequence of microenvironments persons inhabit and ap propriately represent the time series of

concentrations that occur within them. As discussed in Appendi x 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 microenvironmen tal 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 microenvir onmental 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 bedroo m). Therefore these more specific locations are aggregated by ma pping these 115 location codes to the 28 modeled microenvironments. Further, all microenvironmen tal concentrations in this exposure assessment are estimated using an ambient concentration (section 5B-7), though thes e concentrations not only vary temporally but spatia lly, depending on the particular microenvironment. The mapping

of locations to the 28 microenvironments also in cludes 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 concentr ation for each individual based on either their home (H), work (W), near work (NW), near hom e (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 O 3 

# 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 us ed to estimate all microenvironmental concentrations

 MicroEnv_Mapping_CHAD_to_APEX_28MEs_022613.txt : maps 115 CHAD locations to 28 APEX microenvironmen ts 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 Variables 1



## 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 Variables 1



## 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 origin ally described in detail in US EPA (2007) Appendix A, though recently upda ted 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 temp eratures, 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 Yor k, NY considering the same temperature bin. For each combination of A/C type, city, a nd temperature with a minimum of 11 AER values, exponential, lognormal, normal, and Weibu ll distributions were fit to the AER values and compared. Generally, the lognormal distribution wa s the best-fitting of the four distributions, and so, for consistency, the fitted lognormal distri butions are used for all the cases. Los Angeles had an adequate number of samples and iden tifiers to distinguis h 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 gene rating 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 th e 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; othe rwise 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 rang es 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 mi croenvironments (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, San Bernardino Counties >25 {0.830, 2.353, 0.1, 10}

# 5B-17 Table 5B-4. AERs for indoor residential mi croenvironments (ME-1) without A/C by study area and temperature.

5B-6.2 Air Conditioning Prevalence for Ind oor 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 represen ted by the AER distri bution and dependent on temperature. The A/C prevalence data were assi gned 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, San Bernardino Counties 10 - 20 {0.665, 2.174, 0.1, 10} 20 - 25 {1.054, 1.711, 0.1, 10}

> 25 {0.827, 2.265, 0.1, 10}5B-18Table 5B-5. American Housing Survey A/Cprevalence from Current Housing Reports (Table 1-4) for selected urban areas.MetropolitanAreaArea1StudyYearsTotal OccupiedHousing Units(x1000)Number of Occupied Housing Units (x1000) with:% of Occupied Housing Units with:2CentralA/C>1 CentralA/C1 RoomUnit2 RoomUnits3+ RoomUnitsCentralA/CWindowUnitsCentral &Window A/CAtlanta MA 2004 1595.8 1473.8245.9 39.8 32.5 18.3 92 698Baltimore MSA 2007 1012.3 785.4 44.4 55.7 72.4 65.4 78 1997Boston CMSA 2007 1057.1 291.5 20.3 259.4 198.7 156.0 28 5886Chicago PMSA 2009 3010.7 2050.6 116.2 412.0 265.1 124.4 68 2795Cleveland PMSA 2004 769.3 416.4 14.1 132.9 47.0 17.6 54 2680Dallas PMSA 2002 1235.3 1146.3 171.6 25.5 29.8 27.8 92.8 6.799.5Denver MA 2004 855.7 425.1 16.8 123.8 20.3 3.9 50 1767Detroit PMSA 2009 1672.5 1194.3 46.5 192.3 82.8 29.2 71 1890Houston PMSA 2007 1872.0 1682.5 153.7 46.0 59.9 60.6 90 999Los Angeles3PMSA 2003 5152.4 2448.4 161.6 702.1 118.6 46.947.516.8 64.4New York4PMSA 2009 4493.3 872.4 38.2 1036.9 1184.1 812.6 19 6887Philadelphia PMSA 2009 1916.2 1095.9 52.3 197.9 260.8 265.8 57 3895Sacramento PMSA 2004 669.4 549.5 30.7 57.1 12.3 2.4 82 1193St. Louis MA 2004 1139.6 974.4 53.7 65.8 43.5 16.6 86 1197Washington, DC MA 2007 1949.11729.6 145.7 69.2 64.8 61.2 89 10991MA – metropolitan area; CMSA – consolidated metropolitan statistical area; PMSA – primary metropolitan statistical area.2Shaded areas indicate final values used inAPEX functions files to select AER distributions used for indoor residential microenvironments (ME-1).3Los Angeles includes Los Angeles-Long Beach, Riverside-San Bernardino-Ontario, and Anaheim-Santa Ana MSA’s.4 New York is represented bythe NY-Nassau-Suffolk-Orange MSA.

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

To estimate AER distributions for non-reside ntial, 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; Pe rsily 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 SF 6 

# 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 m easurements in 96 large, mechanically ventilated

offices. AERs were measured bot h by a volumetric method and by a CO 2 

# ratio method, and included their uncertainty estimates. For thes e analyses, we used the recommended “Best Estimates” defined by the values with the lower es timated 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 indivi dual measurements, to account for the strong dependence of the AER measurements for the sa me office space over a relatively short period. The mean values are similar for the two studies, but the standard deviati ons are about twice as high for the Persily data. We f itted 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 restau rants, 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 fr om Turk et al. (1989) and Shendell et al. (2004).

# 5B-20 The AER distribution used for indoor restau rants, bars, night clubs, and cafés (ME-3, ME-6) is a fitted lognormal di stribution, having a geometric m ean = 3.712, geometric standard deviation = 1.855 and bounded by the lower and uppe r values of the sample data set {1.46, 9.07}. This distribution was develo ped 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 Factor s for In-vehicle and Near-Road Microenvironments For the in-vehicle proximity and penetra tion factors (ME-22, ME-23, ME-24, ME-28), we use distributions developed from the Cincinna ti Ozone Study (America n Petroleum Institute, 1997, Appendix B; Johnson et al., 1995). This field study was conducted in the greater Cincinnati metropolitan area in August and Sept ember, 1994. Vehicle tests were conducted according to an experimental design specifying th e 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), pr incipal urban arterial roads (urban), and local roads (local). Nomi nal vehicle speeds (typical ly met over one minute intervals within 5 mph) were at 35 mph, 45 mp h, 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 para meters of the normal distributions developed for penetration and proximity factors for in-vehicle microenvironm ents 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 Bound 1 

## 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 5 th 

## percentile value estimated using a normal appro ximation as Mean – 1.64 × standard deviation.

# 5B-21 The Vehicle Miles of Travel (VMT) fractions 10 

# provided by the U.S. Department of Transportation (DOT) are used to generate da ily conditional variables that determine the selection of which proximity factor distri butions 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 urba n 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 we re 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 microenvironmen ts (ME-17, ME-18, ME-19, ME-20, ME-21) we employed the distribution for local road s (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 O 3 

# concentrations. Therefore, both the penetration factor and proximity factor for th is microenvironment were set to equal 1.

5B-6.6 Ozone Decay and Deposition Rates

A distribution for combined O 3 

# 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 Ca lifornia. Measurements of decay rates in a

second room were made in 24 of these residenc es. 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 stan dard 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.xls5B-22Table 5B-7. VMT fractions of interstate, urban, and local roads in the study areas used to select in-vehicle proximity factordistributions.Study Area20062007200820092010InterstateUrbanLocalInter-stateUrbanLocalInter-stateUrbanLocalInter-stateUrbanLocalInter-stateUrbanLocalAtlanta 0.34 0.46 0.20 0.34 0.47 0.19 0.32 0.45 0.23 0.31 0.44 0.25 0.30 0.43 0.27Baltimore 0.34 0.59 0.07 0.34 0.59 0.070.34 0.59 0.07 0.34 0.59 0.07 0.34 0.59 0.07Boston 0.32 0.55 0.13 0.32 0.55 0.13 0.32 0.54 0.14 0.32 0.54 0.14 0.32 0.54 0.14Chicago 0.30 0.58 0.12 0.30 0.58 0.12 0.31 0.57 0.12 0.30 0.57 0.13 0.31 0.56 0.13Cleveland 0.40 0.44 0.16 0.40 0.44 0.160.39 0.45 0.16 0.39 0.45 0.16 0.38 0.46 0.16Dallas 0.30 0.66 0.04 0.30 0.66 0.04 0.30 0.65 0.05 0.30 0.66 0.04 0.29 0.67 0.04Denver 0.23 0.67 0.10 0.24 0.66 0.10 0.25 0.65 0.10 0.25 0.65 0.10 0.25 0.65 0.10Detroit 0.26 0.64 0.10 0.25 0.65 0.10 0.24 0.66 0.10 0.25 0.65 0.10 0.26 0.63 0.11Houston 0.24 0.72 0.04 0.24 0.72 0.04 0.24 0.72 0.04 0.24 0.72 0.04 0.24 0.72 0.04Los Angeles 0.29 0.66 0.05 0.29 0.67 0.040.28 0.67 0.05 0.29 0.66 0.05 0.29 0.66 0.05New York 0.19 0.66 0.15 0.19 0.65 0.160.19 0.66 0.15 0.19 0.66 0.15 0.19 0.65 0.16Philadelphia 0.23 0.65 0.12 0.24 0.65 0.110.24 0.65 0.11 0.20 0.59 0.21 0.18 0.52 0.30Sacramento 0.25 0.72 0.03 0.25 0.70 0.050.23 0.69 0.08 0.23 0.69 0.08 0.23 0.69 0.08St. Louis 0.36 0.45 0.19 0.37 0.45 0.18 0.38 0.45 0.17 0.37 0.45 0.18 0.36 0.44 0.20Wash., DC 0.31 0.61 0.08 0.31 0.61 0.080.30 0.61 0.09 0.30 0.61 0.09 0.30 0.61 0.09A 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 assessm ent, APEX requires hourly ambient O 3 

# concentrations at a set of locati ons (or air districts) within stud y area. We used hourly ambient monitoring data along with a statistical appr oach (VNA) to better approximate spatial heterogeneity (where such heterogeneity might be present) across each study area (O 3 

# HREA, Chapter 4). General processing steps perfor med to generate the final APEX ambient concentration input files that were used were as follows. After identifying the 15 study areas to be m odeled in this assessment, staff defined a broad air quality modeling domain for each st udy area, specifically bounding where exposures were to be estimated. We evaluated 1) counties modeled in the previous 2007 O 3 

# NAAQS review common to current study areas, 2) polit ical/statistical count y aggregations (MSA, PMSA), and 3) if the study area was designate d 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 O 3 

# concentrations were estimated at every census tract within the counties that comprised each study area (O 3 

# 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 fi elds were selected, then any tracts/monitor radii that were largely outside of the urban core were removed, t hus 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 distri ct locations differed sli ghtly from that of the exposure tracts, resulting in differe nt numbers of air dist ricts 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 al ways uses the air distri ct 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 computationa l 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 sta ndard PC capabilities); based on simulations that ran to completion, the maximu m number of air districts possi ble using a standard 32-bit PC was estimated as 1,900 – 2,000. Thus, to make the an alysis 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 tr acts within 30 km of ambient monitors in the broad county domain). Using this approach, the number of ai r 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 SURVEYSE LECT procedure; the number of air districts for Chicago remained at 1,882. While we estimated this num ber of districts would run on a standard PC, these three study areas woul d 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 censu s tract IDs where ambient concentrations were

estimated is within the APEX air di stricts files. Table 5B-9 contains the final list of counties, the number of US census tracts where exposures we re 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 wh ere 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 th rough Figure 5B-4 illustrate the general exposure modeling domains (i.e., the select ed 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 O 3 

# 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 a nd longitudes, start and stop dates of concentrations5B-25Table 5B-8. Identification of U.S. counties and thenumber of APEX air districts included each study area.Study Area (State Abbreviation: List of Counties1)APEX Air Districts(VNA Total)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)664 (1,019)Baltimore(MD: Anne Arundel, Baltimore, Carroll, Harford, Howard, Queen Anne's, Baltimore (City))603 (679)Boston(MA:Barnstable, Bristol,Dukes, Essex, Middlesex,Nantucket, Norfolk, Plymouth, Suffolk, Worcester)1,005 (1,276)Chicago(IL: Cook, DeKalb, DuPage, Grundy, Kane, Kankakee, Kendall, Lake, McHenry, Will; IN: Jasper, Lake, LaPorte,Newton, Porter, Kenosha)1,882 (2,267)Cleveland(OH: Ashtabula, Cuyahoga, Geauga, Lake, Lorain, Medina, Portage, Summit) 802 (830)Dallas(TX: Collin, Dallas, Denton, Ellis, Hunt, Johnson, Kaufman, Parker, Rockwall, Tarrant, Wise) 1012 (1,312)Denver(CO: Adams, Arapahoe, Boulder,Broomfield2, Clear Creek, Denver, Douglas, Elbert, Gilpin, Jefferson, Larimer, Park,Weld)655 (839)Detroit(MI:Genesee, Lapeer, Livingston, Macomb, Monroe,Oakland, St. Clair, Washtenaw, Wayne)1,419 (1,568)Houston(TX:Austin, Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, San Jacinto, Waller)779 (1,074)Los Angeles,(CA: Los Angeles, Orange, Riverside, San Bernardino, Ventura) 2,000 (3,920)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)1,900 (5,003)Philadelphia(DE: New Castle, MD: Cecil; NJ: Atlantic, Burlington,Camden, Cape May, Cumberland, Gloucester, Ocean,Salem; PA: Bucks, Chester, Delaware, Montgomery, Philadelphia)1,452 (1,735)Sacramento(CA: El Dorado, Nevada, Placer, Sacramento, Solano, Sutter, Yolo) 447 (623)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)494 (626)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, AlexandriaCity, Fairfax City, Falls Church City, Fredericksburg City, Manassas City, Manassas Park City, Winchester City, WV: Jefferson)1,013 (1,391)All AREAS (Counties: 207 Exposure of 215 Air Quality)16,127 (24,162)1italicized: in air quality domain but not in exposure modeling domain; considered outside of urban core or no monitors.2this county is newly defined in the 2010 census.5B-26Table 5B-9. Ambient monitors used todefine exposure modeling domain and the population modeled in each study area.Study Area (State Abbreviation: List of Monitors1)CensusTracts forExposurePopulationRepresentedAtlanta(GA: 130590002, 130670003, 130770002, 130850001,130890002, 130893001, 130970004, 131130001,131210055, 131350002, 131510002, 132230003, 132319991, 132470001)678 3,850,951Baltimore(MD: 240030014, 240051007, 240053001, 240130001, 240251001, 240259001, 240290002, 240313001,240330030, 240338003, 240339991, 245100054)6182,209,226Boston(MA: 250092006, 250094004, 250094005, 250095005,250170009, 250171102, 250213003, 250250041,250250042, 250270015, 250270024; NH: 330111011; RI: 440071010)1,028 4,449,291Chicago(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)2,0558,345,373Cleveland(OH: 390071001, 390350034, 390350060, 390350064, 390355002, 390550004, 390850003, 390850007,390853002, 390930018, 391030003, 391030004, 391331001, 391510016, 391514005, 391530020)8792,692,846Dallas(TX: 480850005, 481130069, 481130075, 481130087, 481133003, 481210034, 481211032, 481390015,481390016, 481391044, 482210001, 482311006, 482510003, 482570005, 483670081, 483970001, 484390075,484391002, 484392003, 484393009, 484393011)1,0364,698,392Denver(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)6752,626,239Detroit(MI: 260490021, 260492001, 260910007, 260990009, 260991003, 261250001, 261470005, 261610008,261619991, 261630001, 261630015, 261630016, 261630019)1,4544,572,479Houston(TX: 480391004, 482010024, 482010026, 482010029,482010046, 482010047, 482010051, 482010055,482010062, 482010066, 482010070, 482010075, 482010416, 482011015, 482011034, 482011035, 482011039,482011050, 483390078)8023,925,054Los 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)3,35214,950,3405B-27Study Area (State Abbreviation: List of Monitors1)CensusTracts forExposurePopulationRepresentedNew 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)4,88918,520,868Philadelphia(DE: 100031007, 100031010, 100031013, MD: 240150003; NJ: 340070003, 340071001, 340110007,340150002, 340210005, 340219991, 340290006; PA: 420170012, 420290100, 420450002, 420910013, 421010004,421010014, 421010024, 421010136)1,5555,506,954Sacramento(CA: 060170010, 060170020, 060570005, 060610002, 060610004, 060610006, 060670002, 060670006,060670010, 060670011, 060670012, 060670013, 060670014, 060675003, 060953003, 061010003, 061010004,061131003)4611,926,598St. Louis(IL: 170831001, 171190008, 171191009, 171193007,171199991, 171630010; MO: 290990012, 290990019,291130003, 291831002, 291831004, 291890004, 291890005, 291890014, 295100085, 295100086)5182,340,325Washington, 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)1,0374,498,374All AREAS (324 ambient monitors)21,03785,113,3101A 30 km radius for monitors operating anytime during 2006-2010 was used to select census tracts in defining the exposure modeling domain.

# 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, Ne w 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 sel ecting human activity da ta 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 devel oping profiles and selecting for am AER, APEX uses temperature data from the closest w eather station to each Census tract. Missing temperature data were estimated by th e following procedure. Where there were consecutive strings of missing va lues (data gaps) of 4 or fe wer 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 R 2 

# , for each hour of the day, subject to the constraints that R 2 

# be greater than 0.50 and the number of regression data values (days) is at least 60. If there were any remaining mi ssing values at this point, for gaps of 6 or fewer hours, missing values were es timated by linear interpol ation between the valid values at the ends of the gap. Any remaining mi ssing 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 averag e, daily mean temperatures were approximately 0.02 °C greater in the final data set used by AP EX, 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 gr eater than that of th e data set with missing values. The greatest negative difference was associated with stat ion ‘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 va lues 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 O 3 

# assessment, varying by the year and study area, tho ugh 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, lati tudes 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 statio ns, locations, and hours of missing data.

Study Area ISH ID 1 

## 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 ID 1 

## 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 ID 1 

## 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 Integrat ed Surface Hourly (ISH) global database.

5B-9 CONDITIONAL VARIABLES APEX has added flexibility in using conditional variables in associati on with selection of the distributions used to represent input vari ables, across several modules (i.e., CHAD diary selection, microenvironmental concentr ation calculations). In this O 3 

# assessment, a number of temperature ranges are used in selecting the particular AER distribut ion (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 (sect ion 5B-6.2), and designation of roadway type travelled based on VMT miles (section 5B-6.4). A single unique APEX input file s is used for the current O 3 

# assessment, varying by the year and study area:

 Functions_O3_CSA[studyar ea]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/O 3 

# 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 determ ine 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 Characteris tics 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: as sessing ozone exposure of gr ade-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 Car bon Monoxide Exposure of Residents of Washington, DC and Denver, Colorado. Resear ch Triangle Park, NC: U.S. Environmental Protection Agency, Office of Research a nd Development, Environmental Monitoring Systems Laboratory. EPA-600/4-84-031. Hertz-Picciotto, I., Cassady, D., L ee, K., Bennett, D. H., Ritz, B., Vogt, R. (2010). Study of Use of Products and Exposure-Related Behavi ors (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 Valle ro, D. (2012). Statis tical 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. Envir onmental Protection Agency, Environmental Monitoring Systems Laboratory. EPA-600/4-84-014. Johnson, T. (1989). Human Activity Patterns in Cincinnati, Ohio. Pa lo 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 St udy in Cincinnati, Ohio. Proceedings 88 th 

# 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 Prot ection 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., Ko enig 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 outdoor s 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 ac tivity data in exposure models: analysis of discriminating factors. J Expos Anal Environ Epidemiol . 13(4): 294-317. Persily, A., Gorfain, J. (2004). Analysis of Ve ntilation 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). Ven tilation 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 Implica tions 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 O 3 

# 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., J ohnson, 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., Geis ling-Sobotka, K. L., Harrison, J., Prill, R. J. (1989). Commercial Building Ventilation Rate s and Particle Concentrations. ASHRAE No. 3248.

# 5B-38 US EPA. (2002). Consolidated Human Activities Database (CHAD) Users Gu ide. Database and documentation available at: http://www.epa.gov/chadnet1/ US EPA. (2007). Ozone Population Exposure Analys is 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/st andards/ozone/s_o3_cr_td.html US. EPA. (2012a). Total Risk Integrated Me thodology (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 Met hodology (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 St udy 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 Reside nts: Final Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. Ava ilable from: NTIS, Springfield, VA., PB94- 108719. Wiley, J. A., Robinson, J. P., Cheng, Y.-T., Piazz a, T., Stork, L., Pladse n, 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 ma tter epidemiology-exposure study: Part 2. Personal exposure associated wi th 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., R odes, C., Thornburg, J. (2003b). The Research Triangle particulate panel study: modeling am bient source contributi ons to personal and residential PM mass concentrations. Atmos Environ . 37:5365-5378. Williams, R., Rea, A., Vette, A., Croghan, C., Wh itaker, D., Stevens, C., McDow, S., Fortmann, R., Sheldon, L., Wilson, H., Thornburg, J., Philli ps, M., Lawless, P., Rodes, C., Daughtrey, H. (2008). The design and field implementa tion 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 .............................................................................................................. 5 C-1

5C-2. RAW ASTHMA PREVALENCE DATA SET DESCRIPTION .............................. 5C-2

5C-3. LOGISTIC MODELING APPR OACH USED TO ESTIMATE ASTHMA

PREVALENCE.......................................................................................................... 5C-4

5C-4. APPLICATION OF LOESS SM OOTHER TO ASTHMA PREVALENCE

ESTIMATION ........................................................................................................... 5C-8

5C-5. CENSUS TRACT LEVEL POVERTY RATIO DATA SET DE SCRIPTION 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 fro m NHIS (2006-2010) sample adult and child files and the number of those respondi ng to asthma survey questions. ............ 5C-4 Table 5C-2. Example of alternat ive logistic models evaluated to estimate child asthma prevalence using the “EVER” asthma re sponse 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 re siduals generated usi ng logistic model, smoothing set to 0.7, and the children ‘E VER’ asthmatic data set. ................ 5C-11 Figure 5C-2. Studentized residuals versus model pred icted betas generated using a logistic model and using the children ‘EVER’ asth matic 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 Nati onal 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 wo rk 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 ag ed 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 ca lculated using a logistic model (PROC SURVEY LOGISTIC; SAS, 2012). A scatter-plot t echnique (LOESS SMOOTHER; SAS, 2012) was applied to smooth the prevalence curves and compute the standard errors and confidence intervals for the smoothed preval ence estimates. Logistic analysis of the raw and smoothed prevalence curves showed statistically signifi cant differences in pr evalence by gender and region, supporting their use as stra tification variables in the fina l 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 asthma tic 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 childre n (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 preval ence for each adults and children were estimated here. The first data set, as was done previous ly, 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/ques t_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, specificall y, 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 fu rther 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 stra tified by age and age groups. Given 1) the significant differences in asthma prevalence by ag e, gender, region, and po verty status, 2) the variability in the spatia l distribution of poverty st atus across census tracts, stratified by age, and 3) the spatial variability in loca l 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 attribut es of this susceptible population.

5C-2 RAW ASTHMA PREVALENCE DATA SET DESCRIPTION In this section we describe the asthma preval ence data sets used and identify the variables retained for our final data set. First, raw da ta and associated documentation were downloaded

from the Center for Disease C ontrol (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-leve l attributes of interest within thes e files, i.e., age in years (‘age_p’), gender (‘sex’), US geographic region (‘region’), coupled with the respon se 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, compri sing over 50,000 children and 120,000 children for years 2006-2010 (Table 5C-1). Information regarding personal and family in come and poverty ranking are also provided by the NHIS in separate files. Five files (‘IN CIMPx.dat’) are available for each survey year, each containing either the actual re sponses (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 compani on 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 us ing 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 co llected over the five y ear 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 nomina l 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 (f or 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 st ratify 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 ra tio threshold was selected as <1.5, therefore including persons that were within 50% above th e 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 cat egorized ‘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 pers on-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 individu al identifier) variables. Note, all persons within the sample

adult and child files had corresponding financial survey data. Two asthma survey response variables were of in terest in this analysis and were used to develop the two separate prevalen ce data sets for each children and adults. The response to the first question “Have you EVER been told by a doctor or other h ealth professional that you [or your child] had asthma?” was recorded as variable name ‘CASHMEV’ for children and ‘AASMEV’ for adults. Only persons having respons es of either ‘Yes’ or ‘No’ to this question were retained to estimate the asthma prevalen ce. 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/pov erty/data/threshld/thresh08.html ) for year 2008 by family size (note, income is the annual salary from the prior year) and calculat ed an appropriate poverty ra tio for each family member.

8 

## These same age groupings were used to create the compan ion file containing the census tract level poverty ratio

probabilities (section 5C-5).

# 5C-4 responding otherwise, i.e., those that ‘refuse d’ to answer, instances where it was “not ascertained’, or the person ‘does not know’, does not affect the esti mated prevalence rate if either

‘Yes’ or ‘No’ answers could actua lly be given by these persons. There were very few persons (<0.3%) that did provide an unusable response (T able 5C-1), thus the above assumption is reasonable. A second question wa s 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, respectivel y. Again, while only persons responding ‘Yes’

and ‘No’ were retained for furt her analysis, the representativene ss 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 pers ons 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 se ction, four person-level analyt ical data sets were created from the raw NHIS data files, generally containi ng 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 (‘Mi dwest’, ‘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. Th is 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 si mplified approach are that the distributions of

# 5C-5 the estimated prevalence rates would not be we ll 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 su rvey 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]. A nother advantage of the logistic modeling is that it can be used to compare alternative statistica l models, such as models where the prevalence probability depends upon age, region, poverty st atus, 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 variab le beta is a given function of age, gender, poverty status, and region. I used the SAS pr ocedure SURVEYLOGISTIC to fit the various logistic models, taking into account the NHIS su rvey weights and survey design (using both stratification and clustering options), as well as co nsidering various combina tions of the selected explanatory variables. As an example, Table 5C-2 lists the mode ls fit and their log- likelihood goodness-of-fit measures using the sample child data and for th e “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’ co lumn lists the eight possible stratifications: no stratification, stratified by ge nder, 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 anothe r 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 +  × age 2 

# , where   and  vary with strata. Cubic in age Beta =  +  × age +  × age 2 

# +  × age 3 

# , where  , 

,  , and  vary with the strata. f(age) Beta = arbitrary function of ag e, 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 indepe ndent 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 L og 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 esti mated by comparing the difference in the -2 Log Likelihood values with a chi-sq uared 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 statis tic are at least 600,000 and thus significant at pvalues well below 1 percent. Based on its havi ng 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 es timating prevalence using the ‘STILL’ ha ve 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 respons e variable and goodness of fit test results.

Model No. Description Strata - 2 Log Likelihood DF 1



## 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. r egion, gender, poverty 278655423.1 288

1 

## model degrees of freedom.

# 5C-8 The SURVEYLOGISTIC procedur e produces estimates of the beta values and their 95% confidence intervals for each combination of ag e, region, poverty status, and gender. By applying the inverse l ogit transformation,

Prob(asthma) = exp( beta) / (1 + exp(beta) ),

one can convert the beta values and associat ed 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 (b eta) × 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 childre n is provided in Attach ment 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 (‘E VER’ had Asthma) and 5CA-4 (‘STILL’ have asthma). Graphical representation of each study group is also provided in a se ries of plots within Attachment A, Figures 5CA-1 to 5CA-4. The vari ables provided in the ta bular 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 identifie d as a potential method for smoothing the curves, but they did not provide the best fit to the data. One reas on 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 lo cal regression approach th at separately fits a

# 5C-9 regression curve to each age A and its neighboring ages was use d, giving a regres sion 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 func tion 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 l ogit 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, we ights 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’ an swers to the asthma question, leading to an estimated value of -14.203 fo r 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 th e 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 thre e 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 l ogit of the prevalence, as a function of age, separately for each re gion, 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 num ber 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 tw o outside ages have assigned weight zero, making the local quadratic model fit exactly at every point ex cept 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 pa rameter 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 lowe r bound could effectively be extended only to 0.9 given the number of age groups. This limits the selection of smoothing pa rameter applied to the two adult data sets to a value of 0.9, though valu es of 0.8 to 1.0 were nevertheless compared for good measure. The first regression diagnostic used was the re sidual 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 th e LOESS procedure, considering region, gender, poverty status and each data set exam ined. For children ‘EVER’ having asthma and when considerin g 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 ge nder, 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 pr eferred. 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 Smoothin g Paramete r
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 devel oped 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 stude ntized residuals should be approximately normally distributed w ith a mean of zero and a variance of  2 

# = 1. To test this assumption, normal probability plots of the residu als were created for each smoothing parameter,

# 5C-11 combining all the studentized re siduals across genders, regions, poverty status, and ages. These normal probability plots are prov ided 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 be low), 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 woul d be 0.9, as Attachment B, Figures 5CB-3 and 5CB-4 supports

this conclusion.

Figure 5C-1. Normal probabilit y plot of studentized residua ls generated using logistic model, smoothing set to 0.7, and the ch ildren ‘EVER’ asth matic 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 sa me graph. Also plotted is a

LOESS smoothed curve fit to th e same set of points, with SAS’s optimal smoothing parameter choice, to indicate the typical pa ttern. Ideally there should be no obvious pattern and an average studentized residual close to zero with no regr ession slope (e.g., see Figure 5C-2). For the children data sets, these plots generally indi cate no unusual patterns, and the results for smoothing parameters 0.4 through 0.6 indicate a fit LOESS curve closes t to the studentized residual equals zero line. When considering th e adult data sets, agai n the appropriate value would be 0.9, as Attachment B, Figures 5 CB-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 3student



### N orm al Percentiles

# 5C-12 Figure 5C-2. Studentized residua ls versus model predicted beta s generated using a logistic model and using the children ‘EVER’ asthma tic data set, with smoothing set to 0.6.

When considering both children asthma pr evalence responses evaluated, the residual standard error (estimated values for sigma) s uggests the choice of smoothing parameter as 0.6 to 0.8. The normal probability plots of the studen tized residuals suggest preference for smoothing at or above 0.6. The plots of residuals agains t smoothed predictions suggest the choices of 0.4 through 0.6. We therefore chose th e 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 presenta tion are provided in Attachment C, following a similar format as th e unsmoothed data provid ed in Attachment A.

5C-5 CENSUS TRACT LEVEL POVERTY RAT IO 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 gr oups 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 geographi c data (“geo”) .uf3 files and associated

documentation were downloaded 10 

# 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 A ll: LO ESS Sm oothed M idw est-Fem ale-A bovePovertyLev M

idw est-Fem ale-B elow PovertyLev M idw est-M ale-A bovePovertyLevel M

idw est-M ale-B elow PovertyLevel N ortheast-Fem ale-A bovePovertyL N

ortheast-Fem ale-B elow PovertyL N ortheast-M ale-A bovePovertyLev N

ortheast-M ale-B elow PovertyLev South-Fem ale-A bovePovertyLevel South-Fem ale-B elow PovertyLevel South-M ale-A bovePovertyLevel South-M ale-B elow PovertyLevel W est-Fem ale-A bovePovertyLevel W

est-Fem ale-B elow PovertyLevel W est-M ale-A bovePovertyLevel W

est-M ale-B elow PovertyLevel

# 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 da ta set retained the following variables: stusab, sumlev, logrecno, stat e, county, tract, name, latitude, and longitude. Second, the individual state level SF3 file s (“30”) were downloaded, retaining the number of persons across the variab le “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 stat e logrecno using the number of persons from

the PCT50 stratificati ons; the fraction of those pers ons having poverty ratios < 1.5 and ≥ 1.5 by summing the appropriate PCT50 variable and divi ding by the total number of persons in that age/age group. Finally the poverty ratio data we re combined with the above described census tract level geographic data using the “stusab” an d “logrecno” variables. The final output was a single file containing relevant tract level povert y 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 file s 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 fr equency of missing data substitu tion 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 pers ons exposed, particularly c onsidering latter case as no persons in that age group would be modeled.

# 5C-14 whereas each US census tract value now e xpresses a tract specific poverty-weighted prevalence, stratified by ages (c hildren 0-17), age groups (adults), and two genders. These final prevalence data are f ound within the APEX asthmaprevalence.txt file.

5C-7 REFERENCES Cohen, J., and Rosenbaum, A. (2005). Anal ysis of NHIS Asthma Prevalence Data. Memorandum to John Langstaff by ICF Incorpor ated. For US EPA Work Assignment 3- 08 under EPA contract 68D01052. SAS. (2012). SAS/STAT 9.2 User's Gu ide, Second Edition. Available at: http://support.sas.com/documentation/ cdl/en/statug/63033/PDF/default/statug.pdf

. US Census Bureau. (2007). 2000 Census of P opulation and Housing. Summary File 3 (SF3) Technical Documentation, available at: h ttp://www.census.gov/prod/cen2000/doc/sf3.pdf. Individual SF3 files '30' (for income/pove rty variables pct50) for each state were downloaded from: http://www2.census.gov/census_2000/datasets/Summary_File_3/

. US EPA. (2007). Ozone Population Exposure Anal ysis 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/sta ndards/ozone/s_o3_cr_td.html

. US EPA. (2008). Risk and Exposure Asse ssment to Support the Review of the NO 2 

# Primary National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 2008. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/nox/data/20081121_NO2_REA_final.pdf

. US EPA. (2009). Risk and Exposure Asse ssment to Support the Review of the SO 2 

# Primary National Ambient Air Quality Standard. Report no. EPA-452/R-09-007. August 2009. Available at: http://www.epa.gov/ttn/naaqs/standard s/so2/data/200908SO2REAFinalReport.pdf.

# 5C-15 

# Appendix 5C, Attachment A

Unsmoothed Asthma Prevale nce Tables and Figures

Appendix 5C, Attachment A, Table CA-1. Unsmoothe d 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. Unsmoothe d 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. Unsmoothe d 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. Unsmoothe d 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. Unsmoothe d 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. Unsmoot hed 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. Unsmoot hed 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 0.07815C-29Appendix 5C, Attachment A, Figure CA-1.Unsmoothed prevalence and confidence intervals for children ‘EVER’ having asthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-30Appendix 5C, Attachment A, Figure CA-1, cont.Unsmoothed prevalence and confidenceintervals for children ‘EVER’ havingasthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Rawasthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-31Appendix 5C, Attachment A, Figure CA-2.Unsmoothed prevalence and confidence intervals for children ‘STILL’ having asthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-32Appendix 5C, Attachment A, Figure CA-2, cont.Unsmoothed prevalence and confidenceintervals for children ‘STILL’ havingasthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Rawasthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-33Appendix 5C, Attachment A, Figure CA-3.Unsmoothed prevalence and confidence intervals for adults ‘EVER’ having asthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-34Appendix 5C, Attachment A, Figure CA-3, cont.Unsmoothed prevalence and confidenceintervals for adults ‘EVER’ havingasthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Rawadult asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-35Appendix 5C, Attachment A, Figure CA-4.Unsmoothed prevalence and confidence intervals for adults ‘STILL’ having asthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-36Appendix 5C, Attachment A, Figure CA-4, cont.Unsmoothed prevalence and confidenceintervals for adults ‘STILL’ havingasthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Rawadult asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male

# 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 2.3963815C-45Appendix 5C, Attachment B, Figure 5CB-1.Normal probability plots of studentized residualsgenerated using logistic model and children ‘EVER’asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.40.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.50.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.60.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.70.1151025507590959999.9-3-2-10123

studentNormal Percentiles5C-46Appendix 5C, Attachment B, Figure 5CB-1, cont.Normal probability plots of studentized residuals generated using logistic model and children‘EVER’ asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.80.1151025507590959999.9-3-2-10123

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=10.1151025507590959999.9-4-3-2-101234

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.90.1151025507590959999.9-4-3-2-101234

studentNormal Percentiles5C-47Appendix 5C, Attachment B, Figure 5CB-2.Normal probability plots of studentized residuals generated using logistic model and children‘STILL’ asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.40.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.50.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.60.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.70.1151025507590959999.9-4-3-2-10123

studentNormal Percentiles5C-48Appendix 5C, Attachment B, Figure 5CB-2, cont. Normal probability plots of studentized residuals generated using logistic model and children‘STILL’ asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.80.1151025507590959999.9-4-3-2-101234

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=0.90.1151025507590959999.9-6-4-2024

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAll genders, regions, poverty ratios combinedSmoothingParameter=10.1151025507590959999.9-6-4-2024

studentNormal Percentiles5C-49Appendix 5C, Attachment B, Figure 5CB-3.Normal probability plots of studentized residualsgenerated using logistic model and adult ‘EVER’asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAdults: All genders, regions, poverty ratios combinedSmoothingParameter=0.80.1151025507590959999.9-2.0-1.5-1.0-0.500.51.01.5

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAdults: All genders, regions, poverty ratios combinedSmoothingParameter=0.90.1151025507590959999.9-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAdults: All genders, regions, poverty ratios combinedSmoothingParameter=10.1151025507590959999.9-2-10123

studentNormal Percentiles5C-50Appendix 5C, Attachment B, Figure 5CB-4.Normal probability plots of studentized residualsgenerated using logistic model and adult ‘STILL’asthmatic data set.Normal probability plot of studentized residuals by smoothing parameterAdults Still: All genders, regions, poverty ratios combinedSmoothingParameter=0.80.1151025507590959999.9-2.0-1.5-1.0-0.500.51.01.52.0

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAdults Still: All genders, regions, poverty ratios combinedSmoothingParameter=0.90.1151025507590959999.9-3-2-1012

studentNormal PercentilesNormal probability plot of studentized residuals by smoothing parameterAdults Still: All genders, regions, poverty ratios combinedSmoothingParameter=10.1151025507590959999.9-3-2-10123

studentNormal Percentiles5C-51Appendix 5C, Attachment B, Figure 5CB-5.Studentized residuals generated using logistic model versus model predicted betas and the child‘EVER’ asthmatic data set.student-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.4reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.5reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.6reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.7reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel5C-52Appendix 5C, Attachment B, Figure 5CB-5, cont.Studentized residuals generated using logistic model versus model predicted betas and the child‘EVER’ asthmatic data set.student-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.8reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=0.9reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of prevalence rates by smoothing parameterSmoothingParameter=1reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel5C-53Appendix 5C, Attachment B, Figure 5CB-6.Studentized residuals generated using logistic model versus model predicted betas and the child‘STILL’ asthmatic data set.student-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.4reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.5reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.6reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.7reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel5C-54Appendix 5C, Attachment B, Figure 5CB-6, cont.Studentized residuals generatedusing logistic model versus model predicted betas using child‘STILL’ asthmatic data set.student-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.8reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=0.9reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-5-4-3-2-1012345Predicted logitprev-5.00000 -4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of still prevalence rates by smoothing parameterSmoothingParameter=1reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel5C-55Appendix 5C, Attachment B, Figure 5CB-7.Studentized residuals generatedusing logistic model versus model predicted betas using adult‘EVER’ asthmatic data set.student-3-2-10123Predicted logitprev-3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameterSmoothingParameter=0.8reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-3-2-10123Predicted logitprev-3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameterSmoothingParameter=0.9reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-3-2-10123Predicted logitprev-3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult prevalence rates by smoothing parameterSmoothingParameter=1reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel5C-56Appendix 5C, Attachment B, Figure 5CB-8.Studentized residuals generatedusing logistic model versus model predicted betas using adult‘STILL’ asthmatic data set.student-3-2-10123Predicted logitprev-4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameterSmoothingParameter=0.8reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-3-2-10123Predicted logitprev-4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameterSmoothingParameter=0.9reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevelstudent-3-2-10123Predicted logitprev-4.00000 -3.00000 -2.00000 -1.00000 0Studentized residual versus smoothed logits of adult still prevalence rates by smoothing parameterSmoothingParameter=1reggendpovAll: LOESS Smoothed Midwest-Female-AbovePovertyLevMidwest-Female-BelowPovertyLev Midwest-Male-AbovePovertyLevelMidwest-Male-BelowPovertyLevel Northeast-Female-AbovePovertyLNortheast-Female-BelowPovertyL Northeast-Male-AbovePovertyLevNortheast-Male-BelowPovertyLev South-Female-AbovePovertyLevelSouth-Female-BelowPovertyLevel South-Male-AbovePovertyLevelSouth-Male-BelowPovertyLevel West-Female-AbovePovertyLevelWest-Female-BelowPovertyLevel West-Male-AbovePovertyLevelWest-Male-BelowPovertyLevel

# 5C-57 

# Appendix 5C, Attachment C

Smoothed Asthma Prevalenc e 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. Smooth ed 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. Smooth ed 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 0.12505C-71Appendix 5C, Attachment C, Figure 5CC-1.Smoothed prevalence and confidence intervals for children ‘EVER’ having asthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-72Appendix 5C, Attachment C, Figure 5CC-1, cont.Smoothed prevalence and confidence intervals for children ‘EVER’ havingasthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 1. Smoothed asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-73Appendix 5C, Attachment C, Figure 5CC-2.Smoothed prevalence and confidence intervals for children ‘STILL’ having asthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-74Appendix 5C, Attachment C, Figure 5CC-2, cont.Smoothed prevalence and confidence intervals for children ‘STILL’ havingasthma.prev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.40.50.6age0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Figure 2. Smoothed asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-75Appendix 5C, Attachment C, Figure 5CC-3.Smoothed prevalence and confidence intervals for Adults ‘EVER’ having asthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-76Appendix 5C, Attachment C, Figure 5CC-3, cont.Smoothed prevalence and confidence intervals for Adults ‘EVER’ havingasthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 3. Smoothed adult asthma 'EVER' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male5C-77Appendix 5C, Attachment C, Figure 5CC-4.Smoothed prevalence and confidence intervals for Adults ‘STILL’ having asthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=Midwest pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=Northeast pov_rat=BelowPoverty LevelgenderFemale Male5C-78Appendix 5C, Attachment C, Figure 5CC-4, cont.Smoothed prevalence and confidence intervals for Adults ‘STILL’ havingasthma.prev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=South pov_rat=BelowPoverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=Above Poverty LevelgenderFemale Maleprev0.00.10.20.30.4age_grp18-24 25-34 35-44 45-54 55-64 65-74 75+Figure 4. Smoothed adult asthma 'STILL' prevalence rates and confidence intervalsregion=West pov_rat=BelowPoverty LevelgenderFemale Male

# 5D-i 

# APPENDIX 5D

Variability Analysis and Uncertainty Characterization

Table of Contents

5D-1. OVERVIEW .............................................................................................................. 5 D-1

5D-2. TREATMENT OF VARIABILIT Y 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 variab ility modeled by APEX. ................................. 5D-4 Table 5D-2. Important components of co-variab ility in exposure modeling. ....................... 5D-6

# 5D-1 5D-1. OVERVIEW An important issue associated with any popul ation exposure or risk assessment is the

characterization of variab ility and uncertainty. Variability refers to the inherent heterogeneity in a population or variable of interest (e.g., residential air exchange ra tes). 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 pu rpose of the assessment (i.e., scenario uncertainty ). Uncertainty is, ideally, reduced to the maximum extent po ssible through improved measurement of key parameters and iterative model refinement. The approaches used to assess variability and to characterize uncertainty in this HREA are discus sed in the following two sections. The primary purpose of this characterizati on is to provide a summary of variability and uncertainty

evaluations conducted to date regarding our O 3 

# exposure assessments and APEX exposure modeling and to identify the most important elements of uncertainty in need of further

characterization. Each section contains a concis e tabular summary of the identified components and how, for elements of uncertainty, each sour ce 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 O 3 

# concentrations, populat ion characteristics,

associated O 3 

# exposure and intake dose, and potential health risk across the study area and for the simulated at-risk populations . In this HREA, there are se veral algorithms that account for variability of input data when generating the number of estimat ed benchmark exceedances or health risk outputs. For example, variabili ty may arise from differences in the population residing within census tracts (e.g., age distribution) and the activities that may affect population exposure to O 3 

# and the resulting intake dose estimate (e.g., time spent outdoors, performing moderate or greater exertion leve l activities outdoors). A comp lete 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 para meters, algorithms, or modeling system used, not necessarily the complete range of the true exposure or risk values. Where possible, staff identified and incorporat ed the observed variability in input data sets rather than employing standard default assump tions and/or using point estimates to describe model inputs. The details regardi ng variability distributions used in data inputs are described in

# 5D-2 Appendix 5B, while details regarding the vari ability addressed within its algorithms and processes are found in the AP EX TSD (US EPA, 2012). Briefly, APEX has been designe d 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 simu lates individuals and then calculates O 3 

# exposures for

each of these simulated individuals. The individu als are selected to re present a random sample from a defined population. The collection of indivi duals represents the vari ability of the target population, and accounts for several types of vari ability, including demographic, physiological, and human behavior. In this assessment, we si mulated 200,000 individuals to reasonably capture the variability expected in the population e xposure distribution for each study area. APEX incorporates stochastic processes representing the natural variability of personal profile characteristics, activity patterns, and microenviro nment parameters. In this way, APEX is able to represent much of the variability in the exposure estimates re sulting from the variability of the factors effecting human exposure.

We note also that correlations and non-linear relationships be tween variables input to the model can result in the model pr oducing incorrect result s if the inherent relationships between these variables are not preserve d. That is why APEX is al so designed to account for covariability, or linear and nonlin ear correlation among the model i nputs, 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 w ithin APEX. For example, there is a non-linear relationship between the outdoor temp erature and air exchange rate in homes. One factor that

contributes to this non-linear re lationship 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 modele d 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 mode l and the estimated exposure distributions reflect this variability. For exam ple, the rate of O 3 

# 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 O 3 

# decay. APEX randomly samples from this

distribution to obtain values which are used in the mass balance model. Further, co-variability can be modeled stocha stically through the use of conditional distributions. If two or more parameters are related, conditiona l distributions that

# 5D-3 depend on the values of the related parame ters 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 diffe rent 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 O 3 

# concentrations and temper atures are used in model calculations. These are input to the model for every hour in the time period modeled at different spatial locations, a nd in this way the variability and covariability of hourly concentrations a nd temperatures are modeled explicitly. Important sources of the vari ability and co-variability acc ounted 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- a nd 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 O 3 

## concentrations Temporal: 1-hour concentrations for an entire O 3 

## 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 in corporated 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 co ncentrations 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 O 3 

## concentrations occurring during a previous hour (and of ambient origin) to calculate a current event’s indoor O 3 

## 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 (NVO 2 

## ) 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 sect ion 7.2 of APEX TSD).

METS by activity Values randomly sampled from distributions developed for specific activities (a few are age-group specif ic) (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 re gression 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 al so 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 repres ent 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 in fluential 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 proc ess 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 availa ble. However, significant uncertainty often remains and emphasis is then placed on characte rizing the nature of that uncertainty and its impact on exposure and risk estimates.

In the final 2008 O 3 

# NAAQS rule, 1 

# EPA staff performed such a characterization and at that time, identified the most important uncertain ties affecting the exposure estimates. The key elements of uncertainty were 1) the m odeling of human activity patterns over an O 3 

# season, 2) the modeling of variations in ambient O 3 

# concentrations near roadways, 3) the modeling of air exchange rates that affect the amount of O 3 

# that penetrates indoors, a nd 4) the characterization of energy expenditure (and related ventilation rate estimates) fo r children engaged in various activities. Further, the primary findings of a qua ntitative 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 asth matic children with exposures above 0.06, 0.07, or 0.08 ppm-8hr under moderate exertion have 95% we re estimated by APEX to have uncertainty intervals of at most ±6 percenta ge points. Details for these previously identified uncertainties are

discussed in the 2007 O 3 

# Staff Paper (section 4.6) and in a technical memorandum describing the

2007 O 3 

# exposure modeling uncertainty analysis (Langstaff, 2007).

The REA’s conducted for the most recent NO 2 

# (US EPA, 2008), SO 2 

# (US EPA, 2009), and CO (US EPA, 2010) NAAQS reviews also pres ented characterizations of the uncertainties associated with APEX exposure modeling (among other pollutant specific issues), albeit mainly qualitative evaluations. Conclu sions drawn from all of these assessments regarding exposure modeling uncertainty have been integrated here, following the st andard approach used by EPA staff since 2008 and outlined by WHO (2008) to identify, evalua te, and prioritize the most important uncertainties relevant to the estimated potential health effect endpoints used in this O 3 

# HREA. Staff selected a mainly qualitative approach here supplemented by various model sensitivity analyses and input data evaluations, all complimenta ry to quantita tive uncertainty

characterizations conducted for the 2007 O 3 

# 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 fr om that described by WHO (2008) in that a greater focus was placed on evaluati ng the direction and the magnitude 2 

# 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 uncertain ty. 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 betw een 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 eff ect on the exposure results. For example, we have commonly employed statistical procedure to substitute missing concentration values to complete the meteorological data sets. Sta ff has consistently compared the air quality distributions and found negligible differences be tween 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 subs titutions needed to complete a data set. There is still uncertainty in the approach used, and there ma y be alternative, and possibly better, methods available to perform such a task. However, in this instance, staff judge d that the quantitative comparison of the ambient concentration data se ts indicates that ther e 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 pro portional) 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 O 3 

# concentration distribution mi ght look in the future, we do know however what the distribution might lo ok 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 es timated 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 es timated 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 th ree) in the estimated number of persons exposed in more than half of the twelve study areas (Langstaff, 2007). Assuming that these types of ambient concentra tion adjustments could re flect the addition of a new or unaccounted for emission source in a pa rticular study area, staff also judged the magnitude of influence in using the quadratic appr oach 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 estima tes were extremely sensitive to the identified source of uncertainty. In addition to characterizing the magnitude of uncertainty, staff also included the direction of influence, indicati ng 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 th e 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 j udge the directional nature of uncertainty associated with the particular source. Sta ff also subjectively scaled the knowledge-base uncertainty associated with each identif ied 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 characteri zation 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 characte rization 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 Heal th Interview Survey, years 2006-10. U.S. Department of Health a nd 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, La ngstaff 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 ve ntilation 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). Meta bolically Derived Huma n Ventilation Rates: A Revised Approach Based Upon Oxygen Consum ption Rates (Final Report). Report no. EPA/600/R-06/129F. Appendix D contains “Response to peer-review comments on Appendix A”, prepared by S. Grah am (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 Appendi x 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 Subm itted 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, Ca pel 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 St aff Memorandum to Ozone NAA QS Review Docket (OAR2005-0172). Subject: Analysis of Uncertainty in Ozone Population Exposure Modeling. [January 31, 2007]. Available at: http://www.epa.gov/ttn/naaqs/st andards/ozone/s_o3_cr_td.html McCurdy T. (2000). Conceptual basis for mu lti-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, a nd review of previous work. Hum Nutr Clin Nutr . 39C(S1):5-41. US Census Bureau. (2007a). Employment St atus: 2000- Supplemental Tables. Available at: http://www.census.gov/population/www/cen2000/phc-t28.html

.

# 5D-11 US Census Bureau. (2007b). 2000 Census of P opulation and Housing. Summary File 3 (SF3) Technical Documentation, available at: http://www.census.gov/pr od/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 Hu man Activities Database. Available at: http://www.epa.gov/chad/

. US EPA. (2007). Ozone Population Exposure Analys is 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/st andards/ozone/s_o3_cr_td.html US EPA. (2008). Risk and Exposure Asse ssment to Support the Review of the NO 2 

# Primary National Ambient Air Quality Standard. Report no. EPA-452/R-08-008a. November 2008. Available at: http://www.epa.gov/ttn/naaqs/sta ndards/nox/data/20081121_NO2_REA_final.pdf

. US EPA. (2009). Risk and Exposure Asse ssment to Support the Review of the SO 2 

# Primary National Ambient Air Quality Standar d. Report no. EPA-452/R-09-007. August 2009. Available athttp://www.epa.gov/ttn/naaqs/standard s/so2/data/200908SO2REAFinalReport.pdf. US EPA. (2010). Quantitative Risk and Exposure Assessment for Carbon Monoxide – Amended. EPA Office of Air Quality Planni ng and Standards. EPA-452/R-10-009. July 2010. Available at: http://www.epa.gov/ttn/naaqs/standa rds/co/data/CO-REA-AmendedJuly2010.pdf US EPA. (2012). Total Risk Integrated Met hodology (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 Docume nt No. 6. Part 1: Guidance document on characterizing and communicating uncertainty in exposure assessment. Available at: http://www.who.int/ipcs/methods/har monization/areas/exposure/en/.

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## 5E-i 

# APPENDIX 5E

Updated Analysis of Air Exchan ge 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; Bost on, MA; Chicago, IL; Cleveland, OH; Dallas, TX: De troit, MI; Denver, CO: Houston, TX; Los Angeles, CA; New York, NY; Philadelphia, PA; Sacrament o, CA; Seattle, WA; St. Louis, MO-IL; Washington, DC. As part of this effort, ICF Internationa l 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 de scribes the analysis of the AER data and the proposed APEX input distri butions. 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 st udy, ozone concentrations and AERs were measured at 126 residences in the great er Los Angeles me tropolitan area between February and December, 1994. M easurements were taken in four communities: Lancaster, Lake Gregory, Riverside, and San Dimas. Data included the daily average outdoor temper ature, the presence or absence of an air conditioner (either central or room), and the presence or absence of a swamp (evaporative) cooler. Air exchange ra tes 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 we re measured at 37 residences in central North Carolina during 200 0 and 2001 (averaging about 23 AER measurements per residence). The residenc es 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 hype rtension living in a low-to-moderate SES neighborhood in Raleigh. Data included the daily average outdoor temperature, and the number of air conditioner units (eit her 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 measur ements (generally over 24 hours) used a PMCH tracer. Data included the daily average outdoor tem perature, and the presence or absence of central air condi tioning, 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/Harva rd (TEACH) study was designed to characterize levels of and factors infl uencing exposures to air toxics among high school students living in inner-city neighborhoods of New York City and Los Angeles, CA. Volatile organic com pounds, 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 th is 1984 study, AER and other data were collected at about 600 southern Ca lifornia 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. T he available data consisted of the three seven-day averages, the month, the re sidence zip code, the presence or absence of a central air conditioner, and the presence or absence of a room air conditioner. We matched these dat a by month and zip code to the corresponding monthly average temper atures obtained from EPA’s SCRAM website as well as from the archives in www.wunderground.com (personal and airport meteorological stati ons). Residences more th an 25 miles away from the nearest available meteorolog ical 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 dat a consisted of the two-day averages, the date, city name, the residence zip code, the pr esence 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 conditi oner. We matched these data by date, city, and zip code to the corresponding dai ly average temperatures obtained from EPA’s SCRAM website as well as from the archives in www.wunderground.com (personal and airport meteor ological stations). Resi dences more than 25 miles

## 5E-3 

# away from the nearest available meteorol ogical station were excluded from the analysis. For our analyses, the city/locat ion 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 nat ionwide residential AER data from several studies conducted between 1982 and 1987. These data were originally compiled by the Lawrence Berkeley Nati onal 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 Ex posure and Aerosol Research Study (DEARS) collected air exchange rate data as well as PM 2.5 

# and other air pollutant data at about 120 homes in Detroit, Michi gan, 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, t he 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 num ber 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 desig n, although some of these studies included information on whether air c onditioners or fans were used (and for how long) and whether windows were closed duri ng 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 P anel study selected two specific cohorts of older subjects with specific diseases. T he RIOPA study was biased towards residences near air toxics sources. The TEACH st udy 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 neighb orhoods 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 Detroi t metropolitan area, th e 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 represent ative of an older hous ing stock than the overall Detroit metropolitan area.

All data and statistical results are comp iled into the attac hed Excel spreadsheet Summary_Statistic.Dec 2012.xls.5E-4Table 1. Summary of Studies ofResidential Air Exchange Rates.Study/AttributeAvolRTP PanelRIOPATEACHWilson 1984Wilson 1991Murray andBurmasterDEARSLocationsLancaster, LakeGregory,Riverside, SanDimas. All inSouthern CAResearchTriangle Park, NCCA; NJ; TXLos Angeles, CA;New York City, NYSouthern CA Southern CAAZ, CA, CO,CT, FL, ID,MD, MN, MT,NJDetroit, MIYears 1994 2000; 20011999; 2000;20011999; 2000 1984, 1985 1984 1982 – 1987 2004 – 2006Months/SeasonsFeb; Mar; Apr;May; Jun; Jul;Aug; Sep; Oct;Nov2000 (Jun; Jul;Aug; Sep; Oct;Nov), 2001 (Jan;Feb; Apr; May)1999 (July toDec); 2000 (allmonths); 2001(Jan and Feb)1999 (Feb; Mar;Apr; Jul; Aug);2000 (Jan; Feb;Mar; Sep; Oct)Mar 1984, Jul 1984, Jan1985Jan, Mar, Jul VariousJan. Feb, Mar,Jul. AugHomes withAERMeasurements86 37 284 85 581 288 1,884 127Total AERMeasurements161 854 524 151 1,362 316 2,844 868Average AERMeasurementsper Home1.87 23.08 1.85 1.78 2.34 1.10 1.51 6.83AERMeasurementDurationNot Available 24 hour 24 to 96 hoursSample time (hours)reported. Rangesfrom about 1 to 7days7 days 7 days Not available 24 hourAERMeasurementTechniqueNot AvailablePerflourocarbontracerPMCH tracerPerflourocarbontracerPerflourocarbon tracer Perflourocarbon tracer Not availablePerflourocarbontracerMin AER (h-1) 0.01 0.02 0.08 0.12 0.03 0.01 0.01 0.08Max AER (h-1) 2.70 21.44 87.50 8.87 11.77 2.91 11.77 13.56Mean AER (h-1) 0.80 0.72 1.41 1.71 1.05 0.57 0.76 0.80Min Temp. (°C) -0.04 -2.18 -6.82 -1.36 11.00 3.00 Not available -12.92Max Temp. (°C) 36.25 30.81 32.50 32.00 28.00 25.00 Not available 30.79Air ConditionerCategoriesNo A/C; Centralor Room A/C;Swamp Cooleronly; Swamp +[Central orRoom]Central or RoomA/C (Y/N)Window A/C(Y/N); EvapCoolers (Y/N)Central or RoomA/C (Y/N)Central A/C (Y/N); RoomA/C (Y/N);Central A/C (Y/N); RoomA/C (Y/N); SwampCooler(Y/N)Not availableCentral orRoom A/C(Y/N)5E-5Study/AttributeAvolRTP PanelRIOPATEACHWilson 1984Wilson 1991Murray andBurmasterDEARSAir ConditionerMeasurementsMadeA/C use inminutesNot AvailableDurationmeasurements inHrs and MinsNot Available Not Available Not Available Not available Not availableFanCategoriesNot available Fan (Y/N) Fan (Y/N) Not Available Not Available Not AvailableNot available Fan (Y/N)FanMeasurementsMadeTime on or off forvarious fan typesduring samplingwas recorded,but not includedin databaseprovided.Not AvailableDurationmeasurements inHours andMinutesNot Available Not AvailableNot Available Not availableDaily durationin minutesWindow Open/Closed DataDuration openbetween times6am-12 pm;12pm - 6 pm;and 6pm - 6amWindows (open /closed along withduration open ininch-hours unitsWindows (Open /Closed) alongwith windowopen durationmeasurementsNot Available Not AvailableNot Available Not availableWindows (Open/ Closed) alongwith windowopen durationmeasurementsCommentsCA sample wasa random sampleof homes. NJand TX homeswere deliberatelychosen to benear to ambientsources.Restricted to innercity homes with highschool students.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological data.Contemporaneoustemperature dataobtained for theseanalyses from SCRAMandwww.wunderground.commeteorological data.

## 5E-6 

# We compiled the data from these eight studi es 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 condition er (A/C), either a room A/C or central A/C or swamp cooler or any of t hem in combination. “Y” = “Yes.” “N” = “No”

 Type_of_AC1 – Indicates t he types of A/C or swamp cooler available in each house measured. Possible values: “C entral A/C” “Central and Room A/C” “Central or Room A/C” “N o A/C” “Swamp + (Central or Room)” “Swamp Cooler only” “Window A/C” “Window and Evap” “ ” (missing)

 Type_of_AC2 – Indicates if a house meas ured has either no A/C or some A/C. Possible values: “No A/C” “Centra l or Room A/C” “ ” (missing)

 Type_of_AC3 – Indicates if a house measur ed has either no A/C, central A/C, or room A/C. If separat e A/C information is available for central A/C and for room A/C, then homes with both central and r oom A/C are coded as having “Central A/C.” Possible values: “No A/C” “C entral 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 “N ot 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 avail able data did not incl ude the presence or absence of an air conditioner or window fan. Instead, the data set included a flag indicating whether or not an ai r conditioner or window fan wa s used on that day. For the data analysis, we assumed that if the air c onditioner or window fan was used on at least one day, then the air conditioner or wi ndow fan was present; otherwise the air conditioner or window fan was absent. Si nce 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 t he attached PC SAS dataset complete_database2.sas7bdat.

The main data analysis was based on the se ven studies other than the Murray and Burmaster study. The Murray and Burmaster data were excluded because of the absence of information on air conditioner pres ence. (However, a subset of these data was used for a supplementary analysis described below.)

Based on our review of the AER data we excl uded nine values with unrealistically high AER values – above 10 per hour (see the wo rksheet “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 corres ponding information on swamp cooler prevalence for the subsequent ozone modeling cities was not available from the American Hous ing Survey. It is plausible that AER distributions depend upon the presence or absence of a swam p cooler. It is also plaus ible that AER distributions also depend upon whether the residence specific ally has a central A/C, room or window A/C, or both. However we determined to us e the broader A/C type definition, which in effect assumes that the exact A/C type and the presenc e of a swamp cooler are approximately proportionately re presented in the surveyed residences. The detailed A/C type variable Type_of_AC3 was used fo r 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 wit h the house as well as other factors such as the temperature. (T he A/C type can be assumed to be the same for each measurement of the same house). We ex pected the temperatur e to be an important factor since the AER will be affected by t he use of the available ventilation (air conditioners, windows, fans), which in tu rn will depend upon the outside meteorology. Therefore it is not appropriate to aver age data for the same house under different conditions, which might have been one way to account for dependence between multiple measurements on the sa me house. To simplify the data analysis, we chose to ignore possible dependence bet ween 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 statis tics for AER and its natural logarithm LOG_AER on selected strata defin ed from the study, ci ty, 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, Or ange, 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”. T he 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 followi ng summary statistics for AER and LOG_AER respectively are shown in the wor ksheets “SUMMARY_STATISTICS_AER” and “SUMMARY_STATISTICS_LOG_AER”:

 Number of values

 Arithmetic Mean

 Arithmetic Standard Deviation

 Arithmetic Variance

 Deciles (Min, 10 th 

# , 20 th 

# ... 90 th 

# 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 clearl y demonstrates that t he AER distributions vary greatly across cities and A/C types and temperatures, so that the selected AER distributions for the model ed cities should also depend upon the city, A/C type and temperature. For example, the mean AER for residences wit h 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 betw een 25 and 30 oC. The need to account for the city as well as the A/ C type and temperature is illustra ted 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 Yo rk. Statistical comparisons are described below.

Statistical Comparisons. Various statistical comparisons between the different strata are shown in the worksheets CO MPARISON_STATISTICS_AER and COMPARISON_STATISTICS_LOG_AER, for the AER and its logarithm, respectively. The various strata are defined as in the Summa ry Statistics section, excluding the “All” cases. For each analysis, we fixed one or tw o of the variables St udy, City, A/C type, temperature, and tested for statistically si gnificant 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 defi ned by the A/C type and temperature range, without specifying the city. Similarly, for the sixth set of comparison s, the study and city are held fixed (18 combinations) and in each case we compare AER distributions across groups defined by the combination of t he A/C type and the temperature range.

The F-Statistic comparisons compare t he 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 worksh eets are the F-Statis tic 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 m eans 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 t hat for the majority of cases, means vary significantly across cities, whet her you first stratify by te mperature range, A/C type, or both.

The Kruskal-Wallis Statistic comparisons ar e non-parametric tests t hat 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 te st is also consistent under weaker assumptions against more general alternatives). The P-values show similar patterns to the parametric F-test comparis ons of the means. Since t he 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-par ametric 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 stat istically significantly different. Since the logarithm is a strictly increasing function and the test is no n-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 combinat ion with a minimum of 11 AER values, we fitted and compared exponent ial, log-normal, normal, and Weibull distributions to the AER values.

## 5E-10 

# The first analysis used the sa me stratifications as in t he above “Summary Statistics” and “Statistical Comparisons” sections. Results are not reported for all strata because of the minimum data requirement of 11 values. Result s for each combinati on of A/C type, city, and temperature (i.e., A, C, and T) are given in the wor ksheet FIT_STATISTICS_ACT. Each combination has four rows, one for each fitted distribution. For each distribution we report the fitted paramet ers (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 co mpares the empirical distribution of the AER values to the fitt ed distribution. The K-S and C-M tests are different tests examining the overall fit, wh ile the Anderson-Darlin g test gives more weight to the fit in the tails of the distribution. For each co mbination, the best-fitting of the four distributions has the highest p-val ue 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 s hape parameters ar e 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 geomet ric 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 di stributions were available only for selected cities, and yet the summary statisti cs and comparisons demonstrate that the AER distributions depend upon the city as well as the temperatur e range and A/C type. As one option to address this issue, we c onsidered modeling cities not listed in the FIT_STATISTICS_ACT worksheet by using t he 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 limitati on of the initial analysis was that distributions were not fitted to all of the temperat ure ranges due to i nadequate data. There are missing values between temperature ranges, and the temperature ranges ar e 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 av ailable data to fit these ranges have finite temperatures. St ratifying 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: Detr oit, AC and NA; Houston, AC and NA; Los Angeles, AC and NA; New York, AC and NA; Research Triangle Pa rk, 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 w hole. For this reason, the worksheet FIT_STATISTICS_ACT2 also reports result s 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 distributi ons in FIT_STATISTICS _ACT2, we created histograms to compare each of the fitted dist ributions 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 works heet. The first three digits are a numerical code used for sorti ng the graphs, and the remaining characters code the A/C type, temperature range, and ci ty in a fairly obvious manner. These graphs are in the attached file Graphs_ACT 2.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 c onditioning system, and we decided to use distributions for the more detailed air conditioning type, Type_of_AC3, as described below.

For Atlanta, Boston, and Phil adelphia, we propose using the distribution for one of the cities thought to have similar characteristi cs to the city to be modeled with respect to factors that might influence AERs. Thes e 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 De troit 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 si milar characteristics, i.e., Chicago and Cleveland.

The distributions proposed for th e eight cities are as follows:

.

 Atlanta, GA: Use log-normal distri butions for Research Triangle Park. Residences with A/C only.

 Boston, MA: Use log-normal distributions for New York.

 Chicago, IL: Use log-normal dist ributions for “Detroit or NYC.”

 Cleveland, OH: Use log-normal dist ributions for “Detroit or NYC.”

 Detroit, MI: Use log-normal distri butions 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-norma l distributions for New York.

All the above distributions are to be f ound in the FIT_STATISTICS_ACT2 worksheet. Since the AER data for Research Triangle Pa rk was only available for residences with air conditioning, AER distri butions 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 ci ty using data from the same city, but this approach was chosen as a r easonable 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 Detroi t, 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 t he distribution for the inland parts of Los Angeles to represent Sacram ento 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 di stributions for “Sacramento” in FIT_STATISTICS_ACT3

 St. Louis, MO-IL: Use log-normal di stributions for “S t. 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 Ba ltimore and Washington DC. Baltimore and Washington DC were judged likely to have similar characteri stics to Detroit, Research Triangle Park and New York City. To choose between thes e 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 da ta for Baltimore and for Gaithersburg and Rockville, primarily collected in March. April, and May 1987, al though there is no information on daily mean temperatures or A/C type. We collected a ll the March, April, and May AER data for Detroit, Research Tr iangle Park and New York City, and compared those three distributions with t he Murray and Burmaster Maryland data for the same three months. The summary stat istics 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 t he other summary statistics worksheets. The corresponding statistical comparisons between Detroit and Maryland, Research Triangle Park and Maryland, and between New York and Maryl and, 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 Maryl and 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 sign ificantly different, and the differences in the mean log (AER) values are much less statistically significant than between New York and Maryland. The scale st atistic comparisons are not statistically significantly different between New Yo rk and Maryland or between Detroit and

## 5E-13 

# Maryland, but were statistically significant ly different between Research Triangle Park and Maryland. Since matching central and m ean values is generally more important than matching the scales, we propose to model both Baltimore and Washington DC residences with air conditioning using the Res earch Triangle Park distributions, stratified by temperature:

 Washington DC: Use log-normal distribut ions for Research Triangle Park in FIT_STATISTICS_ACT2. Residences with A/C only.

 Baltimore, MD: Use log-normal distribut ions for Research Triangle Park in FIT_STATISTICS_ACT2. Residences with A/C only.

Since the AER data for Research Triangle Pa rk was only available for residences with air conditioning, the estimated AER dist ributions 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 Wash ington DC, Baltimore MD, a nd Atlanta GA Residences With No A/C. For Atlanta, Baltimore, and Wash ington 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 c onditioning, so there are no available distributions for the “No A/C” cases. For these th ree cities, one option is to use AER distributions fitted to all the study data for residences without A/C, stratified by temperature. These fitted distribut ions are given in the worksheet FIT_STATISTICS_AT2. The distributions fo r “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 Californi a, we instead propose to use the “No A/C” AER distributions aggregated across cities outsi de 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-norma l distributions for “St. Louis” in FIT_STATISTICS_ACT3. Residences without A/C only.

 Atlanta, GA, No A/C: Use log-norma l distributions for “St. Louis” in FIT_STATISTICS_ACT3. Resi dences without A/C only.

 Baltimore, MD. No A/C: Use log-no rmal 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 1 991 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 eval uate 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 temper ature range. The Avol study included

## 5E-14 

# information on whether or not the residence had an A/C but not on w hether 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 t he Avol data from these analys es, 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 temperat ure range are shown in the worksheets SUM_STAT_LOS_ANGELES_AER and SU M_STAT_LOS_ANGELES_LOG_AER, respectively. The corresponding statis tical comparisons bet ween residences with Central A/C only and Room A/C only are s hown in the worksheets COMPARE_LA_AER and COMPARE_LA_LOG_AER. T hese 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, rat her than only by the presence or absence of any A/C. For each of the temperatur e 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 va lues, except for the mean comparison of AER in the range 10-20 (p-value 0.06), and no statistically sign ificant 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 signifi cant differences in the scale statistics. For the temperature range 0-10, the statisti cal 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_STATISTI CS_ACT4 compares exponential, log-normal, normal, and Weibull distributions fitted to each stratu m of the Los Angeles data, defined by Type_of_AC3 and temperature r ange, 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 t he first column of FIT_STATISTICS_ACT4. Since the tabulated goodness-of -fit statistics and histogr ams generally support the lognormal distribution, we propose to model Los Angeles using the Los Angeles data stratified by the detailed A/C type, Ty pe_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 t he 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 ea ch modeled day according to the mean daily temperature. To simu late the A/C type, we obtaine d estimates of A/C prevalence from the American Hous ing 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 fo r selecting the A/C type “Central or Room A/C”. Obviously, 1-p is t he 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 (representi ng Research Triangle Park, NC). As discussed above, we propose modeling the 96-98% of Atlanta, Baltimore and Washington DC residences with A/C us ing 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 Angele s CMSA residences with central or room A/C 1



## 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 addi tional residential an d non-residential AER studies that might provide additional in formation 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 vent ilation and air exchange rates. Weschler (2000) includes a useful overview of residential and non-residential AER studies.

AER Distributions for Ot her Indoor Environments

To estimate AER distributions for non-reside ntial, indoor environment s (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 SF 6 

# 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 qua lity, including ventilation, in a large number of randomly selected office build ings throughout the U.S. The data base consists of a total of 390 AER measurem ents 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 we re relatively large, with at least 25 occupants, and preferably 50 to 60 occ upants. AERs were measured both by a volumetric method and by a CO 2 

# ratio method, and included their uncertainty estimates. For these analyses, we used the recommended “B est Estimates” defined by the values with the lower estimated uncertainty; in the va st 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 ra te, temperature, and heating, ventilating, and air conditioning (HVAC) system characteri stics. The study included: seven retail establishments; five restaurants; eight offi ces; two each of gas stations, hair salons, healthcare facilities, grocer y 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 dist ributed across each of five regions of the state; north-coastal, north-inland, south-c oastal, south-inland, and central-inland. Measurements were made in summer and winte r, and three of the buildings were sampled twice, once in each season. Thus there were a tota l of 40 measured AER values. Whole building ventilation rates were determined with a tracer decay method using SF 6 

# , 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 usi ng the individual measur ements, 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 dat a, analyses of variance of the AER values and of their logarithms confi rmed the finding of the study authors (Bennett et al 2011) that the AER varied statistically significant ly between restaurants and other buildings. Analyses of variance using a categorized ou tdoor temperature variable (15-25, 25-35, and 35 or more) also showed that the AER did not vary statistically significantly with the outdoor temperature. Al though the study authors also r eported significant differences depending upon whether or not doors were open, the stratification by whether or not doors are open would not be feasible for AP EX modeling since that would require a model for whether or not doors are kept open (per haps based on the outdoor temperature and some building characterist ics). 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 t he three studies are presented in Table 5.

The overall mean values are similar for t he three studies, but the mean value for the restaurants in the Bennett study is almost four times the m ean value for the nonrestaurants. Compared to the Turk study, the standard deviat ions are about twice as high for the Persily data and for the Bennett st udy restaurants, but are much lower for the Bennett study non-restaurants. The propos ed AER distributions were derived only from the most recent Bennett data study stratified into re staurants and non-restaurants.

Similarly to the analyses of the residential AE R distributions, we fi tted 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 of fices and other non-residential buildings.

Study Variable Subgroup N Mean Std Dev Min 25 th 

## %ile Median 75 th 

## %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 di stributions from the Bennett et al. (2011).

Scale Shape Mean Std_Dev Distribution P-ValueKolmogorovSmirnov P-ValueCramervon Mises P-ValueAndersonDarling 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-ValueKolmogorovSmirnov P-ValueCramervon Mises P-ValueAndersonDarling 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 re sidential AER distributions above.) For restaurants, according to two of the th ree 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 t he log-normal. Reasonable choices for the lower and upper bounds are the observ ed minimum and maximum AER values.

We therefore propose the following i ndoor, non-residential AER distributions.

 AER distribution for indoor, non-resi dential, restaurant microenvironments: Lognormal, with scale and shape parameter s 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-resident ial, non-restaurant microenvironments: Lognormal, with scale and shape parameter s -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 facilit ies 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-vehi cle, 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 usi ng ratio distributions developed from the Cincinnati Ozone Study (American Petroleum Institute, 1997, Appendi x B; Johnson et al. 1995). The field study was conducted in t he greater Cincinnati metr opolitan area in August and September, 1994. Vehicle test s were conducted according to an experimental design specifying the vehi cle 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 loca l roads (local). Nominal vehicle speeds (typically met over one minute intervals withi n 5 mph) were at 35 mph, 45 mph, or 55 mph. Ventilation modes were as follows:

 Vent Open: Air conditioner off. Ventilati on 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 t he vehicle, outside the vehicle, and at six fixed site monitors in the Cincinnati area.

The proximity factor can be estimated from t he distributions of the ratios of the outsidevehicle ozone concentrations to the fixed-si te 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 site 1 

## Road Type 1 

## Number of

cases 1 

## Mean 1 

## Standard Deviation 1 

## 25 th 

## Percentile 1 

## 50 th 

## Percentile 1 

## 75 th 

## Percentile 1 

## Estimated 5 th 

## Percentile 2 

## 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 25 th 

# and 75 th 

# percentiles appear to be approximately equidistant from the median (50 th 

# percentile). Therefor e 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 distributi on allows small positive values and can

## 5E-20 

# even produce impossible, negative values (with a very low pr obability), the titration of ozone concentrations near a road is limited. T herefore, as an empirical approach, we recommend a lower bound of the estimated 5 th 

# percentile, as shown in the final column of the above table. Therefor e in summary we propose:

 Penetration factor fo r outdoors, near road: 1.

 Proximity factor for out doors, near road: Normal di stribution. 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 dist ributions as for outdoors-near-road:

 Penetration factor for outdoor s, public garage / parking lot: 1.

 Proximity factor for outdoors, public garage / parking lot: Normal distribution. Mean = 0.755. Standard Devi ation = 0.203. Lower Bou nd = 0.422. Upper Bound = 1.

Outdoors, Other

The outdoors, other ozone concentrations s hould be well represented by the ambient monitors. Therefore we propose:

 Penetration factor for outdoors, other: 1.

 Proximity factor fo r 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 di stributed, the upper bound to be1, and the lower bound to be the estimated 5 th 

# 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-v ehicle, urban roads: Normal di stribution. Mean = 0.754. Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for in-vehicle, inters tates: Normal distribution. Mean = 0.364. Standard Deviation = 0.165. Lower Bound = 0.093. Upper Bound = 1.

To complete the specification, the distribut ion of road type needs to be estimated for each city to be modeled. Vehicle miles travel ed (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

1 

## http://www.fhwa.dot.gov/policyinformation/statistics/2008/pdf/hm71.pdf



# Thus to simulate the proximit y 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 def ined 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 stratificati ons by vehicle type, vehicle speed, road type, traffic (light, moderate, or heavy), and ventilation. The overall results and results by ventilation type are shown in Table 10.

Table 10. Ratio of inside-vehicle ozone to outside-vehicle ozone 1



# . Ventilation 1 

## Number of

cases 1 

## Mean 1 

## Standard Deviation 1 

## 25 th 

## Percentile 1 

## 50 th 

## Percentile 1 

## 75 th 

## Percentile 1 

## Estimated 5 th 

## Percentile 2 

## 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 0.000 3



## All 812 0.300 0.232 0.117 0.251 0.463 0.000 3



## 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 in dicate that the inside-to-out side 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 script ed, the ventilation conditions may not represent realworld vehicle ventilation scenarios. Theref ore, we propose to use the overall average distributions.

 Penetration factor for in-vehicle: No rmal distribution. Mean = 0.300. Standard Deviation = 0.232. Lower Bound = 0.000. Upper Bound = 1.

Mass Transit

The mass transit microenvironment is expe cted to be similar to the in-vehicle microenvironment. Therefore we reco mmend using the same APEX modeling approach:

 Proximity factor for mass transit, loca l 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 di stribution. Mean = 0.754. Standard Deviation = 0.243. Lower Bound = 0.355. Upper Bound = 1.

 Proximity factor for mass transit, inters tates: 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: No rmal 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. Co lome (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 C haracteristics 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 air borne 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 & El ectric Co., San Diego Gas & Electric Co., Southern California Gas Co. Jakobi, G and Fabian, P. (1997). Indoor/ outdoor concentratio ns 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 88 th 

# annual meeting and exposition of the Air & Wa ste 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 metropo litan Toronto area. J. Air Waste Manage. Assoc . 45: 223-234 Meng, Q. Y., B. J. Turpin, L. Korn, C. P. We isel, M. Morandi, S. Co lome, J. J. Zhang, T. Stock, D. Spektor, A. Winer, L. Zhang, J. H. Lee, R. Giov anetti, W. Cui, J. Kwon, S. Alimokhtari, D. Shendell, J. Jones, C. Farr ar, S. Maberti (2004). Influence of ambient (outdoor) sources on residential indoor and personal PM 2.5 

# concentrations: Analyses of RIOPA data. Journal of Exposure Analysis and Environ Epidemiology . Preprint. Murray, D. M. and D. E. Bu rmaster (1995). Residential Air Exchange Rates in the United States: Empirical and Estimated Pa rametric 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 Buildi ng Assessment Survey and Evaluation (BASE) Study. National Institute of Standards and Technology, NISTIR 7145, December 2004. Persily, A., J. Gorfain, G. Brunner.(2005). Ve ntilation 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 

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. 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. S heldon, C. Rodes, J. Thornburg (2003b). The Research Triangle Park particulate pa tter panel study: modeling ambient source contribution to personal and resi dential 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 Nit rogen Dioxide, Phase I, Final Report. Prepared for Southern California Gas Company, Los Angeles. Wilson, A. L., S. D. Colome, Y. Tian (1994). California Residentia l Indoor Air Quality Study, Volume 2, Carbon Monoxide and Air Exchange Rate: A Univariate & Multivariate Analysis. Report prepared for the Gas Resear ch Institute, Pacific Gas & Electric Co., San Diego Gas & Electr ic 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 Analysi s and Environ Epidemiology . Vol. 6, No. 3.

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# 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 O 3 

# exposures at or above 60, 70, and 80 ppb-8hr while at moderate or greater exertion, years 2006-2010, base air quality. .......................................................................................................................... 5F-1 0 Table 5F-2. Percent of all school-age children with O 3 

# 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 a ll school-age children (and associated days per O 3 

# season) with at least one daily maximum 8-hr average O 3 

# 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 tw o daily maximum 8-hr average O 3 

# exposures in all study areas by year, base air quality and air quality adju sted 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 av erage O3 exposures at or above benchmark levels, all 15 ur ban 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 O 3 

# exposure at or above 60, 70, and 80 ppb wh ile 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 O 3 

# 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 w ith at least one daily maximum 8-hr average O 3 

# exposure at or above 60, 70, and s 80 ppb wh ile 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 O 3 

# exposure at or above 60, 70, and 80 ppb while at m oderate 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 O 3 

# exposures at or above 60 ppb per study area O 3 

# 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 O 3 

# exposures at or above 70 ppb per study area O 3 

# 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 wi th at least one daily maximum 8-hr average O 3 

# 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 O 3 

# 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 O 3 

# 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 w ith at least one daily maximum 8-hr average O 3 

# 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 O 3 

# 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 adju sted air quality. ............................................. 5F-19 Figure 5F-12. Incremental increases in percent of all school-age children w ith at least two daily maximum 8-hr average O 3 

# exposures at or abov e 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 w ith at least two daily maximum 8-hr average O 3 

# exposures at or abov e 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 adju sted air quality. ............................................. 5F-21

# 5F-1 This appendix contains the detailed results for the primary APEX simulations performed to estimate exposures associated with base ai r quality (section 5F-1) a nd for air quality just meeting the existing and alternativ e 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 childr en, asthmatic adults (age s 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 consid ering each benchmark level are presented on the same scale to allo w for direct comparisons across the multi-panel display. The most notable patterns in the e xposure results are descri bed here using one study group (i.e., school-age children), as there is a general consistency in the year-to-ye ar variability within each study area across all four study grou ps. Any deviation from the observed pattern will be discussed for the subsequent study group. Ta ble 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-a ge children experiencing at least one O 3 

# exposure at or above the select ed benchmark levels while at moderate or greater exertion. Consistent with the previously discussed obser vations regarding year-to-year variability in ambient concentrations (Chapter 4), most study ar eas have the greatest percent of school-age children experiencing con centrations at or abov e the three benchmark le vels during 2006 or 2007 along with having the lowest percent of sc hool-age children exposed during 2009. Three Western U.S. study areas, Dallas, Los Angeles, an d 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 occu r during year 2008. In general, between 20 to 40% of school-age children experience at least one daily maximum 8-hr average O 3 

# exposure at or above 60 ppb, 10 to 20% e xperience at least one O 3 

# exposure at or above 70 ppb, and 0 to 10% experience at least one O 3 

# 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 O 3 

# exposure at or above the se lected benchmark levels whil e at moderate or greater exertion (Figure 5F-2) is virtuall y indistinguishable from that of all school-age children (Figure 5F-1) regarding both the y ear-to-year pattern and pe rcent of persons expose d. This is the result of having both simulated study groups use an iden tical 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 expos ure for asthmatic adults is similar to that observed for school-age child ren, though the percent of asthmatic adults experiencing exposures at or above the health e ffect benchmark levels is lower by a factor of about three or more (Figure 5F-3). Having a lower percent of asthma tic adults exposed is expected given that outdoor time expenditu re is an important determinant of O 3 

# exposure (HREA section 5.3.2) and that adults spend less time outdoors than child ren (REA section 5.3.1). In general, between 5 to 10% of asthmatic adults experience at least one daily maximum 8-hr average O 3 

# exposure at or above 60 ppb, 0 to 5% e xperience at least one daily maximum 8-hr average O 3 

# exposure at or above 70 ppb, and 0 to 2% experience at least one daily maximum 8- hr average O 3 

# 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 (o r 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 O 3 

# 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 lowe r by a fewer percentage points when compared with the results for asthmatic adults. Again, ol der 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 modera te 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 O 3 

# exposure at or above 60 ppb-8hr, less than 5% experience at least one O 3 

# exposure at or above 70 ppb-8hr, and about 2% or less experience

at least one O 3 

# exposure at or above 80 ppb-8hr, all wh ile 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 gr oups, 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 ar eas, 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 benchm ark during the ozone season, while about 5 to 10% could experience at l east four. Most study areas and years are estimated to have fewer than 5% of school-age children experi ence six or more exposures above 60 ppb considering the base air quality. When considering the multi-day expo sures 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 se ason, while four or more exposur es were generally limited to fewer than 4% of school-age children.

# 5F-4Percent of All School-Age Children (5-18)with at Least One 8-hour Exposure At orAbove Benchmark Level While at Moderate or Greater Exertion, Base Air Quality



# Figure 5F-1. Percent of all school-age child ren with at least one daily maximum 8-hr average O 3 

# 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-5Percent of Asthmatic School-Age Children (5-18) with at Least One 8-hour ExposureAt 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 O 3 

# exposure at or above 60, 70, and 80 ppb while at moderate or greater exertion, years 2006-20 10, base air quality. Exposure Benchmark (ppb)

# 5F-6Percent of Asthmatic Adults (19-95) with at Least One 8-hour Exposure At or AboveBenchmark 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 O 3 

# exposure at or above 60, 70, and s 80 ppb while at moderate or grea ter exertion, years 2006-2010, base air quality. Exposure Benchmark (ppb)

# 5F-7Percent of Older Adults (65-95) with atLeast One 8-hour Exposure At or AboveBenchmark 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 O 3 

# 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 O 3 

# exposures at or above 60 ppb per study area O 3 

# season, while at moderate or greater exertion, years 2006-20 10, base air quality. Number of Exceedances Per O 3 

### Season



# ≥ 2

≥ 4

≥ 6

# 5F-9 Figure 5F-6. Percent of all school-age children with multiple daily maximum 8-hr average O 3 

# exposures at or above 70 ppb per study area O 3 

# season, while at moderate or greater exertion, years 2006-20 10, base air quality. Number of Exceedances Per O 3 

### Season



# ≥ 2

≥ 4

≥ 6

# 5F-10 Table 5F-1. Percent of all school-age children with O 3 

# 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 O 3 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 O 3 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 O 3 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 O 3 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 O 3 

# NAAQS standard, as well as when considering potential alternative standard levels ( 55, 60, 65, 70 ppb 8-hr) of the existi ng standard. We note that one study area (Chicago) O 3 

# ambient monitor design values were be low 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 st andard level of 60 ppb-8hr or below in the New York study area, thus APEX simulations for thes e air quality scenarios could not be performed for the New York study area. First are presented three-pane led 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 benc hmark). 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 additiona lly characterized by calculating the mean percent (averaged over the st udy 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 (Fi gure 5F-11). Further, the ma ximum (Figure 5F-12) and mean (Figure 5F-13) percent of all sc hool-age children experiencing at least two exposures at or above

the three health effect benchm ark levels are presented. Following these figures, Table 5F-2 provides the complete exposure output for all stu dy areas, years, benchmark levels, and adjusted air quality scenarios for all sc hool-age children, the study group c ontaining the greatest percent and number of persons e xposed in the HREA. And finally, the mean and ma ximum number of all school-a ge children and associated person days with at least one e xposure 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 a ll 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 childre n and asthmatic school-age children experiencing at least one or two exposure s above the selected benchmar k levels, summed for all 15 study areas, and representing all simu lated 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 O 3 

# exposure at or above 60 ppb (top panel), 70 ppb (middle panel), or 80 ppb (bottom panel) using the ma ximum 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 O 3 

# exposure at or above 60 ppb (top panel), 70 ppb (middle panel), or 80 ppb (bottom panel) us ing 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 O 3 

# 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 O 3 

# 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 O 3 

# exposure at or above 60 ppb (top panel), 70 ppb (middle panel), or 80 ppb (bottom panel) using the me an 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 O 3 

# exposures at or above 60 ppb (top panel), 70 ppb (middle panel), or 80 ppb (bottom panel) using the ma ximum 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 O 3 

# exposures at or above 60 ppb (top panel), 70 ppb (middle panel), or 80 ppb (bottom panel) using the me an percent exposed for each study area, year 2006-2010 adjusted air quality.

# 5F-22 Table 5F-2. Percent of all school-age children with O 3 

# exposures at or above 60, 70, and 80 ppb-8hr while at moderate or greater ex ertion, years 2006-2010, adjusted air quality.

Study Area Exposure Benchmark

(ppb-8hr) Air Quality Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 Scenario 1 Year % of all school-age children experiencing multiple exposures per O 3 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 (a nd associated days per O 3 

# season) with at least one daily maximum 8-hr average O 3 

# 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 O 3 

# exposures in all study areas by year, base air quality and air quality adjusted to just meet ing 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 O 3 

## Season 1



## 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 acro ss 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 O 3 

## Season 1



## 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 sch ool-age children and asthmatic school-age children with at least one or two daily maxi mum 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 6,890,00 0 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 perio ds) 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 CH AD Survey Participants .................... 5G-4

5G-1.3 Afternoon Time Spent Outdoors For AT US 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 IN FLUENCING HIGH EXPOSURES . 5G-14

5G-3 ANALYSIS OF APEX SIMULATED LONGITUDINAL ACTIVITY PATTERNS

..................................................................................................5G-23

5G-4 EXPOSURE RESULTS FOR ADDITI ONAL 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 Imp act 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 pa rticipants within CHAD and used by APEX. . 5G-3 Table 5G-2. Comparison of outdoor time expenditure and ex ertion level among asthmatic and non-asthmatic diary days for CHAD diaries used by APEX. ......................... 5G-11 Table 5G-3. Percent of waking hours spent out doors 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 outdo ors at an elevated activ ity level: a comparison of CHAD with EPRI (1992) study asthmatics. ............................................... 5G-13 Table 5G-5. Percent of waking hours spent outdo ors at an elevated activ ity level: a comparison of CHAD with EPRI (1988) study asthmatics. ............................................... 5G-13 Table 5G-6. Range of R 2 

# fit statistics for ANOVA models used to evaluate daily maximum 8- hour O 3 

# exposure concentrations stratified by study area, air quality scenario, and exposure level. ................................................................................................ 5G-16 Table 5G-7. Range of R 2 

# fit statistics for ANOVA models used to evaluate daily maximum 8- hour O 3 

# exposure concentrations in Los Angeles stratified by age group, air quality scenario, and ex posure level. .............................................................. 5G-16 Table 5G-8. Distribution of days per week spent performi ng outdoor work considering the BLS/O*NET data set and stratified by APEX/CHAD occupation groups. .... 5G-33 Table 5G-9. Personal attri butes and mean time spent work ing outdoors for CHAD diaries reporting at least two hour s of outdoor work. ................................................. 5G-34 Table 5G-10. Distribution of days per week spent performing outdoor work considering the APEX simulated population and stratifi ed by APEX/CHAD occupation and age groups. ............................................................................................................. 5G-38



# List of Figures

Figure 5G-1. Participation ra te in outdoor activities (top) and mean time spent outdoors (bottom) for CHAD diaries having at l east one minute outdoors (left) and CHAD diaries having at least two hours outdoor s (right) during the afternoon. .......... 5G-5 Figure 5G-2. Participation ra te in outdoor activities (top) and mean time spent outdoors (bottom) for ATUS diaries having at l east one minute outdoors (left) and ATUS diaries having at least two hours outdoors (right). ............................................ 5G-8 Figure 5G-3. Contribution of influential f actors to daily maximum 8-hour ozone exposures using base air quality (left), air quality adjusted to just meet the existing standard (right), considering all pers on 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 f actors to daily maximum 8-hour ozone exposures using base air quality (left), air quality adjusted to just meet the existing standard (right), considering all pers on days (top) and those days where daily maximum 8- hour exposure exceeded 50 ppb (bot tom) in Atlanta. ..................................... 5G-19 Figure 5G-5. Distributions of afternoon out door time expenditure and daily maximum 8-hour ambient O 3 

# concentrations for simulated Boston school-age children (ages 5 to

# 5G-iii 18) (left) and adults (ages 19 to 35) (r ight) using base air quality (top) and concentrations adjusted to just meet the existing standard (bottom) for person days having daily maximum 8-hour expos ures 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 O 3 

# exposures ≥ 50 ppb, Boston base air quality, 2006. .................................................................................................. 5G-23 Figure 5G-7. Cumulative dist ribution of median time spen t outdoors (top row), afternoon outdoor participation ≥ 1 minute/day (2 nd 

# row), and afternoon outdoor participation ≥ 2 hours/day (3 nd 

# row) for male and fema le 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 (4 th 

# row) and the number of particular CHAD study diary days used (bottom row) for each exposure simulation day. .......................................................... 5G-27 Figure 5G-8. Cumulative dist ribution of median time spen t outdoors (top row), afternoon outdoor participation ≥ 1 minute/day (2 nd 

# row), and afternoon outdoor participation ≥ 2 hours/day (3 nd 

# row) for male and fema le 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 (4 th 

# 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 a ll school-age children having daily maximum O 3 

# concentration at or above 60 ppb-8hr (t op), 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 ag e 19-35 (left) and 36- 55 (right) experiencing exposures at or above selected benchmar k levels while at moderate or greater exertion using an outdoor worker scenar io-based approach (top) and a general population-based approach (bottom) consider ing air quality adjust ed to just meet the existing standard in Atla nta, GA, Jun-Aug, 2006. .................................... 5G-39 Figure 5G-11. Distribution of daily personal O 3 

# exposures (top row), outdoor time (2 nd 

# row from top), ambient O 3 

# concentrations (3 rd 

# row from top), and air exchange rate (bottom row) for DEARS study participants (left column) and APEX simulated individuals (right column) in Wa yne County, MI, July-August 2006. ........... 5G-46

# 5G-1 This Appendix presents the complete results of several targeted eval uations and exposure simulations designed to provide ad ditional insights to APEX input data or approaches used to estimate exposures, algorithm and model performa nce evaluations, and estimated exposures for additional exposure study groups and lifestages of interest.

5G-1 ANALYSIS OF TIME-LO CATON-ACTIVITY DATA We first present an overview of the data cu rrently available in the CHAD database used by APEX, including comparison with the version of CHAD used to estimate exposures in the 1 st



# draft O 3 

# HREA. This is followed by an analysis of time spent outdoors - one of the most important attributes influencing exposures at or above benchmark le vels - using CHAD and recent time-location-activity patte rn 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 Partic ipants in CHAD and Used by APEX The survey participants whose diary data are within CHAD were asked a number of questions regarding their persona l 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 simu late individuals using se veral personal attributes, namely age, sex, temperature (as a surrogate for seasonal variation in ac tivity patterns), and dayof-week. These attributes are c onsidered 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 (p ersons between the ages of 5 and 95), the actual number of diary days having complete information and used by APEX in the final O 3 

# HREA is 41,474. 1 

# This represents an increase of about 8,700 diarie s currently used by APEX compared with what was used by APEX in the 1 st 

# draft O 3 

# HREA. Additionally, there ha ve been eight new study data sets incorporated into CHAD and used in our current exposure assessment since the previous O 3 

# NAAQS review conducted in 2007, most of which we re 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 evaluatio n 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. Accommod ations 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 O 3 

# exposure modeling and has

increased the number of children’s di aries by about a factor of five. Table 5G-1 presents a summary of the impor tant personal attribut es used by APEX in creating activity patterns for simulated persons, al ong with other attributes of potential interest (e.g., race/ethnicity). First, we compared the repr esentation of several attr ibutes in the current CHAD used by APEX versus that used in the 1 st 

# draft O 3 

# HREA. Outside of increases in the number of persons, the general distribution of diar ies within the APEX diary selection attributes (e.g., age, sex, temperature, day-of-week) is sim ilar 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 surv eys conducted in the past decade, while the pre1990s represent less than 15% of th e total diaries available by APEX. While there may be other personal or situ ational attributes that affect daily time expenditure, these are typically not included in ou r 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 thei r occupation (Table 5G-1). Missing response data in CHAD results from either the study not havi ng an income/occupation related survey question or perhaps the participant refused to answer the question. Note al so, 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 artific ially reducing variability in tim e expenditure. In addition, the desired study group to be simulate d may have too few diaries within a diary pool if most diaries are missing the needed attribute, leading to a simu lation 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 in fluence 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 regardi ng the representativeness of the diaries from

studies conducted in the 1980s and whether th ere are any recognizable patterns in time expenditure in the CHAD diaries across the time pe riod when data were collected. Because time spent outdoors is a significant factor influencing daily maximum 8-hour O 3 

# 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 O 3 

# concentrations commonly occur during the afternoon hours of summer months, we restricted th e analysis to those times of day (12 PM to 8 PM) and year (May through Se ptember). 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 ha ving at least one minute outdoors and person-days having at least 2-hours outdoors. Se parating the data into these sub- groups give us insight to the diaries most likely to be used in simulating a pe rson that exceeds a select ed benchmark level and protects (to a limited degree) from study sample de sign 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 conducte d. As a reminder, CHAD is composed of primarily crosssectional data, thus the trend eval uated over the three deca des is changes (if a ny) in participation rate and the time spent outdoors by the study group, not individuals. Figure 5G-1 illustrates the trends in afte rnoon outdoor activity participation and mean time expended outdoors, considering three decad es, five age groups, and whether the total afternoon time spent outdoors was at least one minut e or two hours. Regardless of decade and duration of time spent outdoors, participation in outdoor activities follow s 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 (age d 65 or greater) tend to have the lowest participation rate (F igure 5G-1, top left panel). Wh en considering decade and CHAD diaries having at least one minut e spent outdoors, the pa rticipation rate ap pears to have a nonlinear concave trend, whereas CHAD diaries co llected during the 1990s exhibit the lowest outdoor participation rate (rangi ng 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 2000sAfternoonOutdoorParticipationRate(%)

Decade CHAD, ≥ 1

‐ mins outdoor time, months May

‐ Sep, hours 12

‐ 8PM

0 50 100 150 200 250 300

1980s 1990s 2000sMeanAfternoonOutdoorTime(minutes/day)

Decade CHAD, ≥ 1

‐ min outdoor time, months May

‐ Sep, hours 12

‐ 8PM 0 10 20 30 40 50 60 70 80 90 100

1980s 1990s 2000sAfternoonOutdoorParticipationRate(%)

Decade CHAD, ≥ 2

‐ hrs outdoor time, months May

‐ Sep, hours 12

‐ 8PM

0 50 100 150 200 250 300

1980s 1990s 2000sMeanAfternoonOutdoorTime(minutes/day)

Decade CHAD, ≥ 2

‐ hrs outdoor time, months May

‐ Sep, hours 12

‐ 8PM



# 0 100

1980s 1990s 2000sAfternoo... 

# CHAD, ≥ 1

‐ mins outdoor time, months May

‐ Sep, hours 12

‐ 8PM ages 4 to 18 ages 19 to 34 ages 35 to 50 ages 51 to 64 ages 65+

Figure 5G-1. Participation rate in outdoor ac tivities (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 outd oors (right) during the afternoon.

It is possible that this obs erved 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 we re reported on a minute or longer basis and many of those same diaries also used a ‘contemporan eous diary’ approach, th at is, real-time data reporting. The CHAD diaries coll ected 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 di aries exhibit the lowest outdoor participation rate due to the participant missi ng or ignoring short duration events (< 15 minutes) that may have occurred outdoors (e.g., out doors in a parking lot and walking to their vehicle). The CHAD diaries coll ected during the 2000s, while also a combination of studies that

used the event level and 15-minute time block a pproach did have a few studies using the real-

# 5G-6 time diary approach, possibly res ponsible for the observed increase in outdoor participation rate during this decade. When restricting the data set to person da ys having at least two hours of afternoon time spent outdoors, the above mentioned temporal pa ttern nearly disappears (Figure 5G-1, top right panel) as most age groups exhibit little to no va riability in outdoor time participation rate across the three decades (~20-30% of person-days). Ho wever, the diaries from the 1980s for children ages 4-18 indicate the highest outdo or participation rate (i.e., 50%) compared to all other age groups and decade of collection. Most of thes e diaries (i.e., 96%) ar e from the California Children’s Study and Cincinnati Activity Pattern s Study. To date it remains unexplained why

these two studies would have th is unusual outdoor participati on rate compared to the other studies and decades and could simply be a functi on of the two study selecti ng 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 incl ude 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 participatio n in outdoor activities and the representativeness of the CHAD stu dy data from the 1980s, it is unl ikely that use of these older diaries would adversely influe nce exposure model estimates. When considering the mean time spen t outdoors during the afternoon hours for diaries having at least one minute recorded out doors, the observed pattern is generally the inverse of participation. This pattern would be consistent wi th the above proposed reasoning: relatively more persons reporti ng shorter duration ev ents leads to higher overall outdoor participation rate coinciding with a decrease in the mean time spen t outdoors (Figure 5G-1, bottom left panel). In general, wh en restricting the data set to pe rson-days that recorded at least two hours outdoors during the aftern oon (Figure 5G-1, bottom right pane l), there is variability in

the amount of outdoor time over the three decad es, with diaries from the 2000s exhibiting perhaps the lowest range of mean outdoor tim e (190-220 min/day) compared with the 1980s (210-240 min/day) and 1990s (212- 258 min/day), a trend perhap s most notable trend when considering the children’s diarie s (a decrease in time spent out door 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 influentia l factors have not been m easured and the observed phenomenon can only be identified as study related.

# 5G-7 period). However, the coefficient of variati on (COV) for each of the age groups and across all

decades for the cross-sectional data was consis tently about 40% (data not shown), supporting a general conclusion of no large diffe rences 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 represen tativeness of the CHAD study data from the 1980s, inclusion of these earlier diaries is also un likely 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 recen t and ongoing data collection efforts, (2) large sample size (>120,000 diary days), (3) national repr esentativeness, and (4) that varying diary approaches would not be an influential or c onfounding factor in evalua ting trends over time. ATUS does however have a few noteworthy li mitations when compared with the CHAD data: (1) there are no survey part icipants under 15 years of age, (2) time spent at home locations is neither distinguished as indoors or outdoors, and (3) missi ng or unknown location data can comprise a significant portion of a persons’ da y (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 out doors (e.g., participation in a sport) to better approximate each ATUS individu al’s outdoor time expenditure . Missing several hours of location and activity information can be problema tic when modeling exposures, an issue that renders the ATUS diaries generally unusable by APEX. However, th e 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 hour s of interest here (i.e., 12 PM-8 PM), with most missing time occurring betw een 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 af ternoon time while also only re taining diaries from ATUS identified metropolitan areas. Da ta 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 2011MeanAfternoonOutdoorTime(minutes/day)

Year ATUS, ≥ 1

‐ min outdoor time, 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 2011AfternoonOutdoorTimeParticipationRate(%)

Year ATUS, ≥ 1

‐ min outdoor time, 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 2011AfternoonOutdoorTimeParticipationRate(%)

Year ATUS, ≥ 2

‐ hrs outdoor time, months May

‐ Sep, hours 12

‐ 8PM

0 50 100 150 200 250 300

2003 2004 2005 2006 2007 2008 2009 2010 2011MeanAfternoonOutdoorTime(minutes/day)

Year ATUS, ≥ 2

‐ hrs outdoor time, months May

‐ Sep, hours 12

‐ 8PM



# 50M



# 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 ac tivities (top) and mean time spent outdoors (bottom) for ATUS diaries having at leas t one minute outdoors (left) and ATUS diaries having at least two hours outdoors (right).

# 5G-9 Not surprisingly given the lack of distin ction 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 lo wer than that of CHAD study subjects; about 30- 40% of ATUS person-days have at least one minute of outdoor time (F igure 5G-2, top left panel). As was observed with the CHAD data, child ren (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 indica ted 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 AT US diary outdoor partic ipation rate (10-20%, Figure 5G-2, top right pane l)) is lower than that observed fo r 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 th at of the CHAD data. Consistent also across the two studies is the participation rate of childre n being greater than that of th e other age groups. There are no consistent trends over the nine ye ar ATUS study period re garding either the pa rticipation rate or the mean time spent outdoors for any of the age groups.

5G-1.4 Outdoor Time and Exertion Level of Asth matics 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), pr ovided they concur with age, sex, temperature, and day-ofweek selection criteria. In general, the assu mption 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 analys is of 5-days of monitoring, van Gent et al. (2007) showed no difference in the activity data co llection 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 stat istically significant difference between the percent of current asthmatics (30.9%) and non-asthmatics (27.8%) characterized as inactive. In addition, these re searchers 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., ei ther moderate or grea ter 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 physic al activity level is strongly linked with their time spent outdoors and is considered an important correl ate 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 f itness in asthmatic child ren, with statistically significant increases in ventilation measures such as maximum minute ventilation rate (VE max 

# ) maximum oxygen uptake (VO 2max 

# ) (e.g., van Vledhoven et al., 2001). Further, in other related research, Santuz et al. (1997) indicated no statistically signifi cant difference between asthmatic and non-asthmatic children when comparing ma ximum exercise performance levels, provided the individuals were conditioned through habitual exercise. Thus it appears that asthmatics perform activities at elevated le vels and do so in outdoor microenvi ronments 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 gr oups regardless of th e 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 in dividual was asthmatic or non-asthmatic, we categorized the data and results using thr ee 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 thos e persons that did spend some time outdoors were retained. As is done by APEX in simulatin g individuals, level of exertion was estimated by sampling from the specific METS distributions as signed 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 particip ation rate (48.5%) when compar ed to non-asthmatic children (41.2%). About half of the adults whose as thmatic condition was known did spend afternoon time spent outdoors with partic ipation rate generally sim ilar for both asthmatic and nonasthmatic adults. Outdoor part icipation rate for persons havi ng unknown asthma status varied

# 5G-11 from that of known persons; about 60% of the ch ildren’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 fo r 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 ques tionnaire 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 ques tionnaire 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 person s that did so varied little across the two study groups and three asthma categories. On average, diaries fr om 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. S lightly less afternoon time is spent outdoors by adults (about 125-130 minutes) when comp ared with children whose asthma status is known, though more notable is th e lowered percent of afternoon time adults perform moderate or greater exertion level activ ities (about 63%). As noted above regardin g the reduced participation rate for adults whose asthma stat us 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 respectiv e cohorts from three independent asthma activity pattern stud ies 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 al l 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 Ta ble 5G-5 summarize the data reported from the three asthma activity pattern st udies 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 ou tdoors at an elevated activity level. A comparison of CHAD with Shamoo et al. (1994) study asthmatics.
Study Shamoo (1994) 1 

## CHAD 2



## 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 studie s, 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 ME TS values from each activity-specific distribution were assigned to each person’s activities. Mo derate and vigorous activity levels we re selected based on activities having a METS value of 3 to <6 and ≥ 6, respectively.



# When considering the three independent as thma studies, the amount of time spent outdoors at moderate activity leve l ranges from a low of approxima tely 2% to a high of about 11% of waking hours. The estimates of outdoor ti me associated with moderate activity level using a similarly constructed coho rt 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 co mpared 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 stud ies (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 ofCHAD diary data from either asthmatics, nonasthmatics, or unclassified asthmatics that coul d contribute to variati on in the time spent outdoors at elevated activity le vels. This would include: the diary/survey collection methods used, the classification of activities performed an d associated activity le vels, the number of study subjects, and sample selection methods. The particulars regarding how each of these were

addressed across the various studies is wide ra nging 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 justifie d 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 EP RI (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 activitie s per hour. Random digit dialing and multiplicity sampling were used. Three consecutive diary days were collect ed 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 EP RI (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 ha ving varying time intervals, th at is, the daily, hourly, and minute-by-minute (or events ) files. Given that the size of th ese 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 O 3 

# adjusted to just meet th e existing standard (75 ppb8hr) air quality scenarios were evaluated in e ach of six study areas. All APEX conditions (e.g., ME descriptions, AERs, MET data) were cons istent with the 200,000 person APEX simulations that generated all of summary output discus sed in the main body of this chapter. We were interested in identifying the specific microenvironments and activities most important to O 3 

# exposure and evaluating their duration a nd particular times of the day people were engaged in them. Because ambient O 3 

# concentrations peak ma inly during the afternoon hours, we focused our microenvironmental tim e 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 insi de vehicles during thes e 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 pers on 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 O 3 

# exposure concentrati ons. 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 models 6 

# included a total of seven explanatory va riables: the main effects of (1) daily

4 

## For the 1 st 

## draft O 3 

## 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 O 3 

## HREA was the air quality data input to APEX: ambient monitoring data were used for the 1 st 

## draft O 3 

## 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 O 3 

## concentrations and it is possible that fewer

persons simulated could result in differences in exposure s 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 expo sures 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 O 3 

# , (2 to 4) afternoon time spent outdoors, near-roads, and inside vehicles, 7 

# and (5) physical activity inde x (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 cons idered: 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 para meter estimates for each variable, model Rsquare statistic (R 2 

# ), and Type III model sums of squares (SS3). 9 

# Model fits, as indicated by an R 2 

# value, were reasonable across each of the study areas (Table 5G-6). The selected factors explain abou t 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 scenario s. When considering only those days where persons had 8-hour daily maximum O 3 

# 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. Furthermor e, 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 R 2 

# results stratified by age groups).

We evaluated the relative contribution each va riable had on the total explained variability using the SS3 in each respective model. 10 

# As with the R 2 

# 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 O 3 

# concentration vari able contributes the greatest to the explained model variance, co nsistently estimated to be about 85% across all age groups and for either the base or existing stan dard air quality scenario s. The interaction of

this variable with af ternoon outdoor time contributes an additional 7-10% to the explained variance, indicating that both ambient concen tration and time spent outdoors collectively contribute to 90% or more of the explained model variance when eval uating all (e.g., high, midrange, and low level) daily maximum 8-hour O 3 

# 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 re duced 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 tota l effect sums of squares (or the total model explained variance).

# 5G-16 contribution). These results suggest that wh en considering the Bost on exposure study groups broadly, the daily maximum 8-hour ambient O 3 

# concentration is the most important driver in estimating population-based O 3 

# exposures, nearly regardless of specific microenvironmental locations where exposure might occur.

Table 5G-6. Range of R 2 

# fit statistics for ANOVA models used to evaluate daily maximum 8-hour O 3 

# exposure concentrations stratified by study area, air quality scenario, and exposure level.

Study Area (O 3 

## season) Base Case Model R 2 

## (sample sizes) Existing Standard Model R 2 

## (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 R 2 

# fit statistics for ANOVA models used to evaluate daily maximum 8-hour O 3 

# exposure concentrations in Los Angeles stratified by age group, air quality scenario, and exposure level.

Study Area Age Group (years) Base Case Model R 2 

## Existing Standard Model R 2



## 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 da ys having daily maximum 8-hour O 3 

# exposures ≥ 50 ppb and for either air quality scenario in Bo ston, collectively the main effects of ambient

concentrations and outdoor time combined with their interaction similarly contribute to approximately 85% of the total explained varian ce (Figure 5G-3, bottom row). However, the main effect of the 8-hour daily maximum ambient O 3 

# 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 resu lts suggest that for highly expo sed persons in Boston, the most important influential factors are time spent out doors corresponding with high daily maximum 8-

hour ambient O 3 

# 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 O 3 

# concentration variable to the total explaine d 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 O 3 

# interaction variable was greater in Atlanta (about 20-40% versus 10% in Boston). This observed dissimilarity in the contribu tion by ambient concentrations and afternoon outdoor time may be driven by the A/C prevalen ce rates and AER distributions used for each study area. 12 

# Boston has lower A/C prevalence th ough overall higher AERs (even when considering mechanical ventilati on), 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 O 3 

# concentration variable in Boston. Afternoon time spent near Atlanta roads was estimat ed to contribute to a bout 20-30% of the total explained variance when considering all person da ys and exposures, a va lue greater than that estimated for Boston (generally about 5%) again possibly reflecting an in creased importance of this outdoor microenvironment in Atlanta (and Hous ton, Sacramento, not shown) relative to that in Boston (and Philadelphia, not shown).

11 

## The discussion regarding the relative contribution of th e 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, an d 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 VarianceAgeGroup Boston, Base Air Quality, All Person Days

0% 25% 50% 75% 100% 5 to 18 19 to 35 36 to 64

≥ 65

Model Explained VarianceAgeGroup 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 VarianceAgeGroup 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 VarianceAgeGroup Boston, Existing 75 ppb Standard, All Person Days



## 0% 25% 50% 75% 100% < 5 36 to 65

Model Ex p lained VarianceAgeGroup 

# Atlanta, Current Standard,

... Max 8

‐ hr Ambient O3 Time Outdoors 12

‐ 8PM

Max 8

‐ hr Ambient O3*Time Outdoors 12

‐ 8PM Physical Activity Index (PAI)

Time Outdoors 12

‐ 8PM*PAI Time Near Roads 12

‐ 8PM

Time In

‐ Vehicles 12

‐ 8PM 

# Figure 5G-3. Contribution of influential fact ors 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 thos e 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 VarianceAgeGroup Atlanta, Base Air Quality, All Person Days

0% 25% 50% 75% 100% 5 to 18 19 to 35 36 to 64

≥ 65

Model Explained VarianceAgeGroup 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 VarianceAgeGroup 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 VarianceAgeGroup Atlanta, Existing 75 ppb Standard,
Person Days w/8

‐ hr Exposures ≥ 50 ppb



## 0% 25% 50% 75% 100% < 5 36 to 65

Model Ex p lained VarianceAgeGroup 

# Atlanta, Current Standard,

... Max 8

‐ hr Ambient O3 Time Outdoors 12

‐ 8PM

Max 8

‐ hr Ambient O3*Time Outdoors 12

‐ 8PM Physical Activity Index (PAI)

Time Outdoors 12

‐ 8PM*PAI Time Near Roads 12

‐ 8PM

Time In

‐ Vehicles 12

‐ 8PM 

# Figure 5G-4. Contribution of influential fact ors 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 thos e days where daily maximum 8-hour exposure exceeded 50 ppb (bottom) in Atlanta.

Because afternoon outdoor time expenditure and daily maximum 8-hour ambient O 3 

# concentrations are an impor tant determinant for high O 3 

# exposures regardless of air quality scenario considered, we compared the distributio ns of these two variables using person days where daily maximum 8-hour O 3 

# exposures were either belo w or above 50 ppb. Figure 5G-5 presents this comparison for Boston 13 

# 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 O 3 

# 8- hour standard. For school-age children that did not experi ence a daily maximum 8-hour

13 

## The overall features of the outdoor time and ambient co ncentration distributions illustrated by simulated persons

in Boston are similar to each of th e 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 u nder 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 (pl ease note, ambient is not exposure). Not surprisingly, the distributions for both th e outdoor time and ambient concentration variables are shifted to the righ t 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 O 3 

# 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 majo rity of ambient concentrations fall well below the existing standard level of 75 ppb. Given so few occurrences of very high 8-hour ambient O 3 

# 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 O 3 

# exposure concentrations (Figure 5G-5, bottom left pa nel, right-most solid line). For additional completeness, we note the time and concentratio n distributions for adu lt 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 480Percentile Daily Maximum 8

‐ Hour Ambient Ozone (ppb)Percentile

Total Afternoon Time Spent Outdoors (minutes) Boston, Children 5

‐ 18 Years Old, 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 480Percentile Daily Maximum 8

‐ Hour Ambient Ozone (ppb)Percentile

Total Afternoon Time Spent Outdoors (minutes) Boston, Children 5

‐ 18 Years Old, 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 480Percentile Daily Maximum 8

‐ Hour Ambient Ozone (ppb)Percentile

Total Afternoon Time Spent Outdoors (minutes) Boston, Adults 19

‐ 35 Years Old, 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 480Percentile Daily Maximum 8

‐ Hour Ambient Ozone (ppb)Percentile

Total Afternoon Time Spent Outdoors (minutes) Boston, Adults 19

‐ 35 Years Old, 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 ou tdoor time expenditure and daily maximum 8- hour ambient O 3 

# concentrations for simulated Boston school-age children (ages 5 to 18) (left) and adults (ages 19 to 35) (right) usin g base air quality (top) and concentrations adjusted to just meet the existing standa rd (bottom) for person days having daily maximum 8-hour exposures ei ther 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. Understanda bly based on the above analyses, the outdoor microenvironment is most important fo r persons experiencing the highest O 3 

# exposures, but we are also interested in the pe rcentage 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, consider ing 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-a ge children (Figure 5G- 6, top left panel) and adults (Figure 5G-6, top right panel) for persons experiencing daily maximum 8-hour average O 3



# exposures ≥ 50 ppb and considering base air quality c onditions. On average, approximately 50%

# 5G-22 of school-age children’s to tal afternoon time is spen t outdoors, of which half of this portion is spent outdoors at home, with parks and other non-residential outdoor lo cations comprising the remaining portion. Approximately 45% of th e school-age children’s afternoon time on high exposure days is spent indoors, wh ile just less than 10% of afte rnoon time is spent near-roads or

inside motor vehicles. Afternoon microenvir onmental time expenditure for highly exposed adults (ages 19-35) in Boston wa s 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 ev aluated the activities performed by highly exposed individuals while they spent ti me 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 di aries that are select ed to represent the simulated study group. We summed the time spen t in each specific ac tivity across all highly exposed persons when spending afternoon time outdoor s, ranked the activity sums, and identified the top eleven activities performed. An aggr egate 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-a ge children (bottom le ft panel), indicating that greater than half of the time highly e xposed children spent outdoors specifically involves performing a moderate or greater exertion level activity, such as a sporting ac tivity. The same type of analysis was done for highly exposed adu lts 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 necessa rily a high exertion level activity) , about 15% of the time was spent playing sports or other moderate or greater ex ertion level activity, with much of the remaining specific activities associated w ith low exertion level (e.g., eating, sitting, visiting) or other less frequently performed activities of variable exertion level. Th ese results support our identification of school-age children as an im portant exposure group, la rgely a result of the combined outdoor time expenditure along with concomitantly performing moderate or high

exertion level activities. It is worth noting, one important gr oup not directly assessed in the general population-based exposur e modeling and remaining as a limitation to the main body HREA results is outdoor workers. This expos ure study group was explicitly modeled using a scenario based approach and su mmarized in section 5G-4.2.

# 5G-23 Indoor (Residence) 33%

Indoor (Other indoor

‐ 9 MEs) 7%

Indoor (School) 5%

Near

‐ road (Within 10

yards of street) 2% Near

‐ road Other (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% Afternoon Time Expenditure: Children 5

‐ 18, 8

‐ hr Exposures ≥ 50 ppb, Boston, Base Air Quality

Indoor (Residence) 25%

Indoor (Other indoor

‐ 11 MEs) 11% Indoor (Office building, Bank, Post office) 3%

Near

‐ road (Within 10

yards of street) 2% Near

‐ road Other (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% Afternoon Time 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% Other sports and active leisure 5% Play games

5% Participate in passive leisure 4% Exercise/Walk 4% Unspecified Work 3% Play outdoors 2% Eat 2% Travel, All

2%

Other (67 activities) 14% Afternoon Outdoor Activities: Children 5

‐ 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% Other repairs 2% Other (80 activities) 27% Afternoon Outdoor Activities: Adults 19

‐ 35, 8

‐ hr Exposures ≥ 50 ppb, Boston, Base Air Quality



# Figure 5G-6. Afternoon microenvironmental ti me (top) and activities performed during afternoon time outdoors (bottom) for school -age children (left) and adults (right) experiencing 8-hour daily maximum O 3 

# exposures ≥ 50 ppb, Boston base air quality, 2006.

5G-3 ANALYSIS OF APEX SIMULATED LO NGITUDINAL 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 pa rticipation rate and the amount of time expended were represented in our populati on-based exposure simulations. Our goal in developing the most reasonable longitudinal profiles is to capture ex pected, important featur es 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 vari ability across the modeled study group in day-today activity patterns (not every simulated indi vidual in the study group does the same thing on the same day). As a reminder, the longitudi nal approach is probabi listic, 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, consider s 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 outdoor s 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 st udy 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 O 3 

# 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 (50 th 

# percentile) of afternoon time spent outdoors was determined for each simulated individual using all days in their study area’s O 3 

# 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 O 3 

# 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 da y. Then for each simulated individual, outdoor participation was determined by summ ing this variable across the simulation period (i.e., the number of days per O 3 

# 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 O 3 

# season. This data set, comprised of individual par ticipation values (provided as a percent) was ra nked and plotted, stratified by sex. 3) Cumulative distribution summarizing each child’s afternoon outdoor participation (at least tw o hours/day) across an O 3 

# 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 simu lated individual spent at least two hours outdoor during the afternoon hours on that given day. 4) Daily time series of afternoon outdoor pa rticipation (at least two hours/day) by study group across O 3 

# season: Using the categorical variable determined in #3, we calculated the study group’s out door participation for every day of each study area’s

O 3 

# season. Data similar in fashion to our earlier analyses of out door 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 O 3 

## season varies across the six study areas. See Table 5G-6.

# 5G-25 outdoor participation for the simulate d study group across each study area’s O 3



# season. 5) Daily time series of the number of st udy-specific CHAD diaries used across O 3



# season: The APEX daily file output can incl ude the identity of the specific CHAD diary used to simulate every individual’s daily activity. For ev ery day simulated, we summed the number of CHAD diaries used to model the school age children’s activity patterns for each da y, though stratified by CHAD study identifier (e.g., see HREA Appendix 5B, Table 5B-1). Plotted is the day-to-day variab ility in particular CHAD study diaries used across each study area’s O 3 

# season.

The results of this longitudinal activity patt ern analysis in given in Figure 5G-7 and Figure 5G-8. To begin, a few generalities rega rding the features of each plot and where consistency is exhibited across study areas. In general, simu lated female school-age children tend to spend less af ternoon time outdoors than thei r male counterparts, cons istent 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 stud y area’s ozone season, while about 10-20% spent just over 2 hours afternoon time spent outdoors for half of thei r days (top row of Figure 5G-7 and Figure 5G-8). Nearly every simulated individual participates in at least one aftern oon outdoor activity

across the O 3 

# season and exhibits a mostly monotonic relationship (2 nd 

# row of Figure 5G-7 and Figure 5G-8), though when considering durations of 2 hours or more, longitudinal outdoor participation drops dramatica lly (an non-linearly) fo r most persons comprising the study group (3 rd 

# row of Figure 5G-7 and Figure 5G-8). For mo re 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 maximu m value (around 3 of 5 days) was simulated for only about 10% or fewer children comprising the study group. St udy group participation in at least two hours of afternoon time outdoors day-to-day ranges from about 5-40% across each study area’s O 3 

# season (4 th 

# 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 st udies (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 diar ies 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 m onths while that of the ISR wane s. 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 O 3

# 5G-26 concentration days. While the use of these different studies in va rying numbers over the simulation period likely drives some of the obser ved variability in the outdoor participation, at this time (and previously) staff treat the C HAD 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 obs erved when considering the magnitude of the within study area variability. While different lengths of each study areas’ O 3 

# season may negate direct comparability of the dist ributions presented, it is reasonable to conclude that simulated school-age children in the Atlanta, Houston, a nd Sacramento study areas had slightly overall greater participation in outdoor activities and spent more tim e outdoors than counterparts in Boston, Denver, and Philadelphia. That said, when considering the daily time series of participation rates, on many summer days Philade lphia and Sacramento school-age participation rates for males are as great or greater than par ticipation rates observed most other study areas, including study areas likely having considerably warmer summer te mperatures (Figure 5G-7 and Figure 5G-8, 4 th 

# row). It is possible that for study ar eas such as Atlanta, summertime maximum daily temperatures exceed the range affording out door comfort, yielding s lightly lower rates of participation in outdoor activities. Overall, the simulated longitudinal profiles indicate the method for linking together cross-sectional diaries generate s a diverse mixture of persons having variable, though expected, activity patterns: a small frac tion of the simulated population spend a limited amount of afternoon time outdoors and occurring at a low frequency across an O 3 

# season, a small fraction consistently spends a greater amount (> 2 hour s) of time outdoors a nd occurring at greater frequency (e.g., 4/5 days per week), while the re maining 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 repr esenting the intended variability in longitudinal activity patterns

# 5G-27 0 10 20 30 40 50 60 70 80 90 100

0 60 120 180 240Cumulative 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%3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date ,

School-Age Children (Females) School-Age Children (Males)

0 200 400 600 800 10003/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Number of CHAD Diary DaysUsed 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 240Cumulative Percentile

Median Afternoon Time Outdoors (Minutes/Day) Boston, 2007

School- A ge Children (Females)
School- A ge 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- A ge 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%3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date ,

School- A ge Children (Females) School- A ge Children (Males)

0 200 400 600 800 10003/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Number of CHAD Diary DaysUsed 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 240Cumulative 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%3/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date ,

School-Age Children (Females) School-Age Children (Males)

0 200 400 600 800 10003/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Number of CHAD Diary DaysUsed Per Day

Exposure Simulation Date ,

CIN ISR OAB SEA SUP CA_studies NHAPS Other



# Figure 5G-7. Cumulative distribution of me dian time spent outdoors (top row), afternoon outdoor participation ≥ 1 minute/day (2 nd 

# row), and afternoon outdoor participation ≥ 2 hours/day (3 nd 

# row) for male and female school-age children in Atlanta (left column), Boston (middle column) and Denver (right colu mn) study areas. Percent of school-age children with ≥ 2 hours outdoors during afternoon hours (4 th 

# 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 240Cumulative 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%1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/200711/1/200712/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date ,

School-Age Children (Females) School-Age Children (Males)

0 200 400 600 800 10001/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/200711/1/200712/1/2007Number of CHAD Diary DaysUsed 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 240Cumulative Percentile

Median Afternoon Time Outdoors (Minutes/Day) Philadelphia, 2007

School- A ge Children (Females)
School- A ge 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- A ge 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%4/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date p ,

School- A ge Children (Females) School- A ge Children (Males)

0 200 400 600 800 10004/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/2007Number of CHAD Diary DaysUsed 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 240Cumulative 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%1/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/200711/1/200712/1/2007Afternoon OutdoorParticipation (≥2 Hours/Day)

Exposure Simulation Date ,

School-Age Children (Females) School-Age Children (Males)

0 200 400 600 800 10001/1/20072/1/20073/1/20074/1/20075/1/20076/1/20077/1/20078/1/20079/1/200710/1/200711/1/200712/1/2007Number of CHAD Diary DaysUsed Per Day

Exposure Simulation Date ,

CIN ISR OAB SEA SUP CA[X] NHAPS Other



# Figure 5G-8. Cumulative distribution of me dian time spent outdoors (top row), afternoon outdoor participation ≥ 1 minute/day (2 nd 

# row), and afternoon outdoor participation ≥ 2 hours/day (3 nd 

# row) for male and female school-age children in Houston (left column), Philadelphia (middle column) and Sacramento (r ight column) study areas. Percent of school-age children with ≥ 2 hours outdoors during afternoon hours (4 th 

# 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 addi tional simulations designed to complement exposures estimated using our general populatio n-based modeling approach presented in the main body of the HREA. These simulations in clude (1) exposures estimated for school-aged children during summer months only (section 5G-4.1), (2) adult outdoo r worker exposures (section 5G-4.2), and (3) exposures to school-a ge children and asthmatic school-age children assuming a portion of these study groups exhibit averting behavior in response to high O 3 

# concentration days (section 5G-4.3).

5G-4.1 Exposure Estimated For All School-A ge Children during Summer Months, neither Attending School nor Performing Paid Work A targeted simulation was performed for the De troit 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 incl uding 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 maximi ze the number of diarie s available for use by APEX we expanded the CHAD di ary selection to include child ren from 4-19 years old. In considering these diary selection criteria, the re sulting 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 analys is. Four air quality scenarios were considered: just meeting the existing 8-hour standa rd 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 r un using all available CHAD diaries during the same summer months (i.e., diar y days that include locations visited and activities performed from persons reporting either school time or paid work). Figure 5G-9 contains the exposur e results for this simulation ( “No School/Work

Diaries” ) and for a nearly identical simulation that di ffered only in that is used all CHAD diaries ( “all CHAD Diaries” ). When restricting the CHAD diar y 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 consideri ng multiple exposures over the simulation period.

# 5G-30 Figure 5G-9. Comparison of the percent of al l school-age children having daily maximum O 3 

# concentration at or above 60 ppb-8h r (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 adul t outdoor worker study groups (ages 19-35 and ages 35-54), using 2006 air quality just meeting the existing O 3 

# standard. To do this, both the daily and longitudinal activity patte rns used by APEX needed to be st reflect patterns expected for adult outdoor workers (e.g., a standardized work schedule during weekdays) while also capturing variability in those patterns across va rious occupation types and the overall simulated adult outdoor worker study group. The developmen t of reasonable time lo cation activity pattern data to be input to APEX was a complex undert aking, attempting to account for a number of

influential factors such as th e distribution of adult outdoor wo rkers, their varying occupation types, the probabilities associat ed with performing outdoor work, the linking of this information with the existing CHAD di aries and APEX METS distributions, all to be done within the existing APEX model framework and capabilities. First, the complete distribution of all empl oyed 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 Em ployment Matrix, data originally developed from the Occupational Employment Statisti cs (OES) survey and based on the 2010/2000 Standard Occupational Classification (SOC) syst em. The three variab les retained for our purposes here included the SOC occupation title s and codes and the 2010 estimated number of persons employed, covering ove r 750 occupation titles. Second, the identification of occupations wh ere 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 pa rtnership of public and private organizations 17 

# via sponsorship by the US Departme nt of Labor/Employment and Training Administration (USDOL/ETA). A wealth of in formation 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 structur al 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 se lected 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 Ins titute (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 worki ng outdoors, exposed to all weather conditions?”

and the second “how often does this job require working outdoor s, under cover (e.g., structure with roof but no walls)?”. The tables contain the responses to these tw o 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, occupati ons 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 partic ular occupation, thus th e greater the context score, the greater the likelihood of outdoor work participation. To start, there were 862 unique occupation code s 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 fo r question #2 alone). Assuming ozone exposure

would be similar for outdoor workers whether unde r cover or totally expo sed to weather, we merged the data responses from the two questio ns by occupation code an d assigned the highest context score of the two respons es to each occupation. Given th e 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 descri bed BLS SOC occupation data set was performed, and was generally agreeabl e, with a few exceptions. 18 

# Following additional processing, there were 144 unique occupation titles having one to fi ve days per week where work was performed outdoors and the number of persons constituti ng 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 co des from the 1990 US Census were mapped to twelve broad occupation categories 19 

# and were assigned METs distributions based on the most commonly performed activities associated with wo rk tasks. In order to use the APEX/CHAD

METs database in its current format and inte grate the newly develope d 2010 BLS/O*NET data set, the 1990 Census occupation codes were tran slated using two additi onal mapping files: a 1990 Census to 2000 Census code map file 20 

# 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 group s are Executive Administrative Managerial (ADMIN), Administrative support (ADMSUP), Professional (PROF), Technical (TECH), Sales (SALE), Protective Services (PROTECT), Service (S ERV), Farming Forestry Fishing (FARM), Precision Production (PREC), Machine Operators (MACH), Transportation (TRANS), and Laborers (LABOR). Household workers (HSHLD), while an APEX/CHA D 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 charac terizing several of the O*NET occupation codes to the broad APEX/CHAD occupation groups, there were seve ral instances where more than one broad occupation group could be assigne d to a single O*NET occupati on. 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 st ratified 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 we ek they spend outdoors.

Table 5G-8. Distribution of days per week sp ent 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 BLS/O*NET mean) (n) 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 inform ation (i.e., 60 locati on 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 patt erns potentially usable in representing

21 

## http://www.census.gov/people/eeotabu lation/documentation/occcategories.pdf

# 5G-34 outdoor workers. We evaluated these diarie s across a number of personal attributes and calculated the mean time spent outdoors for each di ary day (Table 5G-9). As expected, very few diaries were from persons that identified th eir occupation, though where that information was present, these diaries retained that specific iden tifier and used to simulate persons having that particular occupation group. Because we were inte rested 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 ti me 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 havi ng 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 occ upation group. This is in part be cause, 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 sa me diary to represent ev ery 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 represen t intra- and inter-personal variability activity patterns over multiple days by APEX, in gene ral 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 elect ed to use a random selection of diaries, though having the selected diaries draw n 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 ex ample follows using a single occupation group of outdoor workers, i.e., those comprisi ng the Transportation (TRANS) group. According to information summarized in Tabl e 5G-8, transportation-associated workers (TRANS) were estimated to, on average, spend four days per week working outdoors. We assigned the set of all availa ble weekday outdoor worker diar ies 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 thos e few diaries having a known occupation, TRANS or otherwise, to total 1,403 outdoor worker diar ies) to generate a da ta set now having four weekday days per person day. Thus from an APEX modeling perspective, a ll personal attributes for persons in that pool are identical from one da y to the next and have an equal likelihood of selection. This four day by 1,403 person activity pa ttern data set, now principally comprised of outdoor workers having ‘TRANS’ as their occupati on, was then combined with a single weekday by 1,403 person activity pattern data set, only that th is particular one day data set, while still

derived from the same set of outdoor worker diar ies, differs in that all of the paid work time spent outdoors was changed to paid work time o ccurring within indoor locations for that one work day. Then, when APEX constructs a long itudinal 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 th at occupation group spends time working outdoors. This process of building up th e activity pattern diary pool was repeated for each outdoor worker occupation group to reflect bo th 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 diar ies (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 days 22 

# 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 executi on. For these outdoor worker simulations to proceed, we determined the maximum size of th e 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 incl ude ages from 16 to 42 years old (n=30,657 person days). As a reminder, only the activity patterns of 16-18 year olds character ized as outdoor workers (i.e., persons having ≥ 2 hours of paid work occurring outdoors) we re available to simulate adults ages 19 or above. All of the anthropometric attri butes of these simulate d adults (e.g., body mass, resting metabolic rate, ventilation rate) were derived from their age appropriate data, distributions, and/or equations. There were adeq uate numbers of diaries available to model the 36-55 year olds such that the age range for inclus ion 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 additiona l input files were needed by APEX. The first was simply a f ile containing the CHAD ID and the specific occupation group identified for th at person day. The second, a prof ile factors file, contains the probabilities a simulated person in the model doma in 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 percen t of employed persons performing outdoor work using the outdoor worker proportions equally acr oss ages, sexes and tracts. An additional modification to the APEX employ ment probabilities input file ( Employment2000_043003.txt) was also needed to generate exposures and a ppropriate output summary tables only for employed persons. And finally, because of the generally lim ited number of diaries available in developing the diary pool for the 19-35 year olds and to us e 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 simulati on 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 long itudinal approaches developed here and used for estimating general population-ba sed 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 profile s, sampling from all available CHAD diaries, and not explicitly addressing simu lated 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 occupa tion, to ascertain whether or not the exposure simulation met our defined goal. In comparing th e 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 distri bution 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-bas ed approach designed specifically to reflect an outdoor worker w eekday schedule or usi ng 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 e xperiences exposures at or above the selected health effect

benchmark levels than that estimated usi ng our general population- based approach. The differences between exposures estimated for th e 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 populati on-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 experi ence exposures at or above that same level. In general, there was little difference in expos ures 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 strati fied by APEX/CHAD occupation and age
groups.

Additional context regardi ng 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 greate r 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 maximu m 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 exposure s 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 wo rking outdoors experienci ng instances of high exposure concentrations at elev ated 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 proba bly constitute a signif icant portion of the overall population-based study group that could ex ceed benchmark levels though at this time it is unknown whether the portion estimate d here of 40-60% is accurate. It is however reasonable to conclude given the comparative exposure resu lts 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%

123456PercentofPopulationExperiencingExposuresAtorAboveSelectedBenchmarkLevelWhileatModerateorGreaterExertion

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%

123456PercentofPopulationExperiencingExposuresAtorAboveSelectedBenchmarkLevelWhileatModerateorGreaterExertion

Number of Times Benchmark Exceeded (June

‐ August 2007) General Population

‐ based Approach (ages 19

‐ 35)

Atlanta, 2006, Just Meet Existing 75 ppb Standard

80 PPB

70 PPB

60 PPB 0% 5% 10% 15% 20% 25% 30%

123456PercentofPopulationExperiencingExposuresAtorAboveSelectedBenchmarkLevelWhileatModerateorGreaterExertion

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%

123456PercentofPopulationExperiencingExposuresAtorAboveSelectedBenchmarkLevelWhileatModerateorGreaterExertion

Number of Times Benchmark Exceeded (June

‐ August 2006) General Population

‐ based Approach (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 leve ls while at moderate or greater exertion using an outdoor worker scenario-based a pproach (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 invo lves evaluating the actions persons might perform in response to high O 3 

# concentration days (ISA, sec tion 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 O 3 

# concentration-response (CR) functions used to estimate health risk and on time expenditu re and activity exertion levels recorded in the CHAD diaries used by APEX to estimate O 3 

# exposures. To this end, we have performed an additional review of the available lite rature here beyond that summarized in the ISA to include several recent technical reports that collected and/or evalua ted averting behavior data. Our purpose is to genera te a few reasonable quantitative a pproximations that allow us to better understand how averting behavior might a ffect our current population-based exposure and risk estimates. We expect that the continue d development and communication of air quality information via all levels of environmental, health, and meteorological organizations will only

further increase awareness of ai r pollution, its associated health effects, and the recommended actions to take to avoid exposure, thus making av erting behaviors and partic ipation rates an even more important consideration in future O 3 

# 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 evalua tion is peoples’ general perception of air pollution and whether they were aware of aler t notification systems. The prevalence of awareness was variable; about 50% to 90% of survey study part icipants 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 se veral factors. In stud ies that considered a persons’ health status, e.g., asthma tics or parents of asthmatic ch ildren, there was a consistently greater degree of awareness (approximately a fe w to 15 percentage points) when compared to that of non-asthmatics. Residing in an urban area was also an impor tant 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 rema ining a statistically significant influential variable, were several commonly corre lated demographic attributes such as age, education-level, and income-level, with each fa ctor positively associated with awareness.

The second element considered in our eval uation was the type of averting behaviors performed. For our purposes in this O 3 

# HREA, the most relevant st udies 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 O 3 

# exposure and associated adverse e ffects, and hence, in accurately estimating human health risk. As stated above regarding ai r quality awareness, asthmatic s consistently indicated a greater likelihood of performing averting behavior s 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 system s. Based on the survey studies reviewed, we estimate that 30% of asthmatics may reduce their outdoor activ ity 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 Zi vin and Neidell (2009) study group is likely comprised mainly of non-asthmatics. That sa id, both attenuation and th e re-establishment of averting behavior was apparent when consider ing a few to several days above high pollution alert levels (either occurring over consecutive da ys or across an entire year) (McDermott et al., 2006; Zivin and Neidell, 2009), s uggesting that participation in averting behavior over amultiday 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 estimate s of durations of averting behavior, either considering outdoor exertion level or outdoo r 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. Ba sed on the studies reviewed, we estimate that outdoor time/exertion during afternoon hours ma y be reduced by about 20-40 minutes in response to an air quality alert not ification. Generally requisite f actors include: a high alert level for the day (e.g., red or greater on the AQI), high O 3 

# concentrations (above the NAAQS), and persons having a compromised health condition (e.g., asthmatic or elderly). The third element considered in our prelimin ary 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 (dia ries) from CHAD to estimate population-based exposures. These diaries origin ate from a number of differing st udies; some were generated as part of an air pollution resear ch study, some were collected during a summer/ozone season, while some diary days may have corresponded with high O 3 

# 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 pers on did or did not adjust thei r activity pattern reduce their exposure. In considering the above discussion re garding 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 redu ced their time spent outdoors or reduced their exertion level while outdoors. Currently, wi thout having a personal identifier for averting

25 

## Many of these studies do not account for one important factor when us ing 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 diarie s are assigned to a simulated persons’ day without directly considering ambient O 3 

# concentration levels 27 

# . Therefore, it is possibl e that there are instances where, on a given APEX simulati on day, the simulated person ma y use a diary day from a person that did engage in one or more types of aver ting behavior (e.g., a di ary having less time than usual spent outdoors in the after noon), while for most other persons simulated on the same day (or the same person on a different high concentrati on day) the diaries used are from persons that did not actively engage in averti ng behavior. As a result, the e ffect 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 estim ates of exposures (and reduced number of persons at or above selected 8-h O 3 

# 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 performe d during June-August 2007 in Detroit to identify a short time period where a high nu mber of children/asthmatic ch ildren were estimated to be exposed at or above the 8-hour O 3 

# benchmark levels of interest. To maintain a degree of tractability in the simulations, the development of the new CHAD i nput data, and the analysis of the exposure results, we restricted the exposur e simulations to 5,000 tota l 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 i nput data set, one adjusted for the above estimated parameters used to refl ect 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 di aries used to simulate the maximum exposure day for each person, obviously some CHAD diaries we re used more than once to simulate different people on their maximu m exposure day. Note als o, 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 inst ances 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 O 3 

## 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 exposur e day, estimated the mean outdoor time and number of persons by hour of day, and th en 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 ha ving an appropriate total tim e spent outdoors to achieve our desired adjustment of 15% and 30% pa rticipation 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 th e ‘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 minut es 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 categ ory (n=1,518 total diary days or 30.4% of all diaries used for each persons’ maximu m 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 out door 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 tw o diaries had time spent outdoors during the hours of 3 PM-4 PM where both days w ould be adjusted for averting, thus likely having a negligible effect the meeti ng of our approximate averting goals.

Both the outdoor time adjusted CHAD and the standard CHAD input files were separately used to simulate exposures to childr en and asthmatic children. Exposure results for the four simulations (simulated averting vs. no av erting for both children and asthmatic children) are found in the main body of the HREA.

5G-5 COMPARISON OF PERSONAL EXPO SURE MEASUREMENT AND APEX MODELED EXPOSURES A new evaluation of APEX was perform ed using a subset of personal O 3 

# exposure measurements obtained from the Detroit Expos ure and Aerosol Research Study (DEARS) (Meng et. al, 2012). For five c onsecutive days, personal O 3 

# outdoor concentrations along with daily time-location activity diaries we re collected from 36 study pa rticipants in Wayne County Michigan during July an d 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 be low the reported detec tion limit of 3 ppb (i.e., 0, 1 and 2 ppb), we approximated those falling belo w this level using a random assignment of concentrations of 1 and 2 ppb. An APEX simulation was performed consider ing these same geographic and temporal features, followed with the sub-setting of AP EX 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 independe ntly, the median daily values for each study participant attribute was genera ted 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 O 3 

# exposure, outdoor time, ambient

O 3 

# concentrations, and air exchange rate from each of the two data sets are presented in Figure

5G-11. Distributions of time spent outdoors and ambi ent concentrations were similar by design of the APEX population-based sample select ion method. The upper pe rcentiles the DEARS participant AER distribution was greater than th at of AER of APEX simulated persons. For example, 40% of DEARS participants had a medi an 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 O 3 

# 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 re sidential indoor air ex change rate for the DEARS participants (i.e., indoor microenvironmen tal exposures would be expected to have a greater influence on total DEARS exposure compar ed with the APEX simulated exposures) all while holding all other potential influential variab les 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 30Cumulative Percent (%)

Daily Mean O 3 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 600Cumulative 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 60Cumulative Percent (%)

Dail y Mean Ambient O 3 Concentration (pp b ) p50

min

max

0 10 20 30 40 50 60 70 80 90 100

0 1 2 3 4 5 6 7 8 9 10Cumulative 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 30Cumulative Percent (%)

Daily Mean O 3 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 600Cumulative 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 60Cumulative Percent (%)

Dail y Mean Ambient O 3 Concentration (pp b ) p50

min

max

0 10 20 30 40 50 60 70 80 90 100

0 1 2 3 4 5 6 7 8 9 10Cumulative Percent (%)

Daily Air Exchange Rate (hour -1 ) p50

min

max



# Figure 5G-11. Distribution of daily personal O 3 

# exposures (top row), outdoor time (2 nd 

# row from top), ambient O 3 

# concentrations (3 rd 

# row from top), and air exchange rate (bottom row) for DEARS study participants (left colu mn) 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 distributio ns of daily breathing rates for narrow age groups of infants and childr en. 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). Th e 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 y ears, using data from doubly labeled water measurements: a proposal for air quality criter ia, standard calculati ons and health risk assessment. Human and Ecologi cal Risk Assessment. 12:675-701. Brochu, P., Ducre-Robitaille, J.-F., Brodeur , J. (2006b). Supplemental Material for Physiological daily inhalation rates for free-liv ing individuals aged 1 month to 96 years, using data from doubly labeled water measurem ents: a proposal for ai r quality criteria, standard calculations and health risk assessmen t. 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. Electri c Power Research Institute. Prepared by Roth Associates. November 1988. EPRI. (1992). A Survey of Daily Asthmatic Ac tivity 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 monitori ng of physical activity in children. Pediatr Exerc Sci

. 1:8-18. George, B. J., McCurdy, T. (2009). Investiga ting the American Time Use Survey from an exposure modeling perspective. JESEE . 21:92-105. Geyh, A. S., Xue, J., Özkaynak, H., and Spengl er, J. D. (2000). The Harvard Southern California chronic ozone exposure study: as sessing 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., Langs taff, 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). Developi ng meaningful cohorts for human exposure models. J Expo Anal Environ Epidemiol . 14: 23-43.

# 5G-48 Graham, S. E., McCurdy, T. (2005). Revised ve ntilation rate (VE) equations for use in inhalation-oriented exposure models. Re port no. EPA/600/X-05/008 is Appendix A of US EPA (2009b). Available at: http://cfpub.epa.gov/ncea/cfm/r ecordisplay.cfm?deid=202543

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. Graham, S. E. (2012). Comprehensive review of published averting behavior studies and available technical documents. Memo to Br yan 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 Car bon Monoxide Exposure of Residents of Washington, DC and Denver, Colorado. Resear ch Triangle Park, NC: U.S. Environmental Protection Agency, Office of Research a nd 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 AE A/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. Envir onmental Protection Agency, Environmental Monitoring Systems Laboratory. EPA-600/4-84-014. Johnson, T. (1989). Human Activity Patterns in Cincinnati, Ohio. Pa lo Alto, CA: Electric Power Research Institute. EPRI EN-6204. Johnson, T., Capel, J., McCoy, M. (1996a). Es timation of Ozone Exposures Experienced by Urban Residents Using a Pr obabilistic Version of NEM and 1990 Population Data. Prepared by International Technology Air Qu ality Services for Of fice 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

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# 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 Prot ection 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, Outd oor Air Quality, and Activity Level. Results from 2005 Kansas Behavioral Risk Factor Surveillance Sy stem. Office of Health Promotion, Kansas Department of Hea lth and Environment. Available at: http://www.kdheks.gov/brfss/PDF/cste_report_final.pdf Langstaff, J. E. (2007). OAQPS Staff Memora ndum 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/st andards/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 hum an 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 assessmen t 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 outdoor s 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 playti me at home and school: Across-sectional analysis of a national sample of presc hool-aged children attending Head Start. Health & Place . 18: 1224-1230. McCurdy, T. (2000). Conceptual basis for mu lti-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 a nd compliance with air pollution advisories: A comparison of parent s of asthmatics with other parents. J Asthma

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# 5G-50 Meng, Q., Williams, R., Pinto, J. P. (2012). Determ inants of the associations between ambient concentrations and personal exposures to PM 2.5 

# , NO 2 

# , and O 3 

# 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. Washingt on, DC: National Academy of Sciences. Neidell, M. (2010). Air quali ty warnings and outdoor activitie s: evidence from Southern California using a regression di scontinuity approach design. J Epidemiol Community Health . 64:921-926. Richmond H., Palma, T, Langstaff, J., McCurd y, T., Glenn, G., Smith, L. (2002). Further Refinements and Testing of APEX (3.0): EPA' s population exposure mo del for criteria and air toxic inhalation exposures. Joint meeting of the Inte rnational 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 Implica tions for Potential Exposure to Pollution. California Air Resources Board, Sacramento, CA. CARB-A6-177-33. Sallis, J. F., Bauman, A., Pratt, M. (1998). E nvironmental 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 Guid e, 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 relations hip to hot weather and air pollution. Environ Res . 107:401-411. Sexton, A. L. (2011). Responses to Air Qu ality 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 a nd diurnal variation of respir atory 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., J ohnson, 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, Augu st 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 St atistics (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: h ttp://www.census.gov/prod/cen2000/doc/sf3.pdf. Individual SF3 files '30' (for income/pove rty 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 phys ical activity: a guide for co mmunity action. Champaign, IL: Human Kinetics. Department of Health a nd Human Services, Pub lic Health Service, Centers for Disease Control and Preventi on, National Center for Chronic Disease Prevention and Health Promotion, Division of Nu trition and Physical Activity. See Table 2-1 available at http://www.cdc.gov/nccdphp/dnpa/physic al/pdf/PA_Intensity_table_2_1.pdf

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# 5G-52 Triangle Park, NC. Available at: http://www.epa.gov/ttn/naaqs/st andards/ozone/s_o3_cr_td.html US EPA. (2008). Risk and Exposure Asse ssment to Support the Review of the NO 2 

# Primary National Ambient Air Quality Standard. EP A-452/R-08-008a. November 2008. Available

at: http://www.epa.gov/ttn/naaqs/standa rds/nox/data/20081121_ NO2_REA_final.pdf

. US EPA . (2009a). Risk and Exposure A ssessment to Support the Review of the SO 2 

# Primary National Ambient Air Quality Standard. EPA-452/R-09-007. August 2009. Available at http://www.epa.gov/ttn/naaqs/standard s/so2/data/200908SO2REAFinalReport.pdf

. US EPA. (2009b). Metabolically derived human ventilation rates: a re vised 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 Planni ng 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 Met hodology (TRIM) - Air Poll utants 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 Method ology (TRIM) - Air Pollut ants Exposure Model Documentation (TRIM.Expo / APEX, Vers ion 4.4) Volume II: Technical Support Document. Office of Air Quality Planning a nd 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, Nati onal Center for Environmental Assessment, U.S. Environmental Protection Agency, Res earch Triangle Park, N. EPA 600/R-10/076F. Available at: http://cfpub.epa.gov/ncea/isa/record isplay.cfm?deid=247492#Download University of Michigan. (2012). The Panel St udy 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 Di fference in physical activ ity in (un)diagnosed asthma and healthy controls. Pediatric Pulmonology . 42:1018-1023. van Veldhoven, N. H. M. J., Vermeer, A., Bogaar d, 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). Associ ation 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., Schw artz, J., Stone, P., Suh, H. H. (2006). The relationship between ambient air pollution and heart rate variab ility 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 Proba bilistic Assessment of Health Risks Associated with Short- and Long-Te rm 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 Reside nts: Final Report. California Air Resources Board, Sacramento, CA. ARB/R93/487. Ava ilable from: NTIS, Springfield, VA., PB94- 108719. Wiley, J. A., Robinson, J. P., Cheng, Y.-T., Piazz a, T., Stork, L., Pladse n, K. (1991b). Study of Children's Activity Patterns: Final Report. Ca lifornia 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 ma tter epidemiology-exposure study: Part 2. Personal exposure associated wi th 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., R odes, C., Thornburg, J. (2003b). The Research Triangle particulate panel study: modeling am bient source contributi ons 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 h aze: Environmental information disclosure and intertemporal avoidance behavior. J Environment Econ Manag . 58(2):119-128.5G-54Attachment 1. Occupations estimated to have at least one day per week where work is performed outdoors.Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkComment11ManagementOccupations Industrial Production Managers 11-0000 150.3 11-3051.02Geothermal ProductionManagers 77 111ManagementOccupationsFarmers, Ranchers, and OtherAgricultural Managers 11-9013 400.8 11-9013.01Nursery and GreenhouseManagers 96 5divided bls code 11-9013 (Farm/Ranch/Other AgManagers- 1202.5) by 3, possibly anoverestimate11ManagementOccupationsFarmers, Ranchers, and OtherAgricultural Managers 11-9013 400.8 11-9013.03 Aquacultural Managers 82 2divided bls code 11-9013 (Farm/Ranch/Other AgManagers- 1202.5) by 3, possibly anoverestimate11ManagementOccupations Construction Managers 11-9021523.1 11-9021.00 Construction Managers 78 113Business and FinancialOperations OccupationsInsurance Appraisers, AutoDamage 13-0000 10.6 13-1032.00Insurance Appraisers, AutoDamage 89 313Business and FinancialOperations Occupations Compliance Officers 13-1041 216.6 13-1041.04Government PropertyInspectors andInvestigators 82 213Business and FinancialOperations Occupations Farm Labor Contractors13-1074 0.3 13-1074.00 Farm Labor Contractors 79 113Business and FinancialOperations OccupationsAppraisers and Assessors ofReal Estate 13-2021 77.8 13-2021.02 Appraisers, Real Estate 77 117Architecture andEngineering Occupations Surveyors 17-0000 25.6 17-1022.00 Surveyors 90 3divided bls code 17-1022 (surveyors- 51.2) by 2,estimate should be ok17Architecture andEngineering Occupations Surveyors 17-1022 25.6 17-1022.01 Geodetic Surveyors 78 1divided bls code 17-1022 (surveyors- 51.2) by 2,estimate should be ok17Architecture andEngineering OccupationsSurveying and MappingTechnicians 17-3031 56.9 17-3031.01 Surveying Technicians 92 419Life, Physical, and SocialScience Occupations Conservation Scientists 19-0000 7.8 19-1031.01Soil and WaterConservationists 79 1divided bls code 19-1031 (ConservationScientists- 23.4) by 3, possibly an overestimate19Life, Physical, and SocialScience Occupations Conservation Scientists 19-1031 7.8 19-1031.03 Park Naturalists 78 1divided bls code 19-1031 (ConservationScientists- 23.4) by 3, possibly an overestimate19Life, Physical, and SocialScience OccupationsForest and ConservationTechnicians 19-4093 36.5 19-4093.00Forest and ConservationTechnicians 90 329Healthcare Practitionersand TechnicalOccupationsEmergency MedicalTechnicians and Paramedics 29-0000 226.5 29-2041.00Emergency MedicalTechnicians andParamedics 87 333Protective ServiceOccupationsFirst-Line Supervisors of Policeand Detectives 33-0000 106.1 33-1012.00First-Line Supervisors ofPolice and Detectives 82 233Protective ServiceOccupationsFirst-Line Supervisors of FireFighting and PreventionWorkers 33-1021 30 33-1021.01Municipal Fire Fighting andPrevention Supervisors 88 3divided bls code 33-1021 (First-Line Supervisorsof Fire Fighting and Prevention Workers- 60.1) by2, estimate should be ok33Protective ServiceOccupationsFirst-Line Supervisors of FireFighting and PreventionWorkers 33-1021 30 33-1021.02Forest Fire Fighting andPrevention Supervisors 86 3divided bls code 33-1021 (First-Line Supervisorsof Fire Fighting and Prevention Workers- 60.1) by2, estimate should be ok33Protective ServiceOccupations Firefighters 33-2011 155.2 33-2011.01 Municipal Firefighters 80 1divided bls code 33-2011 (Firefighters- 310.4) by2, estimate should be ok33Protective ServiceOccupations Firefighters 33-2011 155.2 33-2011.02 Forest Firefighters 87 3divided bls code 33-2011 (Firefighters- 310.4) by2, estimate should be ok33Protective ServiceOccupationsFire Inspectors andInvestigators 33-2021 6.8 33-2021.01 Fire Inspectors 86 3divided bls code 33-2021 (Fire Inspectors andInvestigators- 13.6) by 2, estimate should be ok33Protective ServiceOccupationsFire Inspectors andInvestigators 33-2021 6.8 33-2021.02 Fire Investigators 84 2divided bls code 33-2021 (Fire Inspectors andInvestigators- 13.6) by 2, estimate should be ok33Protective ServiceOccupationsForest Fire Inspectors andPrevention Specialists 33-2022 1.6 33-2022.00Forest Fire Inspectors andPrevention Specialists 77 15G-55Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkComment33Protective ServiceOccupationsDetectives and CriminalInvestigators 33-3021 23.9 33-3021.01 Police Detectives 87 3divided bls code 33-3021 (Detectives andCriminal Investigators- 119.4) by 5, possibly anoverestimate33Protective ServiceOccupationsDetectives and CriminalInvestigators 33-3021 23.9 33-3021.03Criminal Investigators andSpecial Agents 76 1divided bls code 33-3021 (Detectives andCriminal Investigators- 119.4) by 5, possibly anoverestimate33Protective ServiceOccupationsDetectives and CriminalInvestigators 33-3021 23.9 33-3021.05Immigration and CustomsInspectors 85 2divided bls code 33-3021 (Detectives andCriminal Investigators- 119.4) by 5, possibly anoverestimate33Protective ServiceOccupations Fish and Game Wardens 33-3031 7.6 33-3031.00 Fish and Game Wardens 96 533Protective ServiceOccupations Parking Enforcement Workers 33-3041 9.8 33-3041.00Parking EnforcementWorkers 95 433Protective ServiceOccupationsPolice and Sheriff's PatrolOfficers 33-3051 332 33-3051.01 Police Patrol Officers 91 4divided bls code 33-3051 (Police and SheriffPatrol Officers- 663.9) by 2, estimate should beok33Protective ServiceOccupationsPolice and Sheriff's PatrolOfficers 33-3051 332 33-3051.03Sheriffs and DeputySheriffs 88 3divided bls code 33-3051 (Police and SheriffPatrol Officers- 663.9) by 2, estimate should beok33Protective ServiceOccupations Animal Control Workers 33-9011 15.5 33-9011.00 Animal Control Workers 87 333Protective ServiceOccupationsPrivate Detectives andInvestigators 33-9021 34.7 33-9021.00Private Detectives andInvestigators 76 133Protective ServiceOccupations Crossing Guards 33-9091 69.3 33-9091.00 Crossing Guards 100 537Building and GroundsCleaning andMaintenance OccupationsFirst-Line Supervisors ofLandscaping, Lawn Service,and Groundskeeping Workers 37-0000 202.9 37-1012.00First-Line Supervisors ofLandscaping, LawnService, andGroundskeeping Workers 94 437Building and GroundsCleaning andMaintenance Occupations Pest Control Workers37-2021 68.4 37-2021.00 Pest Control Workers 98 537Building and GroundsCleaning andMaintenance OccupationsLandscaping andGroundskeeping Workers 37-3011 1151.5 37-3011.00Landscaping andGroundskeeping Workers 99 537Building and GroundsCleaning andMaintenance OccupationsPesticide Handlers, Sprayers,and Applicators, Vegetation 37-3012 29.5 37-3012.00Pesticide Handlers,Sprayers, and Applicators,Vegetation 89 337Building and GroundsCleaning andMaintenance Occupations Tree Trimmers and Pruners 37-3013 50.6 37-3013.00Tree Trimmers andPruners 99 539Personal Care andService Occupations 39-0000 29.3 39-4031.00Morticians, Undertakers,and Funeral Directors 77 1used employment data from bls code 39-4831(funeral dir., etc), estimate should be ok39Personal Care andService Occupations Baggage Porters and Bellhops 39-6011 46 39-6011.00Baggage Porters andBellhops 82 243Office and AdministrativeSupport Occupations Couriers and Messengers 43-0000 116.2 43-5021.00 Couriers and Messengers 89 343Office and AdministrativeSupport Occupations Meter Readers, Utilities 43-5041 40.5 43-5041.00 Meter Readers, Utilities 99 543Office and AdministrativeSupport Occupations Postal ServiceMail Carriers 43-5052 316.7 43-5052.00Postal Service MailCarriers 97 545Farming, Fishing, andForestry OccupationsFirst-Line Supervisors ofFarming, Fishing, and ForestryWorkers 45-0000 11.8 45-1011.05First-Line Supervisors ofLogging Workers 95 4divided bls code 45-1011 (First-Line Supervisorsof Farming, Fishing, and Forestry Workers- 47)by 4, estimate should be ok5G-56Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkComment45Farming, Fishing, andForestry OccupationsFirst-Line Supervisors ofFarming, Fishing, and ForestryWorkers 45-1011 11.8 45-1011.06First-Line Supervisors ofAquacultural Workers 87 3divided bls code 45-1011 (First-Line Supervisorsof Farming, Fishing, and Forestry Workers- 47)by 4, estimate should be ok45Farming, Fishing, andForestry OccupationsFirst-Line Supervisors ofFarming, Fishing, and ForestryWorkers 45-1011 11.8 45-1011.07First-Line Supervisors ofAgricultural Crop andHorticultural Workers 87 3divided bls code 45-1011 (First-Line Supervisorsof Farming, Fishing, and Forestry Workers- 47)by 4, estimate should be ok45Farming, Fishing, andForestry OccupationsFirst-Line Supervisors ofFarming, Fishing, and ForestryWorkers 45-1011 11.8 45-1011.08First-Line Supervisors ofAnimal Husbandry andAnimal Care Workers 84 2divided bls code 45-1011 (First-Line Supervisorsof Farming, Fishing, and Forestry Workers- 47)by 4, estimate should be ok45Farming, Fishing, andForestry Occupations Animal Breeders45-2021 11.5 45-2021.00 Animal Breeders 91 445Farming, Fishing, andForestry Occupations 45-2091 186.6 45-2091.00Agricultural EquipmentOperators 95 4used employment data from bls code 45-2090(misc ag- 746.4) divided by 4, possibly anoverestimate45Farming, Fishing, andForestry Occupations 45-2092 186.6 45-2092.01 Nursery Workers 76 1used employment data from bls code 45-2090(misc ag- 746.4) divided by 4, possibly anoverestimate45Farming, Fishing, andForestry Occupations 45-2092 186.6 45-2092.02Farmworkers andLaborers, Crop 98 5used employment data from bls code 45-2090(misc ag- 746.4) divided by 4, possibly anoverestimate45Farming, Fishing, andForestry Occupations 45-2093 186.6 45-2093.00Farmworkers, Farm,Ranch, and AquaculturalAnimals 83 2used employment data from bls code 45-2090(misc ag- 746.4) divided by 4, possibly anoverestimate45Farming, Fishing, andForestry OccupationsFishers and Related FishingWorkers 45-3011 32 45-3011.00Fishers and RelatedFishing Workers 93 445Farming, Fishing, andForestry Occupations 45-3021 0.645-3021.00 Hunters and Trappers 99 5diff of parent bls 45-3000 (fish hunt- 32.6) andsub 45-3011 (fishing- 32), estimate should be ok45Farming, Fishing, andForestry OccupationsForest and ConservationWorkers 45-4011 13.7 45-4011.00Forest and ConservationWorkers 83 245Farming, Fishing, andForestry Occupations Fallers45-4021 9.6 45-4021.00 Fallers 99 545Farming, Fishing, andForestry Occupations Logging Equipment Operators 45-4022 35.1 45-4022.00Logging EquipmentOperators 95 445Farming, Fishing, andForestry Occupations Log Graders and Scalers 45-4023 3.8 45-4023.00 Log Graders and Scalers 81 247Construction andExtraction OccupationsFirst-Line Supervisors ofConstruction Trades andExtraction Workers 47-0000 558.5 47-1011.00First-Line Supervisors ofConstruction Trades andExtraction Workers 83 247Construction andExtraction Occupations Brickmasonsand Blockmasons 47-2021 89.2 47-2021.00Brickmasons andBlockmasons 99 547Construction andExtraction Occupations Stonemasons47-2022 15.6 47-2022.00 Stonemasons 92 447Construction andExtraction Occupations Carpenters 47-2031 500.8 47-2031.01 Construction Carpenters 78 1divided bls code 47-2031 (Carpenters-1001.7) by2, estimate should be ok47Construction andExtraction Occupations Carpenters 47-2031 500.8 47-2031.02 Rough Carpenters 83 2divided bls code 47-2031 (Carpenters- 1001.7) by2, estimate should be ok47Construction andExtraction OccupationsCement Masons and ConcreteFinishers 47-2051 144.7 47-2051.00Cement Masons andConcrete Finishers 100 547Construction andExtraction Occupations Construction Laborers47-2061 998.8 47-2061.00 Construction Laborers 97 547Construction andExtraction OccupationsPaving, Surfacing, andTamping Equipment Operators 47-2071 51.6 47-2071.00Paving, Surfacing, andTamping EquipmentOperators 94 447 Construction and Pile-Driver Operators 47-2072 4.1 47-2072.00 Pile-Driver Operators 100 55G-57Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkCommentExtraction Occupations47Construction andExtraction OccupationsOperating Engineers and OtherConstruction EquipmentOperators 47-2073 349.1 47-2073.00Operating Engineers andOther ConstructionEquipment Operators 99 547Construction andExtraction Occupations Glaziers47-2121 41.9 47-2121.00 Glaziers 91 447Construction andExtraction OccupationsInsulation Workers, Floor,Ceiling, and Wall 47-2131 23.2 47-2131.00Insulation Workers, Floor,Ceiling, and Wall 87 347Construction andExtraction Occupations Pipelayers 47-2151 53.1 47-2151.00 Pipelayers 97 547Construction andExtraction OccupationsPlumbers, Pipefitters, andSteamfitters 47-2152 419.9 47-2152.02 Plumbers 79 147Construction andExtraction OccupationsReinforcing Iron and RebarWorkers 47-2171 19.1 47-2171.00Reinforcing Iron and RebarWorkers 94 447Construction andExtraction Occupations Roofers47-2181 136.7 47-2181.00 Roofers 100 547Construction andExtraction OccupationsStructural Iron and SteelWorkers 47-2221 59.8 47-2221.00Structural Iron and SteelWorkers 97 547Construction andExtraction OccupationsHelpers--Brickmasons,Blockmasons, Stonemasons,and Tile and Marble Setters 47-3011 29.4 47-3011.00Helpers--Brickmasons,Blockmasons,Stonemasons, and Tile andMarble Setters 86 347Construction andExtraction OccupationsHelpers--Pipelayers, Plumbers,Pipefitters, and Steamfitters 47-3015 57.9 47-3015.00Helpers--Pipelayers,Plumbers, Pipefitters, andSteamfitters 87 347Construction andExtraction Occupations Helpers--Roofers47-3016 12.7 47-3016.00 Helpers--Roofers 88 347Construction andExtraction OccupationsConstruction and BuildingInspectors 47-4011 102.4 47-4011.00Construction and BuildingInspectors 88 347Construction andExtraction Occupations Fence Erectors47-4031 32.1 47-4031.00 Fence Erectors 99 547Construction andExtraction Occupations Highway Maintenance Workers 47-4051 148.5 47-4051.00Highway MaintenanceWorkers 84 247Construction andExtraction OccupationsRail-Track Laying andMaintenance EquipmentOperators 47-4061 15 47-4061.00Rail-Track Laying andMaintenance EquipmentOperators 93 447Construction andExtraction OccupationsSeptic Tank Servicers andSewer Pipe Cleaners 47-4071 25.3 47-4071.00Septic Tank Servicers andSewer Pipe Cleaners 92 447Construction andExtraction Occupations Segmental Pavers47-4091 1.3 47-4091.00 Segmental Pavers 84 247Construction andExtraction Occupations Derrick Operators, Oil and Gas 47-5011 18.9 47-5011.00Derrick Operators, Oil andGas 100 547Construction andExtraction OccupationsRotary Drill Operators, Oil andGas 47-5012 22.5 47-5012.00Rotary Drill Operators, Oiland Gas 99 547Construction andExtraction OccupationsService Unit Operators, Oil,Gas, and Mining 47-5013 40.7 47-5013.00Service Unit Operators, Oil,Gas, and Mining 99 547Construction andExtraction OccupationsEarth Drillers, Except Oil andGas 47-5021 17.8 47-5021.00Earth Drillers, Except Oiland Gas 95 447Construction andExtraction OccupationsExplosives Workers, OrdnanceHandling Experts, and Blasters 47-5031 6.8 47-5031.00Explosives Workers,Ordnance HandlingExperts, and Blasters 98 547Construction andExtraction Occupations Rock Splitters, Quarry 47-5051 3.5 47-5051.00Rock Splitters, Quarry 94 447 Construction and Roustabouts, Oil and Gas 47-5071 52.7 47-5071.00 Roustabouts, Oil and Gas 100 55G-58Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkCommentExtraction Occupations49Installation, Maintenance,and Repair OccupationsTelecommunicationsEquipment Installers andRepairers, Except LineInstallers 49-0000 194.9 49-2022.00TelecommunicationsEquipment Installers andRepairers, Except LineInstallers 79 149Installation, Maintenance,and Repair OccupationsElectrical and ElectronicsRepairers, Powerhouse,Substation, and Relay 49-2095 23.4 49-2095.00Electrical and ElectronicsRepairers, Powerhouse,Substation, and Relay 92 449Installation, Maintenance,and Repair OccupationsAutomotive Glass Installers andRepairers 49-3022 18.1 49-3022.00Automotive Glass Installersand Repairers 88 349Installation, Maintenance,and Repair OccupationsBus and Truck Mechanics andDiesel Engine Specialists 49-3031 242.2 49-3031.00Bus and Truck Mechanicsand Diesel EngineSpecialists 83 249Installation, Maintenance,and Repair OccupationsFarm Equipment Mechanicsand Service Technicians 49-3041 32.9 49-3041.00Farm EquipmentMechanics and ServiceTechnicians 83 249Installation, Maintenance,and Repair OccupationsMobile Heavy EquipmentMechanics, Except Engines 49-3042 124.6 49-3042.00Mobile Heavy EquipmentMechanics, ExceptEngines 78 149Installation, Maintenance,and Repair Occupations Rail Car Repairers 49-3043 21.7 49-3043.00 Rail Car Repairers 81 249Installation, Maintenance,and Repair OccupationsMotorboat Mechanics andService Technicians 49-3051 20.8 49-3051.00Motorboat Mechanics andService Technicians 86 349Installation, Maintenance,and Repair Occupations Mechanical Door Repairers 49-9011 12.8 49-9011.00 Mechanical Door Repairers 93 449Installation, Maintenance,and Repair OccupationsControl and Valve Installersand Repairers, ExceptMechanical Door 49-9012 43.8 49-9012.00Control and Valve Installersand Repairers, ExceptMechanical Door 88 349Installation, Maintenance,and Repair OccupationsHeating, Air Conditioning, andRefrigeration Mechanics andInstallers 49-9021 133.9 49-9021.01Heating and AirConditioning Mechanicsand Installers 84 2divided bls code 49-9021 (Heating AC refrigmech- 267.8) by 2, estimate should be ok49Installation, Maintenance,and Repair OccupationsHeating, Air Conditioning, andRefrigeration Mechanics andInstallers 49-9021 133.9 49-9021.02Refrigeration Mechanicsand Installers 94 4divided bls code 49-9021 (Heating AC refrigmech- 267.8) by 2, estimate should be ok49Installation, Maintenance,and Repair Occupations Millwrights 49-9044 36.5 49-9044.00 Millwrights 77 149Installation, Maintenance,and Repair OccupationsElectrical Power-Line Installersand Repairers 49-9051 108.4 49-9051.00Electrical Power-LineInstallers and Repairers 95 449Installation, Maintenance,and Repair OccupationsTelecommunications LineInstallers and Repairers 49-9052 160.6 49-9052.00Telecommunications LineInstallers and Repairers 94 449Installation, Maintenance,and Repair OccupationsCoin, Vending, and AmusementMachine Servicers andRepairers 49-9091 39.1 49-9091.00Coin, Vending, andAmusement MachineServicers and Repairers 85 249Installation, Maintenance,and Repair Occupations Commercial Divers 49-9092 3.8 49-9092.00 Commercial Divers 92 449Installation, Maintenance,and Repair Occupations Locksmiths andSafe Repairers 49-9094 25.7 49-9094.00Locksmiths and SafeRepairers 87 349Installation, Maintenance,and Repair OccupationsManufactured Building andMobile Home Installers 49-9095 7.8 49-9095.00Manufactured Building andMobile Home Installers 86 349Installation, Maintenance,and Repair Occupations Riggers 49-9096 15.2 49-9096.00 Riggers 86 349Installation, Maintenance,and Repair OccupationsSignal and Track SwitchRepairers 49-9097 7.1 49-9097.00Signal and Track SwitchRepairers 96 549 Installation, Maintenance, 49-9099 143.6 49-9099.01 Geothermal Technicians 94 4 used employment data from bls code 49-97995G-59Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkCommentand Repair Occupations(install, repair, other, etc.), possibly anoverestimate51 Production OccupationsWater and WastewaterTreatment Plant and SystemOperators 51-0000 110.7 51-8031.00Water and WastewaterTreatment Plant andSystem Operators 93 451 Production Occupations Gas Plant Operators 51-8092 13.7 51-8092.00 Gas Plant Operators 92 451 Production OccupationsPetroleum Pump SystemOperators, Refinery Operators,and Gaugers 51-8093 44.2 51-8093.00Petroleum Pump SystemOperators, RefineryOperators, and Gaugers 93 453Transportation andMaterial MovingOccupationsAircraft Cargo HandlingSupervisors 53-0000 6.3 53-1011.00Aircraft Cargo HandlingSupervisors 77 153Transportation andMaterial MovingOccupations Commercial Pilots 53-201232.7 53-2012.00 Commercial Pilots 92 453Transportation andMaterial MovingOccupationsBus Drivers, Transit andIntercity 53-3021 186.3 53-3021.00Bus Drivers, Transit andIntercity 83 253Transportation andMaterial MovingOccupations Driver/Sales Workers 53-3031406.6 53-3031.00 Driver/Sales Workers 76 153Transportation andMaterial MovingOccupationsHeavy and Tractor-Trailer TruckDrivers 53-3032 1604.8 53-3032.00Heavy and Tractor-TrailerTruck Drivers 100 553Transportation andMaterial MovingOccupationsLight Truck or Delivery ServicesDrivers 53-3033 856 53-3033.00Light Truck or DeliveryServices Drivers 100 553Transportation andMaterial MovingOccupations Taxi Drivers and Chauffeurs 53-3041 239.9 53-3041.00Taxi Drivers andChauffeurs 78 153Transportation andMaterial MovingOccupations Locomotive Engineers 53-401138.7 53-4011.00 Locomotive Engineers 86 353Transportation andMaterial MovingOccupations Locomotive Firers 53-40121.1 53-4012.00 Locomotive Firers 86 353Transportation andMaterial MovingOccupationsRail Yard Engineers, DinkeyOperators, and Hostlers53-4013 5.6 53-4013.00Rail Yard Engineers,Dinkey Operators, andHostlers 84 253Transportation andMaterial MovingOccupationsRailroad Brake, Signal, andSwitch Operators 53-4021 21.7 53-4021.00Railroad Brake, Signal, andSwitch Operators 100 553Transportation andMaterial MovingOccupationsRailroad Conductors andYardmasters 53-4031 40.8 53-4031.00Railroad Conductors andYardmasters 87 353Transportation andMaterial MovingOccupationsSubway and StreetcarOperators 53-4041 6.5 53-4041.00Subway and StreetcarOperators 77 153Transportation andMaterial MovingOccupations Sailors and Marine Oilers 53-501133.4 53-5011.00 Sailors and Marine Oilers 99 553Transportation andMaterial MovingOccupationsCaptains, Mates, and Pilots ofWater Vessels 53-5021 36.1 53-5021.02Mates- Ship, Boat, andBarge 95 45G-60Major BLSSOC codeMajor BLS SOC nameBLS SOC nameBLS SOCcodeEmployed(1000’s)O*NET CodeO*NET NameContextOutdays/wkComment53Transportation andMaterial MovingOccupations Motorboat Operators 53-5022 3.1 53-5022.00 Motorboat Operators 85 253Transportation andMaterial MovingOccupations Ship Engineers 53-5031 10.1 53-5031.00 Ship Engineers 77 153Transportation andMaterial MovingOccupations Parking Lot Attendants 53-6021125.1 53-6021.00 Parking Lot Attendants 76 153Transportation andMaterial MovingOccupationsAutomotive and WatercraftService Attendants 53-6031 86.3 53-6031.00Automotive and WatercraftService Attendants 94 453Transportation andMaterial MovingOccupations Transportation Inspectors 53-6051 27.4 53-6051.08Freight and CargoInspectors 80 153Transportation andMaterial MovingOccupationsTransportation Attendants,Except Flight Attendants 53-6061 24.8 53-6061.00Transportation Attendants,Except Flight Attendants 77 153Transportation andMaterial MovingOccupations Dredge Operators 53-70312.1 53-7031.00 Dredge Operators 86 353Transportation andMaterial MovingOccupationsExcavating and LoadingMachine and DraglineOperators 53-7032 61.5 53-7032.00Excavating and LoadingMachine and DraglineOperators 88 353Transportation andMaterial MovingOccupationsGas Compressor and GasPumping Station Operators 53-7071 4.5 53-7071.00Gas Compressor and GasPumping Station Operators 91 453Transportation andMaterial MovingOccupationsPump Operators, ExceptWellhead Pumpers 53-7072 10.8 53-7072.00Pump Operators, ExceptWellhead Pumpers 99 553Transportation andMaterial MovingOccupations Wellhead Pumpers 53-7073 15.1 53-7073.00 Wellhead Pumpers 93 453Transportation andMaterial MovingOccupationsRefuse and Recyclable MaterialCollectors 53-7081 139.9 53-7081.00Refuse and RecyclableMaterial Collectors 100 553Transportation andMaterial MovingOccupationsTank Car, Truck, and ShipLoaders 53-7121 10.4 53-7121.00Tank Car, Truck, and ShipLoaders 91 45G-61Attachment 2. Additional mapping of O*NET occupationcodes to CHAD/APEX METs occupation activity codes.ifocc_code_onet='13-1032.00'thenOcc_name_CHAD='ADMSUP';*Insurance Appraisers, Auto Damage;ifocc_code_onet='13-1041.04'thenOcc_name_CHAD='ADMSUP';*Government Property Inspectors and Investigators;ifocc_code_onet='13-1074.00'thenOcc_name_CHAD='ADMSUP';*Farm Labor Contractors;ifocc_code_onet='13-2021.02'thenOcc_name_CHAD='ADMSUP';*Appraisers, Real Estate;ifocc_code_onet='17-3031.01'thenOcc_name_CHAD='TECH';*Surveying Technicians;ifocc_code_onet='19-4093.00'thenOcc_name_CHAD='TECH';*Forest and Conservation Technicians;ifocc_code_onet='33-9011.00'thenOcc_name_CHAD='PROTECT';*Animal Control Workers;ifocc_code_onet='33-9021.00'thenOcc_name_CHAD='PROTECT';*Private Detectives and Investigators;ifocc_code_onet='33-9091.00'thenOcc_name_CHAD='PROTECT';*Crossing Guards;ifocc_code_onet='37-3013.00'thenOcc_name_CHAD='FARM';*Tree Trimmers and Pruners;ifocc_code_onet='43-5021.00'thenOcc_name_CHAD='ADMSUP';*Couriers and Messengers;ifocc_code_onet='45-2021.00'thenOcc_name_CHAD='FARM';*Animal Breeders;ifocc_code_onet='45-2093.00'thenOcc_name_CHAD='FARM';*Farmworkers, Farm, Ranch, and Aquacultural Animals;ifocc_code_onet='45-3011.00'thenOcc_name_CHAD='FARM';*Fishers and Related Fishing Workers;ifocc_code_onet='45-4011.00'thenOcc_name_CHAD='FARM';*Forest and Conservation Workers;ifocc_code_onet='45-4023.00'thenOcc_name_CHAD='FARM';*Log Graders and Scalers;ifocc_code_onet='47-2061.00'thenOcc_name_CHAD='LABOR';*Construction Laborers;ifocc_code_onet='47-2071.00'thenOcc_name_CHAD='TRANS';*Paving, Surfacing, and Tamping Equipment Operators;ifocc_code_onet='47-2073.00'thenOcc_name_CHAD='TRANS';*Operating Engineers and Other Construction Equipment Operators;ifocc_code_onet='47-2121.00'thenOcc_name_CHAD='PREC';*Glaziers;ifocc_code_onet='47-4011.00'thenOcc_name_CHAD='ADMIN';*Construction and Building Inspectors;ifocc_code_onet='47-4051.00'thenOcc_name_CHAD='LABOR';*Highway Maintenance Workers;ifocc_code_onet='47-4061.00'thenOcc_name_CHAD='LABOR';*Rail-Track Laying and Maintenance Equipment Operators;ifocc_code_onet='47-5071.00'thenOcc_name_CHAD='LABOR';*Roustabouts, Oil and Gas;ifocc_code_onet='49-3022.00'thenOcc_name_CHAD='PREC';*Automotive Glass Installers and Repairers;ifocc_code_onet='49-9091.00'thenOcc_name_CHAD='PREC';*Coin, Vending, and Amusement Machine Servicers and Repairers;ifocc_code_onet='49-9094.00'thenOcc_name_CHAD='PREC';*Locksmiths and Safe Repairers;ifocc_code_onet='49-9096.00'thenOcc_name_CHAD='TRANS';*Riggers;ifocc_code_onet='49-9099.01'thenOcc_name_CHAD='TECH';*Geothermal Technicians;ifocc_code_onet='53-1011.00'thenOcc_name_CHAD='TRANS';*Aircraft Cargo Handling Supervisors;ifocc_code_onet='53-5011.00'thenOcc_name_CHAD='TRANS';*Sailors and Marine Oilers;ifocc_code_onet='53-5031.00'thenOcc_name_CHAD='TRANS';*Ship Engineers;ifocc_code_onet='53-6051.08'thenOcc_name_CHAD='TRANS';*Freight and Cargo Inspectors;*this following was mischaracterized as 'SALES';ifocc_code_onet='53-3032.00'thenOcc_name_CHAD='TRANS';*Heavy and Tractor-Trailer Truck Drivers;

# 5G-62 This page left inte ntionally blankUnited StatesEnvironmental ProtectionAgencyOffice of Air Quality Planning and StandardsHealth and Environmental Impacts DivisionResearch Triangle Park, NCPublication No. EPA-452/R-14-004cAugust 2014