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## 4 EXPOSURE TO AMBIENT OZONE

4.1 Introduction

The 2006 O 3 AQCD evaluated O 3 concentrations and exposures in multiple 

### 1 microenvironments, discussed methods for estimating personal and population exposure 

### 2 via monitoring and modeling, analyzed relationships between personal exposure and 

### 3 ambient concentrations, and discussed the impli cations of using ambient O 3 

### 4 concentrations as an estimate of exposure in epidemiologic studies. This chapter presents 

### 5 new information regarding exposure to ambient O 3 in the context of existing relevant 

### 6
information summarized in the 2006 O 3 AQCD, which in many areas remains definitive. 

### 7 A brief summary of findings from the 2006 O 3 AQCD is presented at the beginning of 

### 8 each section as appropriate. 

### 9

Section 4.2 presents general exposure concepts describing the relationship between 

### 10 ambient pollutant concentratio ns and personal exposure. Section 4.3 describes exposure 

### 11 measurement techniques and studies that measured personal, ambient, indoor, and 

### 12
outdoor concentrations of O 3 and related pollutants. Section 4.4 presents material on 

### 13 parameters relevant to exposure e stimation, including activity patterns, averting behavior, 

### 14 and population proximity to ambient monitors. Section 4.5 describes techniques for 

### 15 modeling local O 3 concentrations, air exchange rates, microenvironmental 

### 16 concentrations, and personal and populati on exposure. Section 4.6 discusses the 

### 17 implications of using ambient O 3 concentrations to estimate exposure in epidemiologic 

### 18 studies, including several factors that contribute to exposure error. 

### 19



## 4.2 General Exposure Concepts

A theoretical model of personal exp osure is presented to highlight measurable quantities 

### 20
and the uncertainties that exist in this framework. An individual ’ s time

- integrated total 

### 21 exposure to O 3 can be described based on a compartmentalization of the person ’ s 

### 22 activities throughout a given ti me period: 

### 23

Equation 4

- 1

where E T = total (T) exposure over a time

- period of interest, C j = airborne O 3 

### 24
concentration at microenvironment j , and dt = portion of the time

- period spent in 

### 25Draft

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. Equation 4

- 1 can be decomposed into a model that accounts for 

### 1 exposure to O 3 , of ambient ( E a ) and nonambient ( E n a ) origin of the form: 

### 2

Equation 4

- 2

Ambient O 3 is formed through photochemical reactions involving NO X , VOCs, and other 

### 3 compounds, as described in Chapter 3. Although nonambient sources of O 3 exist, such as 

### 4
O 3 generators and laser printers, these sources are specific to individuals and may not 

### 5

represe nt important sources of population exposure. Ozone concentrations generated by 

### 6 ambient and nonambient sources are subject to spatial and temporal variability that can 

### 7 affect estimates of exposure and influence epidemiologic effect estimates. Exposure 

### 8

param eters affecting interpretation of epidemiologic studies are discussed in Section 4.5. 

### 9

This assessment focuses on the ambient component of exposure because this is more 

### 10 relevant to the NAAQS review. Assuming steady

- state outdoor conditions, E a can be 

### 11

expres sed in terms of the fraction of time spent in various outdoor and indoor 

### 12
microenvironments ( Wallace et al., 2006 ; Wilson et al., 2000 ) : 

### 13

Equation 4

- 3

where f = fraction of the relevant time period (equivalent to dt in Equation 4

- 1), subscript 

### 14

o = index of outdoor microenvironments, subscript i = index of indoor 

### 15
microenviron ments, subscript o,i = index of outdoor microenvironments adjacent to a 

### 16 given indoor microenvironment i , and F inf, i = infiltration factor for indoor 

### 17
microenvironment (i) . Equation 4

- 3 is subject to the constraint Σ f

o + Σ f

i = 1 to reflect the 

### 18 total exposure over a specified time period, and each term on the right hand side of the 

### 19 equation has a summation because it reflects various microenvironmental exposures. 

### 20 Here, “ indoors ” refers to being inside any aspect of the built e nvironment, e.g., home, 

### 21 office buildings, enclosed vehicles (automobiles, trains, buses), and/or recreational 

### 22 facilities (movies, restaurants, bars). “ Outdoor ” exposure can occur in parks or yards, on 

### 23 sidewalks, and on bicycles or motorcycles. F inf is a fu nction of the building air exchange 

### 24 characteristics. Assuming steady state ventilation conditions, the infiltration factor is a 

### 25 function of the penetration ( P ) of O 3 into the microenvironment, the air exchange rate ( a ) 

### 26 of the microenvironment, and the rate of O 3 loss ( k ) in the microenvironment; F inf = 

### 27
Pa /( a + k ). 

### 28

In epidemiologic studies, the central

- site ambient concentration, C a , is often used in lieu 

### 29 of outdoor microenvironmental data to represent these exposures based on the availability 

### 30Draft

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- 3 September 2011 of data. Thus it is often assumed that C o = C a and that the fraction of time spent outdoors 

### 1 can be expressed cumulatively as f o ; the indoor terms still retain a summation because 

### 2 infiltration differs among different microenvironments. If an epidemiologic study 

### 3 employs onl y C a , then the assumed model of an individual ’ s exposure to ambient O 3 , 

### 4 first given in Equation 4

- 3 , is re

- expressed solely as a function of C a : 

### 5

Equation 4

- 4
The spatial variability of outdoor O 3 concentrations due to meteorology, varying 

### 6 precursor emissions and O 3 formation rates; design of the epidemiologic study; and other 

### 7
factors determine wheth er or not Equation 4

- 4 is a reasonable approximation for 

### 8 Equation 4

- 3. Errors and uncertainties inherent in use of Equation 4

- 4 in lieu of 

### 9 Equation 4

- 3 are described in Section 4.6 with respect to implications for interpreting 

### 10 epidemiologic studies. Epidem iologic studies often use concentration measured at a 

### 11 central site monitor to represent ambient concentration; thus α , the ratio between personal 

### 12 exposure to ambient O 3 and the ambient concentration of O 3 , is defined as: 

### 13

Equation 4

- 5
Combination of Equation 4

- 4 and Equation 4

- 5 yields: 

### 14

Equation 4

- 6

where α varies between 0 and 1. If a person ’ s exposure occurs in a single 

### 15 microenvironment, the ambient co mponent of a microenvironmental O 3 concentration 

### 16 can be represented as the product of the ambient concentration and F inf . Wallace et al. 

### 17 ( 2006 ) note that time

- activity data and corresponding estimates of F inf for each 

### 18 microenvironmental exposure are needed to compute an individual ’ s α with accuracy. In 

### 19 epidemiologic studies, α is assumed to be constant in lieu of time

- activity data and 

### 20
estimates of F inf , which can vary with building and meteorology

- related air exchange 

### 21 characteristics. If local outdoor sources and sinks exist and are significant but not 

### 22 captured by central site monitors, then the ambient component of the local outdoor 

### 23 concentration may be estimated using dispersion models, land use regression m odels, 

### 24 receptor models, fine scale CTMs or some combination of these techniques. These 

### 25 techniques are described in Section 4.5. 

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## 4.3 Exposure Measurement

This section describes techniques that have been used to measure microenvironmental 

### 1
concentrations of O 3 and personal O 3 exposures as well as results of studies using those 

### 2 techniques. Previous studies from the 2006 O 3 AQCD are described along with newer 

### 3
studies that evaluate indoor

- outdoor concentration relationships, associations between 

### 4 personal exposure and ambient monitor concentration, and multipollutant exposure to 

### 5 other pollutants in conjunction with O 3 . Tables are provided to summarize important 

### 6 study results. 

### 7

4.3.1 Personal Monitoring Techniques

As described in the 2006 O 3 AQCD, passive samplers have been developed and deployed 

### 8 to measure personal exposure to O 3 ( Grosjean and Hisham, 1992 ; Kanno and 

### 9 Yanagisawa, 1992 ) . Widely used versions of these samplers utilize a filter coated with 

### 10 nitrite, which is converted to nitrate by O 3 and then quantified by a technique such as ion 

### 11 chromatography ( Koutrakis et al., 1993 ) . This method has been licensed and marketed by 

### 12 Ogawa, Inc., Japan ( Ogawa and Co, 2007 ) . The cumulative sampling a nd the detection 

### 13 limit of the passive badges makes them mainly suitable for monitoring periods of 24 

### 14 hours or greater, which limits their ability to measure short

- term daily fluctuations in 

### 15 personal O 3 exposure. Longer sampling periods give lower detection limits; use of the 

### 16 badges for a 6

- day sampling period yields a detection limit of 1 ppb, while a 24

- hour 

### 17 sampling period gives a detection limit of approximately 5

- 10 ppb. This can result in a 

### 18 substantial fraction of daily samples being below the detectio n limit ( Sarnat et al., 2006b ; 

### 19 Sarnat et al., 2005 ) , which is a limitation of past and current exposure studies. 

### 20 Development of improved passive samplers capable of shorter

- duration monitoring w ith 

### 21 lower detection limits would enable more precise characterization of personal exposure in 

### 22 multiple microenvironments with relatively low participant burden. 

### 23

The nitrite

- nitrate conversion reaction has also been used as the basis for an active 

### 24 sampler c onsisting of a nitrite

- coated glass tube through which air is drawn by a pump 

### 25 operating at 65 mL/min ( Geyh et al., 19 99 ; Geyh et al., 1997 ) . The reported detection 

### 26
limit is 10 ppb

- h, enabling the quantification of O 3 concentrations measured over a few 

### 27
hours rather th an a full day ( Geyh et al., 1999 )

. 

### 28

A portable active O 3 monitor based on th e UV photometric technique used for stationary 

### 29 monitors (Chapter 3) has recently been approved as a FEM (75 FR 22126). This monitor 

### 30 includes a Nafion tube in the inlet line to equalize humidity, reducing the effect of 

### 31 humidity changes in different microenv ironments ( Wilson and Birks, 2006 ) . Its size and 

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### 1 configuration. The monitors are currently used by the U.S. National Park service as 

### 2 stationary monitors to measure O 3 in several national parks (Chapter 3). Future 

### 3 improvements and continued miniaturization of real

- time O 3 monitors can yield highly 

### 4
time

- resolved personal measurements to further evaluate O 3 exposures in specific 

### 5 situations, such as near roadways or while in transit. 

### 6

4.3.2 Indoor

- Outdoor Concentration Relationships

Several studies summarized in the 2006 O 3 AQCD, along with some newer studies, have 

### 7 evaluated the relationship between indoor O 3 concentration and the O 3 concentration 

### 8 immediately outside the indoor microenvironment. These studies show that the ind oor 

### 9 concentration is often substantially lower than the outdoor concentration unless indoor 

### 10 sources are present. Low indoor O 3 concentrations can be explained by reactions of O 3 

### 11
with surfaces and airborne constituents. Studies have shown that O 3 is deposit ed onto 

### 12 indoor surfaces where reactions produce secondary pollutants such as formaldehyde 

### 13 ( Reiss et al., 1995a ; Reiss et al., 1995b ) . However, the indoor

- outdoor relationship is 

### 14 greatly affected by the air exchange rate; under conditions of high air exchange rate, such 

### 15 as open windows, the indoor O 3 concentration may approach the outdoor concentration. 

### 16
Table 4

- 1 summarize s indoor

- outdoor (I/O) ratios and correlations reported by older and 

### 17 more recent studies, with discussion of individual studies in the subsequent text. In 

### 18 general, I/O ratios range from about 0.1 to 0.4, with some evidence for higher ratios 

### 19 during the O 3 s eason when concentrations are higher. 

### 20

O 3 concentrations near and below the monitor detection limit cause uncertainty in I/O 

### 21 ratios, because small changes in low concentration values cause substantial variation in 

### 22 resulting ratios. This problem is particula rly acute in the non

- ozone season when ambient 

### 23
O 3 concentrations are low. Further improvements in characterization of 

### 24
microenvironmental O 3 concentrations and I/O ratios will rely on improved monitoring. 

### 25 Until new monitoring techniques are available and ca n be used in the field, past studies 

### 26
summarized in the 2006 O 3 AQCD remain relevant to consider along with more recent 

### 27 studies in evaluating the relationship between indoor and outdoor O 3 concentrations. 

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### Table 4

- 1 Relationships of Indoor and Outdoor Ozone Concentration

Study Location Years/Season Population Sample duration Ratio a Correlation Micro

- environment Others Geyh et al. ( 2000 ) Upland, Southern Calif ornia June

- September 1995 and May 1996 Children 6 day 0.24 NR Home Air

- conditioned

October 1995

- April 1996 0.15

Mountain Communities, Southern California June

- September 1995 and May 1996 0.36 Opening windows October 1995

- April 1996 0.08

Avol et al. ( 1998b ) Southern California February

December, 1994 NR 24 h 0.37 SD: 0.25 0.58 Home

Summer 0.43 SD: 0.29 NR

Non

- summer 0.32 SD: 0.21 NR

Romieu et al. ( 1998b ) Mexico City, Mexico September 1993

- July 1994 Children 7 or 14 day 0.20 0.15 b Range: 0.01

- 1.00 NR Home

Lee et al. ( 2004a ) Nashville, TN Summer 1994 Children 1 week 0.1 NR Home

Héroux et al. ( 2010 ) Regina, Saskatchewan, Canada Summer 2007 All age groups 5 day 0.13 NR Home

López

- Aparicio et al. ( 2011 ) Prague, Czech Republic July 2009

– March 2010 NR 1 month 0.10

- 0.30 NR Home No heating or air conditioning

Liu et al. ( 1995 ) Toronto, Canada Winter, 1992 All age groups 1 week 0.07 SD: 0.10 NR Home
Summer, 1992 0.40 SD: 0.29 Summer, 1992 12 h 0.30 SD: 0.32 Daytime
Summer, 1992 0.43 SD: 0.54 Nighttime

Romieu et al. ( 1998b ) Mexico City, Mexico September 1993

- July 1994 Children 24 h/day, 14 days 0.15 NR School
Children (during school hours) 5 h/day, 10 days 0.30

- 0.40 Immediately outside the schools Blondeau et al. ( 2005 ) La Rochelle, France Spring, 2000 Chil dren NR Range: 0.00

- 0.45 NR School

Riediker et al. ( 2003 ) North Carolina August

- October 2001 Adults 9 h 0.51 NR Vehicle

a Mean value unless otherwise indicated b Median NR = not reported SD = standard deviation Geyh et al. ( 2000 ) measured 6

- day indoor and outdoor concentrations at 116 homes in 

### 1 southern California, approximately equally divided between the community of Upland 

### 2
and several mountain communi ties. The extended sampling period resulted in a relatively 

### 3 low detection limit (1 ppb) for the passive samplers used. The Upland homes were nearly 

### 4
all air

- conditioned, while the mountain community homes were ventilated by opening 

### 5 windows. During the O 3 se ason, the indoor O 3 concentration averaged over all homes 

### 6 was approximately 24% of the overall mean outdoor concentration in Upland 

### 7 (11.8 versus 48.2 ppb), while in the mountain communities, the indoor concentration was 

### 8 36% of the outdoor concentration (21 .4 versus 60.1 ppb). This is consistent with the 

### 9 increased air exchange rate expected in homes using window ventilation. In the non



### 10 ozone season, when homes are likely to be more tightly closed to conserve heat, the ratios 

### 11Draft

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- 7 September 2011 of indoor to outdoor concentratio n were 0.15 (3.2 versus 21.1 ppb) and 0.08 (2.8 versus 

### 1
35.7 ppb) in Upland and the mountain communities, respectively. Avol et al. ( 1998b ) 

### 2
observed a mean I/O ratio of 0.37 for 239 matched 24

- h samples collected between 

### 3 February and December at homes in the Los Angeles are a. The I/O ratio during summer 

### 4 was higher than the non

- summer I/O ratio (0.43 versus 0.32). The authors also reported a 

### 5
correlation of 0.58 between the 24

- h avg indoor concentration and the outdoor 

### 6 concentration, which was only slightly higher than the cor relation between the indoor 

### 7 concentration and the concentration at the neighborhood fixed

- site monitor (0.49). 

### 8 Substantially higher summer I/O ratios were reported in a study in Toronto ( Liu et al., 

### 9
1995 ) , which found summer I/O ratios of 0.30

- 0.43, in comparison with a winter I/O ratio 

### 10
of 0.07. Romieu et al. ( 1998b ) reported a mean I/O ratio of 0.20 in 145 homes in 

### 11
Mexico City for 7

- or 14

- day cumulative samples, with the highest ratios observed in 

### 12
homes where windows were usually open during the day and where there was no 

### 13 carpeting or air filters. Studies conducted in Nashville, TN and Regina, Saskatchewan 

### 14 reported mean residential I/O ratios of approximately 0.1 ( Héroux et al., 2010 ; Lee et al., 

### 15
2004a )

. 

### 16

Investigators have also measured I/O ratios for non

- residential microenvironments, 

### 17 including schools and vehicles. Romieu et al. ( 1998b ) reported that O 3 concentrations 

### 18 measured during school hours (10

- day cumulative sample, 5 h/day) were 30

- 40% of 

### 19 concentrations immediately outside the schools, w hile overall I/O ratios (14

- day 

### 20 cumulative sample, 24 h/day) were approximately 15%. The authors attribute this 

### 21 discrepancy to increased air exchange during the school day due to opening doors and 

### 22 windows. Air exchange was also identified as an important f actor in the I/O ratios 

### 23 measured at eight French schools ( Blondeau et al., 2005 ) . In this study, the I/O ratios 

### 24 based on simultaneous continuous measurements ranged from 0

- 0.45, increasing with 

### 25 decreasing building tightness. A historical library building in Prague, Czech Republic 

### 26 with no heating or air conditioning (i.e., natural ventilati on) was observed to have ratios 

### 27
of one

- month indoor and outdoor concentrations ranging from 0.10

- 0.30 during a nine



### 28 month sampling campaign, with the highest ratios reported in Nov

- Dec 2009 and the 

### 29 lowest ratios during Jul

- Aug 2009 ( López

- Aparicio et al., 2011 ) . Indoor concentrations 

### 30 were relatively constant (approximately 3

- 7 ug/m 3 or 2

- 3 ppb), while outdoor 

### 31 concentrations were lower in the winter (9

- 10 ug/ m 3 or about 5 ppb) than in the summer 

### 32 (35

- 45 ug/ m 3 or about 20 ppb) . This seasonal variation in outdoor concentrations 

### 33 coupled with homogeneous indoor concentrations, together with increased wintertime air 

### 34 exchange rate due to higher indoor

- outdoor temperature differences, is likely responsible 

### 35 for the observed seasonal p attern in I/O ratios. 

### 36

Exposures in near

- road, on

- road and in

- vehicle microenvironments are likely to be highly 

### 37
variable and lower than those in other microenvironments due to reaction of O 3 with NO 

### 38Draft

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- 8 September 2011 and other combustion emissions. Depending on wind directi on, O 3 concentrations near 

### 1 the roadway have been found to be 20

- 80% of ambient concentrations at sites 400 m or 

### 2 more distant from roads (Section 3.6.2.1). A study on patrol cars during trooper work 

### 3 shifts reported in

- vehicle 9

- h concentrations that were ap proximately 51% of 

### 4 simultaneously measured roadside concentrations (mean of 11.7 versus 22.4 ppb) 

### 5 ( Riediker et al., 2003 )

. 

### 6

4.3.3 Personal

- Ambient Concentration Relationships

Several factors influence the relationship between personal O 3 exposure and ambient 

### 7
concentration. Due to the lack of indoor O 3 sources, along with reduction of ambient O 3 

### 8 that penetrates into enclosed microenvironments, indoor and in

- vehicle O 3 concentrations 

### 9 are highly dependent on air exchange rate and therefore vary widely in different 

### 10
microenviron ments. Ambient O 3 varies spatially due to reactions with other atmospheric 

### 11 species, especially near busy roadways where O 3 concentrations are decreased by 

### 12 reaction with NO (Section 3.6.2.1). This is in contrast with pollutants such as CO and 

### 13
NO X , which sho w appreciably higher concentrations near the roadway than several 

### 14 hundred meters away ( Karner et al., 2010 )

. O 3 also varies temporally over multiple 

### 15 scales, with a generally increasing trend during the daytime hours, and higher O 3 

### 16 concentrations during summer than in winter. An example of this variability is shown in 

### 17 Figure 4

- 1, taken fro m a personal exposure study conducted by Chang et al. ( 2000 )

. 

### 18

Hourly personal exposures are seen to vary from a few ppb in some indoor 

### 19
microenvironments to tens of ppb in vehicle and outdoor microenvironments. The 

### 20
increase in ambient O 3 concentration during the day is apparent from the outdoor 

### 21 monitoring data. In comparison, ambient PM 2.5 exhibits less temporal variability over the 

### 22 day than O 3 , although pe rsonal exposure to PM 2.5 also varies by microenvironment. This 

### 23 combined spatial and temporal variability for O 3 results in varying relationships between 

### 24 personal exposure and ambient concentration. 

### 25

Correlations between personal exposure to O 3 and correspon ding ambient concentrations, 

### 26
summarized in Table 4

- 2, exhibit a wide range (generally 0.3

- 0.8, although both higher 

### 27 and lower values have been reported), with higher correlations generally observed in 

### 28 outdoor microenvironments, high building ventilation co nditions, and during the summer 

### 29
season. Low O 3 concentrations indoors and during the winter lead to a high proportion of 

### 30 personal exposures below the sampler detection limit, which may partially explain the 

### 31
low correlations observed in some studies under t hose conditions. Ratios of personal 

### 32 exposure to ambient concentration, summarized in Table 4

- 3, are generally lower in 

### 33 magnitude (typically 0.1

- 0.3), and are also variable, with increasing time spent outdoors 

### 34Draft

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- 9 September 2011 associated with higher ratios. The next two sub sections describe studies that have 

### 1 reported personal

- ambient correlations and slopes for a variety of seasons, locations, and 

### 2 populations. 

### 3

Source: Reprinted with permission of Air a nd Waste Management Association ( Chang et al., 2000 )
The notation below each clock hour shows the location or activity during that hour.



### Figure 4

- 1 Variation in hourly personal and ambient concentrations of O 3 

### and PM 2.5 

### in va rious microenvironments during daytime hours.

O 3 concentrations near and below the passive sampler detection limit lead to uncertainty 

### 4 in personal

- ambient correlations and ratios. Correlations are reduced in magnitude by 

### 5
values below the detection limit b ecause noise obscures the underlying signal in the data, 

### 6 while ratios tend to fluctuate widely at low concentration since small changes in 

### 7 measured values cause large relative changes in resulting ratios. As with I/O ratios, this 

### 8 problem is particularly ac ute in the non

- ozone season when ambient O 3 concentrations 

### 9 are low. Improved characterization of the relationship between personal exposure and 

### 10 ambient concentration will depend on improved monitoring techniques to accurately 

### 11 capture low O 3 concentrations, preferably at high time resolution to facilitate evaluation 

### 12 of the effect of activity pattern on exposure. Until new monitoring techniques are 

### 13 available, past studies summarized in the 2006 O 3 AQCD remain relevant to consider 

### 14Draft

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- 10 September 2011 along with mo re recent studies in evaluating personal

- ambient concentration 

### 1 relationships. 

### 2

Personal

- Ambient Correlations. Correlations between personal exposure and ambient 

### 3
O 3 concentrations have been evaluated in several research studies, many of which were 

### 4
conducted prior to 2005 and are discussed in the 2006 O 3 AQCD. Some studies evaluated 

### 5 subject

- specific, or longitudinal correlations, which describe multiple daily measurements 

### 6 for a single individual. These studies indicate the inter

- individual variability of perso nal



### 7 ambient correlations. Another type of correlation is a pooled correlation, which combines 

### 8 data from multiple individuals over multiple monitoring periods (e.g., days), providing an 

### 9 overall indicator of the personal

- ambient relationship for all study su bjects. A third type 

### 10 of correlation is a community

- average correlation, which correlates average exposure 

### 11 across all study subjects with fixed

- site monitor concentrations. Community

- average 

### 12 correlations are particularly informative for interpreting time

- se ries epidemiologic 

### 13 studies, in which ambient concentrations are used as a surrogate for community

- average 

### 14 exposure. However, few studies report this metric; this represents another opportunity for 

### 15 improvement of future personal exposure studies. Table 4

- 2 summarizes studies reporting 

### 16 personal

- ambient correlations, and the studies in the table are discussed in the subsequent 

### 17

text. 

### 18

The results of these studies indicate that personal exposures are moderately well 

### 19 correlated with ambient concentrations, and th at the ratio of personal exposure to ambient 

### 20 concentration is higher in outdoor microenvironments and during the summer season. In 

### 21 situations where a lack of correlation was observed, this may be due in part to a high 

### 22 proportion of personal measurements be low the detection limit. The effect of season is 

### 23 unclear, with mixed evidence on whether higher correlations are observed during the O 3 

### 24 season. Chang et al. ( 2000 ) measured hourly personal exposures in multiple 

### 25 microenvironments and found that the pooled correlation between personal exposure and 

### 26 ambient concentration was highest for outdoor microenvironments (r = 0.68

- 0.91). In



### 27
vehicle microenvironments sh owed moderate to high correlations (0.57

- 0.72). 

### 28 Correlations in residential indoor microenvironments were very low (r = 0.05

- 0.09), with 

### 29 moderate correlations (0.34

- 0.46) in other indoor microenvironments such as restaurants 

### 30 and shopping malls. Liard et al

. ( 1999 ) evaluated community

- average correlations based 

### 31
on 4

- day mean personal O 3 exposure measurements for adults and children and found a 

### 32 relatively high correlation (r = 0.83) with ambient concentrations, even tho ugh 31

- 82% of 

### 33 the personal measurements were below the detection limit. Sarnat et al. ( 2000 ) studied a 

### 34 population of older adults in Baltimore and found that longi tudinal correlations between 

### 35
24

- h personal exposure and ambient concentration varied by subject and season, with 

### 36 somewhat higher correlations observed in this study during summer (mean = 0.20) than 

### 37 in winter (mean = 0.06). Some evidence was presented that subjects living in well

- 

### 38Draft

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- 11 September 2011 ventilated indoor environments have higher correlations than those living in poorly 

### 1 ventilated indoor environments, although exceptions to this were also observed. Ramirez



### 2 Aguilar et al. ( 2008 ) measured 48

- to 72

- h personal exposures of four groups of asthmatic 

### 3 children aged 6

- 14 in Mexico City during 1998

- 2000. A moderate pooled correlation (r = 

### 4 0.35) was observed between these exposures and corresponding ambient concentrations. 

### 5

Tab le 4

- 2 Correlations between Personal and Ambient Ozone Concentration

Study Location Years/Season Population Sample duration Correlation Study Type Others Chang et al. ( 2000 ) Baltimore, MD Summer 1998 Older adults 1 h 0.91 Pooled Outdoor near roadway Winter 1999 0.77

Summer 1998 0.68 Outdoor away from road Winter 1999 0.86

Summer 1998 0.72 In vehicle

Winter 1999 0.57

Summer 1998 0.09 Indoors

- residence Winter 1999 0.05

Summer 1998 0.34 Indoors

- other

Winter 1999 0.46

Liard et al. ( 1999 ) Paris, France Summer 1996 All age groups 4 day 0.83 Community

- averaged Sarnat et al. ( 2000 ) Baltimor e, MD Summer Older adults 24 h 0.20 Longitudinal
Winter 0.06

Linn et al. ( 1996 ) Southern California All seasons from 1992 to 1993 Children 24 h 0.61 Community

averaged Brauer and Brook ( 1997 ) Vancouver, Canada Summers 1992 and 1993 Health clinic workers 24 h 0.60 Pooled 0

- 25% of time outdoors Camp counselors 24 h 0.42 Pooled 7.5

- 45% of time outdoors Farm workers 24 h 0.64 Pooled 100% of time outdoors Ramírez

Aguilar et al. ( 2008 ) Mexico City, Mexico December 1998

- April 2000 Asthmatic children 48 h to 72 h 0.35 Pooled

NR = not reported

Consistent with hourly microenvironment

- specific results from the Chang et al. ( 2000 ) 

### 6 study described above, studies have found moderate to high personal

- ambient 

### 7 correlations for individuals spending time outdoors. A moderate pooled correlation of 

### 8
0.61 was reported between 24

- h avg personal and central

- site measurements by Linn et 

### 9
al. ( 1996 ) for a population of southern California schoolchildren who spent an average of 

### 10
101

- 136 minutes per day outdoors. The authors also report a correlation of 0.70 between 

### 11
central

- site measurements and concentrations outside the children ’ s schools. Although 

### 12 the average school outdoor concentration (34 ppb) was higher than the average central



### 13 site concentration (23 ppb) and the average personal exposure concentration was lower 

### 14Draft

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- 12 September 2011 (5 ppb) than the centra l

- site value, the similarity between the correlations indicate that 

### 1
central

- site monitor concentrations can represent personal exposures in addition to 

### 2 representing local outdoor concentrations. A study in Vancouver, BC provided another 

### 3
illustration of the effect of outdoor microenvironments on personal

- ambient relationships 

### 4 by comparing three groups spending different amounts of time outdoors: health clinic 

### 5 workers (0

- 25% of time outdoors), camp counselors (7.5

- 45% of time outdoors), and 

### 6 farm workers (100% of time outdoors) ( Brauer and Brook, 1997 ) . Health clinic workers 

### 7 and camp counselors were monitored 24 h/day, while farm workers were monitored 

### 8 during their work shift (6

- 14 hours). In this study, the pooled correlations between 

### 9 personal exposure and fixed

- site concentration not substantially different among the 

### 10 groups (r = 0.60 , 0.42, and 0.64, respectively). The ratios of personal exposure to fixed



### 11 site monitor concentration increased among the groups with increasing amount of time 

### 12 spent outdoors (0.35, 0.53, and 0.96, respectively). This indicates that temporal variations 

### 13 in p ersonal exposure to O 3 are driven by variations in ambient concentration, even for 

### 14 individuals that spend little time outdoors. 

### 15

Personal

- Ambient Ratios. Studies indicate that the ratio between personal O 3 exposure 

### 16 and ambient concentration varies widely, d epending on activity patterns, housing 

### 17 characteristics, and season. Higher personal

- ambient ratios are generally observed with 

### 18 increasing time spent outside, higher air exchange rate, and in seasons other than winter. 

### 19
Table 4

- 3 summarizes the results of se veral such studies discussed in the 2006 O 3 AQCD 

### 20 together with newer studies showing the same pattern of results. 

### 21

O ’ Neill et al. ( 2003 ) studied a population of shoe cleaners working outdoors in 

### 22
Mexico City and presented a regression model indicating a 0.56 ppb increase in 6

- h 

### 23 personal exposure for each 1 ppb increase in ambient conce ntration. Regression analyses 

### 24 by Sarnat et al. for 24

- h data from mixed populations of children and older adults in 

### 25
Baltimore ( 2001 ) and Boston ( 2005 ) found differing results between the two cities, with 

### 26 Baltimore subjects showing a near

- zero slope (0.01) during the summertime while Boston 

### 27 subjects showed a positive slope of 0.27 ppb personal exposure per 1 ppb ambient 

### 28 concentration. In both cities, the winter slope was near zero. The low slope observed in 

### 29 Baltimore may have been d ue to differences in time spent outdoors, residential 

### 30 ventilation conditions, or other factors. Xue et al. ( 2005 ) measur ed 6

- day personal 

### 31 exposure of children in southern California and found that the average ratio of personal 

### 32 exposure to ambient concentration was relatively stable throughout the year at 0.3. These 

### 33 authors also regressed personal exposures on ambient concen tration after adjusting for 

### 34
time

- activity patterns and housing characteristics and found a slope of 0.54 ppb/ppb, with 

### 35 the regression R 2 value of 0.58. Unadjusted regression slopes were not presented. 

### 36Draft

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- 13 September 2011 

### Table 4

- 3 Ratios of Personal to Ambient Ozone Concentra tion

Study Location Years/Season Population Sample duration Ratio Study Type Others

Sarnat et al. ( 2001 ) Baltimore Summer 1998 Older adults, children, and individuals with COPD 24 h 0.01 Longitudinal Winter 1999 0.00 Brauer and Brook ( 1997 ) Vancouver, Canada Summers 1992 and 1 993 Health clinic workers 24 h 0.35 Pooled 0

- 25% of time outdoors Camp counselors 0.53 Pooled 7.5

- 45% of time outdoors Farm workers 0.96 Pooled 100% of time outdoors O ’ Neill et al. ( 2003 ) Mexico City, Mexico April

- July 1996 Shoe cleaners 6 h 0.56 95% CI: 0.43

- 0.69 Longitudinal

Sarnat et al. ( 2005 ) Boston Summer Older adults and children 24 h 0.27 95% CI: 0.18

- 0.37 Longitudinal

Winter 0.04 95% CI: 0.00

- 0.07 Xue et al. ( 2005 ) Southern California June 1995

- May 1996 Children 6 day 0.3 Longitudinal Ratio 0.54 Regression slope (R 2 = 0.58) Sarnat et al. ( 2006b ) Steubenville, OH Summer Older adults 24 h 0.15 SE: 0.02 Longitudinal High

- ventilation

0.08 SE: 0.04 Low

- ventilation

Fall 0.27 SE: 0.03 High

- ventilation

0.20 SE: 0.05 Low

- ventilation

Ramírez

- Aguilar et al. ( 2008 ) Mexico City, Mexico Dec.1998

- Apr. 2000 Asthmatic children 48 h to 72 h 0.17 Pooled

NR = not reported

A few additional studies have been published since the 2006 O 3 AQCD comparing 

### 1 personal exposures with ambient concentrations, and these findings generally confirm the 

### 2
conclusions of the 2006 O 3 AQCD that ventilation conditions, activity pattern, and 

### 3 season may impact perso nal

- ambient ratios. Sarnat et al. ( 2006b ) measured 24

- h personal 

### 4 exposures for a panel of older adults in Steubenville, OH during summer and fall 2000. 

### 5 Subjects were classified as high

- ventilation or low

- ventilation based on whether they 

### 6 spent time in indoor environments with open windows. Regression of perso nal exposures 

### 7 on ambient concentration found a higher slope for high

- ventilation subjects compared 

### 8
with low

- ventilation subjects in both summer (0.15 versus 0.08) and fall (0.27 versus 

### 9 0.20). Suh and Zanobetti ( 2010 ) reported an average 24

- h personal exposure of 2.5 ppb as 

### 10 compared to 24

- h ambient concentration of 29 ppb for a group of individuals with either 

### 11 recent MI or diagnosed COPD in Atlanta. A similar result was observed in Detroit, where 

### 12
the mean 24

- h personal exposure across 137 participants i n summer and winter was 2.1 

### 13 ppb, while the mean ambient concentration on sampling days was 25 ppb ( Williams et 

### 14 al., 2009b ) . Although no personal e xposures were measured, McConnell et al. ( 2006 ) 

### 15 found that average 24

- h home outdoor O 3 concentrations were within 6 ppb of O 3 

### 16
concentrations measured at central

- site monitors in each of three southern California 

### 17

comm unities, with a combined average home outdoor concentration of 33 ppb compared 

### 18
to the central

- site average of 36 ppb. In Mexico City, Ramirez

- Aguilar et al. ( 2008 ) 

### 19Draft

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- 14 September 2011 regressed 48

- to 72

- h personal exposures of four groups of asthmatic children aged 6

- 14 

### 1 with ambient concentrations and found slope of 0.17 ppb/ppb after adjustment for 

### 2
dista nce to the fixed

- site monitor, time spent outdoors, an interaction term combining 

### 3 these two variables, and an interaction term representing neighborhood and study group. 

### 4

4.3.4 Co

- Exposure to Other Pollutants and Environmental Stressors

Exposure to ambient O 3 oc curs in conjunction with exposure to a complex mixture of 

### 5 ambient pollutants that varies over space and time. Multipollutant exposure is an 

### 6 important consideration in evaluating health effects of O 3 since these other pollutants 

### 7 have either known or potenti al health effects that may impact health outcomes due to O 3 . 

### 8
The co

- occurrence of high O 3 concentrations with high heat and humidity may also 

### 9 contribute to health effects. This section presents data on relationships between overall 

### 10 personal O 3 exposure and exposure to other ambient pollutants, as well as co

- exposure 

### 11 relationships for near

- road O 3 exposure. 

### 12

4.3.4.1 Personal Exposure to Ozone and Co

- pollutants

Personal exposure to O 3 shows variable correlation with personal exposure to other 

### 13 pollutants, with differen ces in correlation depending on factors such as instrument 

### 14 detection limit, season, city

- specific characteristics, and spatial variability of the 

### 15 copollutant . Suh and Zanobetti ( 2010 ) reported Spearman rank correlation coefficients 

### 16 during spring and f all between 24

- h avg O 3 measurements and co

- pollutants of 0.14, 

### 17 0.00, and

- 0.03 for PM 2.5 , EC, and NO 2 , respectively. Titration of O 3 near roadways is 

### 18 likely to contribute to the low or slightly negative correlations with the traffic

- related 

### 19 pollutants EC and NO 2 . The somewhat higher correlation with PM 2.5 may reflect the 

### 20 influence of air exchange rate and time spent outdoors on co

- exposures to ambient PM 2.5 

### 21
and O 3 . Overall, the copollutant correlations are quite small, which may be due to the 

### 22 very low personal exposures observed in this study (2

- 3 ppb), likely to be near or below 

### 23 the detection limit of the passive sampler over a 24

- h period. Chang et al. ( 2000 ) 

### 24 measured hourly personal exposures to PM 2.5 and O 3 in summer and winter in Baltimore, 

### 25 Maryland. Correlations between PM 2.5 and O 3 were 0.05 and

- 0.28 in summer and 

### 26 winter, respective ly. Results indicate personal O 3 exposures were not significantly 

### 27 associated with personal PM 2.5 exposures in either summer or winter. These non



### 28 significant correlations may be attributed in part to the relatively low personal O 3 

### 29 exposures observed in this study; in both summer and winter, the mean personal O 3 

### 30 exposure was below the calculated limit of detection. 

### 31Draft

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- 15 September 2011 Studies conducted in Baltimore ( Sarnat et al., 2001 ) and Boston ( Sarnat et al., 2005 ) 

### 1 found differing results for the corr elation between 24

- h avg personal O 3 and personal 

### 2
PM 2.5 exposures, particularly during the winter season. Sarnat et al. ( 2001 ) found a 

### 3 positive slope when regressing personal exposures of both total PM 2.5 (0.21) and PM 2 .5 of 

### 4 ambient origin (0.22) against personal O 3 exposures during the summer season, but 

### 5 negative slopes (

- 0.05 and

- 0.18, respectively) during the winter season. The summertime 

### 6 slope for personal PM 2.5 exposure versus personal O 3 exposure was much higher f or 

### 7 children (0.37) than for adults (0.07), which may be the result of different activity 

### 8 patterns. This team of researchers also found a positive, although higher, summer slope 

### 9
between 24

- h avg personal O 3 and personal PM 2.5 in Boston (0.72) ( Sarnat et al., 2005 )

. 

### 10 However, the winter slope was positive (1.25) rather than negative, as in Baltimore. In 

### 11 both cities during both seasons, there was a wide range of subject

- specific correlations 

### 12 between personal O 3 and personal PM 2.5 exposures, with some subjects showing 

### 13 relatively strong positive correlations (>0.75) and others showing strong negative 

### 14 correlations (<

- 0.50). The median correlation in both cities was slightly positive in the 

### 15 summer and near zero (Boston) or slightly negative (Baltimore) in the winter. These 

### 16 results indicate the potential effects of city

- specific characteristics, such as housing stock 

### 17 and building ventilation patterns, on relationships between O 3 and co

- pollutants. 

### 18

4.3.4.2 Near

- Road Exposure to Ozone and Co

- pollutants

Beckerman et al. ( 2008 ) measured both 1

- week and continuous concentrations of O 3 , 

### 19 NO, NO 2 , NO X , PM 2.5 , PM 1.0 , and several VOCs (the BTEX compounds, MTBE, 

### 20 hexane, and THC) in the vicinity of heavily traveled (annual average daily traffic 

### 21 [AADT] >340,000) road ways in Toronto, Canada. Passive samplers were deployed for 

### 22 one week in August 2004. Ozone concentrations were negatively correlated with all 

### 23 pollutants, with the exception of VOCs at one of the monitoring sites which were 

### 24 suspected of being influenced by small area sources. Site specific correlations are given 

### 25 in Figure 4

- 2. Correlations were

- 0.77 to

- 0.85 for NO 2 , - 0.48 to

- 0.62 for NO, and

- 0.55 

### 26

to

- 0.63 for NO X . Pooled correlations using data from both sites were somewhat lower in 

### 27 magnitude. PM 2.5 and PM 1.0 correlations were

- 0.35 to

- 0.78 and

- 0.34 to

- 0.58, 

### 28 respectively. At the monitoring site not influenced by small area sources, O 3 - VOC 

### 29 correlations ranged from

- 0.41 to

- 0.66. 

### 30

Beckerman et al. ( 2008 ) also made on

- road m easurements of multiple pollutants with a 

### 31 instrumented vehicle. Concentrations were not reported, but correlations between O 3 and 

### 32 other pollutants were negative and somewhat greater in magnitude (i.e., more negative) 

### 33
than the near

- road correlations. SO 2 , C O, and BC were measured in the mobile 

### 34 laboratory, although not at the roadside, and they all showed negative correlations with 

### 35Draft

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- 16 September 2011 O 3 when the data were controlled for site. Correlations for continuous concentrations 

### 1
between O 3 and co

- pollutants were somewhat lower than the 1

- week correlations, except 

### 2
for O 3 - PM 2.5 correlations. Correlations were

- 0.90,

- 0.66,

- 0.77, and

- 0.89 for NO 2 , NO, 

### 3
NO X , and PM 1.0 respectively. The continuous O 3 - PM 2.5 correlation was

- 0.62, which is 

### 4 in the range of the 1

- week correlation. 

### 5

Source data from: Beckerman et al. ( 2008 )



### Figure 4

- 2 Correlations between 1

- week concentrations of O 3 

### and copollutants measured near roadways.

4.3.4.3 Indoor Exposure to Ozone and Co

- pollutants

Ambient O 3 that infiltrates indoor s reacts with organic compounds and other chemicals to 

### 6 form oxidized products, as described in Section 3.2.3 as well as the 2006 O 3 AQCD

. It is 

### 7 anticipated that individuals are exposed to these reaction products, although no evidence 

### 8 was identified regardi ng personal exposures. The reactions are similar to those occurring 

### 9 in the ambient air, as summarized in Chapter 3. For example, O 3 can react with terpenes 

### 10 and other compounds from cleaning products, air fresheners, and wood products both in 

### 11 the gas phase and on surfaces to form particulate and gaseous species, such as 

### 12 formaldehyde ( Chen et al., 2011 ; Shu and Morrison, 2011 ; Aoki and Tanabe, 2007 ; Reiss 

### 13 et al., 1995a ) . Ozone has also been shown to react with material trapped on HVAC filters 

### 14 and gen erate airborne products ( Bekö et al., 2007 ; Hyttinen et al., 2006 ) . Potential 

### 15 oxygenated reaction products have bee n found to act as irritants ( Anderson et al., 2007 )
, 

### 16 Copollutant

NO 2

NO

NO X

PM 2.5

PM 1.0

VOC

- 0.9

- 0.8

- 0.7

- 0.6

- 0.5

- 0.4

- 0.3

- 0.2

- 0.1 0

Pearson Correlation CoefficientDraft

– Do Not Cite or Quote 4

- 17 September 2011 indicating that these reaction products may have health effects separate from those of O 3 

### 1
itself ( Weschler and Shields, 1997 ) . Ozone may also react to form other oxidants, which 

### 2 then go on to participate in additional reactions. White et al. ( 2010 ) found evidence that 

### 3 HONO or other oxidants may have been present during experiments to estimate indoor 

### 4 OH concentrations, indicating complex indoor oxidant chemistry. Rates of these reactions 

### 5
are d ependent on indoor O 3 concentration, temperature, and air exchange rate, making 

### 6 estimation of exposures to reaction products difficult. 

### 7



## 4.4 Exposure

- Related Metrics

In this section, parameters are discussed that are relevant to the estimation of exposure, 

### 8
but are not themselves direct measures of exposure. Time

- location

- activity patterns, 

### 9 including behavioral changes to avoid exposure, have a substantial influence on exposure 

### 10 and dose. Proximity of populations to ambient monitors may influence how well their 

### 11

ex posure is represented by measurements at the monitors, although factors other than 

### 12 distance play an important role as well. 

### 13

4.4.1 Activity Patterns

The activity pattern of individuals is an important determinant of their exposure. 

### 14
Variation in O 3 concentrations among various microenvironments means that the amount 

### 15 of time spent in each location, as well as the level of activity, will influence an 

### 16
individual ’ s exposure to ambient O 3 . The effect of activity pattern on exposure is 

### 17 explicitly accounted for in Equati on 4

- 3 by the fraction of time spent in different 

### 18
microenvironments. 

### 19

Activity patterns vary both among and within individuals, resulting in corresponding 

### 20 variations in exposure across a population and over time. Large

- scale human activity 

### 21 databases, such a s those developed for the National Human Activity Pattern Survey 

### 22 (NHAPS) ( Klepeis et al., 2001 ) or the Consolidated Human Activity Database (CHAD) 

### 23 ( McCurdy et al., 2000 ) , which includes NHAPS data together with other activity study 

### 24 results, have been designed to characterize exposure patterns among much larg er 

### 25 population subsets than can be examined during individual panel studies. The complex 

### 26 human activity patterns across the population (all ages) are illustrated in Figure 4

- 3 

### 27 ( Klepeis et al. , 2001 ) , which is presented to illustrate the diversity of daily activities 

### 28 among the entire population as well as the proportion of time spent in each microen



### 29 vironment. For example, about 25% of the individuals reported being outdoors or in a 

### 30
vehicle between 2:00 and 3:00 pm, when daily O 3 levels are peaking, although about half 

### 31Draft

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- 18 September 2011 of this time was spent in or near a vehicle, where O 3 concentrations are likely to be lower 

### 1 than ambient concentrations. Different patterns would be anticipated when breaking 

### 2
d own activity patterns only for subgroups such as children or the elderly. Population 

### 3 exposures can be estimated using O 3 concentration data in each microenvironment. 

### 4

Longitudinal activity pattern information is also an important determinant of exposure, as 

### 5 different people may exhibit different patterns of time spent outdoors over time due to 

### 6 age, gender, employment, and lifestyle

- dependent factors. These differences may mani



### 7 fest as higher mean exposures or more frequent high

- exposure episodes some individ uals. 

### 8 The extent to which longitudinal variability in individuals contributes to the population 

### 9 variability in activity and location can be quantified by the ratio of between

- person 

### 10 variance to total variance in time spent in different locations and activi ties (the intraclass 

### 11 correlation coefficient, ICC). Xue et al. ( 2004 ) quantified ICC values in time

- activity data 

### 12 collected by Harvard University for 160 children aged 7

– 12 years in Southern California 

### 13 ( Geyh et al., 2000 ) . For time spent outdoors, the ICC was approximately 0.15, indicating 

### 14
that 15% of the variance in outdoor time was due to between

- person differenc es. The ICC 

### 15 value might be different for other population groups. 

### 16

Source: Reprinted with permission of Nature Publishing Group ( Klepeis et al., 2001 )

.


### Figure 4

- 3 Di stribution of time that NHAPS respondents spent in ten microenvironments based on smoothed 1

- min diary data.Draft

– Do Not Cite or Quote 4

- 19 September 2011 The EPA ’ s National Exposure Research Laboratory (NERL) has consolidated the 

### 1 majority of the most significant human activity databases into one com prehensive 

### 2 database called the Consolidated Human Activity Database (CHAD). The current version 

### 3 of CHAD contains data from nineteen human activity pattern studies (including NHAPS), 

### 4 which were evaluated to obtain over 33,000 person

- days of 24

- h human activ ities in 

### 5
CHAD ( McCurdy et al., 2000 ) . The surveys include probability

- based recall studies 

### 6 conducted by EPA and the Califo rnia Air Resources Board, as well as real

- time diary 

### 7 studies conducted in individual U.S. metropolitan areas using both probability

- based and 

### 8 volunteer subject panels. All ages of both genders are represented in CHAD. The data for 

### 9 each subject consist of o ne or more days of sequential activities, in which each activity is 

### 10 defined by start time, duration, activity type, and microenvironment classification (i.e., 

### 11 location). Activities vary from one minute to one hour in duration, with longer activities 

### 12 being subdivided into clock

- hour durations to facilitate exposure modeling. CHAD also 

### 13 provides information on the level of exertion associated with each activity, which can be 

### 14 used by exposure models to estimate ventilation rate and pollutant dose. 

### 15

4.4.2 Ozone Avertin g Behavior

Individuals can reduce their exposure to O 3 by altering their behaviors, such as reducing 

### 16 their time outdoors. To protect the public from O 3 - related health effects, EPA and 

### 17 organizations such as the American Lung Association recommend that peopl e spend 

### 18 more time indoors and engage in less strenuous activities on days with relatively high O 3 

### 19
concentrations. To assist individuals concerned about O 3 conditions, EPA developed the 

### 20 Air Quality Index (AQI). This index combines information about O 3 (and other pollutant) 

### 21 concentrations to produce five categories of air

- quality, ranging from good to very 

### 22 unhealthy. Forecasted and actual conditions typically are reported to the public during 

### 23 high

- O 3 months through local media outlets, using various versions of this air

- quality 

### 24 categorization scheme. These advisories explicitly state that children in general and 

### 25 children with asthma in particular are potentially sensitive to air pollution. Parents are 

### 26
advised to curtail children ’ s outdoor exertion to varying d egrees depending on the 

### 27 predicted pollution levels and whether their children have asthma or other relevant 

### 28
medical conditions. 

### 29

Evidence of individual averting behaviors in response to advisories has been found in 

### 30 several studies, especially for susceptib le populations, such as children, older adults, and 

### 31 asthmatics. Reduced time spent outdoors was reported in an activity diary study in 35 

### 32
U.S. cities ( Mansfield et al., 2006 ) , which found that asthmatic children who spent at 

### 33 least some time outdoors reduced their total time spent outdoors by an average of 30 min 

### 34
on a code red O 3 day relative to a code green, yellow, or orange day; however, the 

### 35Draft

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- 20 September 2011 authors noted that there was appreciable variation in both the overall amount of time 

### 1 spent outdoors and the reduction in outdoor time on high ozone days among asthmatic 

### 2
children. Br esnahan et al. ( 1997 ) examined survey data collected during 1985

- 86 from a 

### 3 panel of adults in the Los Angeles area, many of whom had compromised respiratory 

### 4 function, by an averting behavior model. A regression analysis indicated that individuals 

### 5 with smog

- related symptoms spent about 12 minutes less time outdoors over a two

- day 

### 6 period for each 10 ppb increase in O 3 concentration above 120 ppb. Considering that the 

### 7 average daily maximum O 3 concentration at the time was approximately 180 ppb on days 

### 8
when the then

- current standard (1

- h max of 120 ppb) was exceeded, this implies that 

### 9 those individuals spent about 40 minutes less time outsi de per day on a typical high O 3 

### 10 day compared to days with O 3 concentrations below the standard. However, the behavior 

### 11 was not specifically linked to exceedances or air quality alerts. 

### 12

The fraction of individuals who reduce time spent outdoors, or restrict their children ’ s 

### 13 outdoor activity, has been found to vary based on health status. In the Bresnahan et al. 

### 14 study ( 1997 ) , 40 percent o f respondents reported staying indoors on days when air quality 

### 15 was poor. Individuals who reported experiencing smog

- related symptoms were more 

### 16 likely to take the averting actions, although the presence of asthma or other chronic 

### 17 respiratory conditions did not significantly affect behavior. A study of parents of 

### 18
asthmatic children ( McDermott et al., 2006 ) suggests that parents are aware of the hazard 

### 19 of outdoor air pollution and the official alerts designed to protect them and their children. 

### 20 It also suggests that a majority of parents (55%) comply with recommendations of the 

### 21
alerts to restrict children ’ s outdoor activity, with more parents of asthmatics reporting 

### 22 awareness and responsiveness to alerts. However, only 7% of all parents complied with 

### 23
more than one

- third of the advisories issued ( McDermott et al., 2006 )

. Wen et al. ( 2009 ) 

### 24 analyzed data from the 2005 Behavioral Risk Factor Surveillance System (BRFSS) and 

### 25 indicated that people with lifetime asthma are about twice as likely as people without 

### 26 asthma to reduce their outdoor activities based on either media alerts of poor air quality 

### 27 (31% vs. 16%) or individual perception of air quality (26% vs. 12%). Respondents who 

### 28 had received advice from a health professional to reduce outdoor activity when air quality 

### 29 is poor were more likely to report a reduction based on media alerts, both for those with 

### 30 and without asthma. In a study of randomly selected individ uals in Houston, TX and 

### 31 Portland, OR, Semenza et al. ( 2008 ) found that a relatively small fraction of survey 

### 32 respondents (9.7% in Houston, 10.5% in Portland) changed their behaviors during poor 

### 33 air quality episodes. This fraction is appreciably lower than the fraction reported f or 

### 34 people with asthma in the Wen et al. ( 2009 ) study, although it is simil ar to the fraction 

### 35 reported in that study for those without asthma. Most of the people in the Semenza et al. 

### 36 ( 2008 ) study reported that their behavioral changes were motivated by self

- perception of 

### 37 poor air quality rather than an air quality advisory. It should be noted that the McDermott 

### 38Draft

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- 21 September 2011 et al. ( 2006 ) , Wen et al. ( 2009 ) , and Semenza et al. ( 2008 ) studies evaluated air quality in 

### 1 general and therefore are not necessarily specific to O 3 . 

### 2

Commuting behavior does not seem to change based on air quality alerts. A st udy in the 

### 3 Atlanta area showed that advisories can raise awareness among commuters but do not 

### 4 necessarily result in a change in an individual ’ s travel behavior ( Henry and Gordon, 

### 5
2003 ) . This finding is consistent with a survey for 1000 commuters in Denver, Colorado, 

### 6 which showed that the majority (76%) of commuters heard and understood the air quality 

### 7 advisories, but did not alter th eir commuting behavior ( Blanken et al., 2001 )

. 

### 8

Some evidence is available for other behavioral changes in response to air quality alerts. 

### 9 Approximately 40 percent of the respondents in the Los Angeles study by Bresnahan et 

### 10
al. ( 1997 ) limited or rearranged leisure activities, and 20 percent increased use of air 

### 11 conditioners. As with changes in time spent outdoors, individuals who reported 

### 12 experiencing smog

- related symptoms, but not those with asthma or chronic respiratory 

### 13 conditions, were more likely to take the averting actions. Other factors influencing 

### 14 behavioral changes, such as increased likelihood of averting behavior among high school 

### 15 graduates, are also reported in the study. In a separate Southern California study, 

### 16 attendance at two outdoor facilities (i.e. a zoo and an observatory) was reduced by 6

- 13% 

### 17 on days when smog alerts were announced, with greater decreases observed among 

### 18
children and older adults ( Neidell, 2010

, 2009 )

. 

### 19

The studies discussed in this section indicate that averting behavior is de pendent on 

### 20 several factors, including health status and lifestage. People with asthma and those 

### 21 experiencing smog

- related symptoms reduce their time spent outdoors and are more 

### 22 likely to change their behavior than those without respiratory conditions. Chil dren and 

### 23 older adults appear more likely to change their behavior than the general population. 

### 24 Commuters, even when aware of air quality advisories, tend not to change their 

### 25 commuting behavior. 

### 26

4.4.3 Population Proximity to Fixed

- Site Ozone Monitors

The distribu tion of O 3 monitors across urban areas varies between cities (Section 

### 27 3.6.2.1), and the population living near each monitor varies as well. Monitoring sites in 

### 28 rural areas are generally located in national or state parks and forests, and these monitors 

### 29

may be relevant for exposures of exercising visitors as well as those who live in similar 

### 30 locations. Rural monitors tend to be less affected than urban monitors by strong and 

### 31 highly variable anthropogenic sources of species participating in the formation and 

### 32
destruction of O 3 (e.g., onroad mobile sources) and more highly influenced by regional 

### 33 transport of O 3 or O 3 precursors (Section 3.6.2.2). This may contribute to less diel 

### 34Draft

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- 22 September 2011 variability in O 3 concentration than is observed in urban areas. It is not necessari ly true 

### 1 that proximity to a monitor determines the degree to which that monitor represents an 

### 2
individual ’ s ambient exposure, but proximity is one indicator. One way to calculate 

### 3 monitor representativeness is to calculate the fraction of the urban populatio n living 

### 4
within a certain radius of a monitor. Table 4

- 4 presents the fraction of the population in 

### 5 selected cities living within 1, 5, 10, and 20 km of an O 3 monitor. Values are presented 

### 6 for both total population and for those under 18 years of age, a po tentially susceptible 

### 7 population to the effects of O 3 . The data indicate that relatively few people live within 

### 8
1 km of an O 3 monitor, while nearly all of the population in most cities lives within 20 

### 9 km of a monitor. Many O 3 monitors are sited at “ neighbo rhood scale, ” intended to 

### 10 represent an area of the city with dimensions in the 0.5

- 4 km range (Section 3.5.6.1). 

### 11 Looking at the results for a 5

- km radius, generally 20

- 30% of the population lives within 

### 12
this distance from an O 3 monitor. Some cities have a greater population in this buffer, 

### 13
such as Salt Lake City, while others have a lower percentage, such as Minneapolis and 

### 14 Seattle. Percentages for children are generally similar to the total population, with no 

### 15
clear trend. 

### 16

Another approach is to divide the metropolitan area into sectors surrounding each 

### 17 monitor such that every person in the sector lives closer to that monitor than any other. 

### 18 This facilitates calculation of the fraction of the city ’ s population represented (according 

### 19 to proximity) by each monitor. In Atlanta, for example, the population fraction 

### 20 represented by each of the 11 monitors in the city ranged from 2.9

- 22%. The two 

### 21 monitors closest to the city center (sites A and B on Figure 3

- 24) accounted for 16% and 

### 22 8% of the population, respect ively. Site B has two listed monitoring objectives, highest 

### 23 concentration and population exposure. The other monitor in Atlanta with a listed 

### 24 objective of highest concentration is Site C, which represents the largest fraction of the 

### 25 population (22%). The e ight monitors with a primary monitoring objective of population 

### 26 exposure account for 2.9

- 17% of the population per monitor. 

### 27Draft

– Do Not Cite or Quote 4

- 23 September 2011 

### Table 4

- 4 Fraction of the 2009 population living within a specified distance of
an ozone monitor in selected U.S. cities

Populatio n Within 1 km Within 5 km Within 10 km Within 20 km
City Total <18 yr Total <18 yr Total <18 yr Total <18 yr Total <18 yr

Atlanta CSA 5,901,670 1,210,932 0.3% 0.3% 8% 9% 28% 29% 75% 77%

Baltimore CSA 8,421,016 1,916,106 1.3% 1.1% 25% 24% 57% 55% 89% 89%
Birmingham CSA 1,204,399 281,983 1.4% 1.6% 22% 24% 56% 59% 73% 74%

Boston CSA 7,540,533 1,748,918 0.9% 0.9% 17% 16% 49% 47% 85% 85%
Chicago CSA 9,980,113 2,502,454 1.5% 1.5% 28% 29% 63% 65% 89% 91%

Dallas CSA 6,791,942 1,530,877 0.4% 0.4% 13% 13% 45% 44% 87% 87%

Denver CSA 3,103,801 675,380 1.7% 1.6% 35% 36% 66% 68% 92% 93%

Detroit CSA 5,445,448 1,411,875 0.8% 0.9% 15% 17% 42% 44% 77% 78%

Houston CSA 5,993,633 1,387,851 1.5% 1.8% 26% 28% 54% 57% 83% 84%

Los Angeles CSA 18,419,720 4,668,441 1.6% 1.7% 28% 29% 77% 79% 98% 98%
Minneapolis CSA 3,652,490 872,497 0.3% 0.3% 5% 4% 16% 16% 57% 56%

New York CSA 22,223,406 5,284,875 1.5% 1.7% 23% 23% 51% 50% 91% 91%
Philadelphia CSA 6,442,836 1,568,878 0.9% 1.0% 22% 24% 55% 56% 89% 89%

Phoenix CBSA 4,393,462 873,084 2.0% 2.4% 35% 41% 74% 79% 96% 97%
Pittsburgh CSA 2,471,403 563,309 1.5% 1.4% 22% 21% 52% 50% 88% 88%

Salt Lake City CSA 1,717,045 460,747 3.0% 3.0% 41% 38% 79% 79% 95% 95%

San Antonio CBSA 2,061,147 484,473 0.5% 0.5% 12% 12% 42% 43% 78% 80%

San Francisco CSA 7,497,443 1,675,711 2.6% 2.9% 41% 40% 81% 81% 98% 98%

Seattle CSA 4,181,278 918,309 0.3% 0.3% 5% 5% 18% 16% 43% 39%

St. Louis CSA 2,914,754 720,746 1.3% 1.5% 17% 18% 52% 53% 80% 82%
Atlanta population fractions for children (<18 years of age) are similar to those for the 

### 1 general population, but other populations show a different pattern of monitor 

### 2 representativeness. Older adults (age 65 and up) were somewhat differently distributed 

### 3 with respect to the monitors, with most mon itors showing a difference of more than a 

### 4 percentage point compared to the general population. Based on 2000 population data, the 

### 5 fraction of older adults closest to the two city center monitors (A and B) was 4% higher 

### 6 and 2% lower, respectively, than the fraction for the population as a whole. Site C 

### 7 showed the highest differential, with 21% of the total population but only 15% of the 

### 8 older adult population. This indicates the potential for monitors to differentially represent 

### 9 potentially susceptible popul ations. 

### 10Draft

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- 24 September 2011 

## 4.5 Exposure Modeling

In the absence of personal exposure measurements, modeling techniques are used to 

### 1 estimate exposures, particularly for large populations for which individual

- level 

### 2 measurements would be impractical. Model estimates may be used as inputs to 

### 3 epidemiologic studies or as stand

- alone assessments of the level of exposure likely to be 

### 4 experienced by a population under certain air quality conditions. This section describes 

### 5 approaches used to improve exposure estimates, including concentrat ion surface 

### 6 modeling, which calculates local outdoor concentrations over a geographic area; air 

### 7 exchange rate modeling, which estimates building ventilation based on housing 

### 8 characteristics and meteorological parameters; and microenvironment

- based exposure 

### 9 modeling, which combines air quality data with demographic information and activity 

### 10 pattern simulations to estimate time

- weighted exposures based on concentrations in 

### 11 multiple microenvironments. These models each have strengths and limitations, as 

### 12
summari zed in Table 4

- 5. The remainder of this section provides more detail on specific 

### 13 modeling approaches, as well as results of applying the models. 

### 14

Table 4

- 5 Characteristics o f e xposure m odeling a pproaches

Model Type Model Description Strengths Limitations

Concentration Surface Spatial Interpolation (e.g., Inverse Distance Weighting, Kriging) Measured concentrations are interpolated across an area to yield local outdoor concentration estimates High concentration resolution; uses available data; requires low to moderate resources for implementation Spatial heterogeneity not fully captured; a single high

- concentration monitor can skew results; no location

- activity information Chemistry

- transport (e.g., CMAQ) Grid

- based O 3 concentrations are calculated from pr ecursor emis

sions, meteorology, and atmos

pheric chemistry and physics First

- principles characterization of physical and chemical processes influencing O 3 formation Grid cell resolution;

resource

- intensive; no location

- activity information

Land

- use regression (LUR) Merges concentration data with local

- scale variables such as land use factors to yield local concentration surface High concentration resolution Reactivity and small

- scale spatial variability of O 3 ; location

- specific, limiting generalizabi lity; no location

activity information Air Exchange Rate Mechanistic (LBL, LBLX) Uses database on building leak

age tests to predict AER based on building characteristics and meteorological variables (including natural ventilation in LBLX) Physical charac terization of driving forces for air exchange Moderate resource requirement; no location

activity information

Empirical Predicts AER based on factors such as building age and floor

area Low input data requirements Cannot account for meteorology; no location

activity information Integrated Microenvironmental Exposure and Dose Population (APEX, SHEDS) Stochastic treatment of air quality data, demographic variables, and activity pattern to generate estimates of microenvironmental concentrations, exposures, and doses Probabilistic estimates of exposure and dose distributions for specific populations; consideration of nonambient sources; small to moderate uncertainty for exercising asthmatic children (APEX) Resource

- intensive; evaluation with measur ed exposures; underestimation of multiple high

- exposure events in an individual (APEX)Draft

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- 25 September 2011 

### 4.5.1 Concentration Surface Modeling

One approach to improve exposure estimates in urban areas involves construction of a 

### 1 concentration surface over a geographic area, with the concentration at locations between 

### 2 monitors estimated using a model to compensate for missing data. The calculated O 3 

### 3 concentration surface can then be used to estimate exposures outside residences, schools, 

### 4 workplaces, roadways, or other locations of interest. This technique does not estimate 

### 5 exposure directly because it does not account for activity patterns or concentrations in 

### 6 different microenvironments. There are three main types of approaches: spatial 

### 7 interpolation of measured concentrations; st atistical models using meteorological 

### 8 variables, pollutant concentrations, and other predictors to estimate concentrations at 

### 9 receptors in the domain; and rigorous first

- principle models, such as chemistry

- transport 

### 10 models or dispersion models incorporatin g O 3 chemistry. Some researchers have 

### 11 developed models that combine these techniques. The models may be applied over urban, 

### 12 regional, or national spatial scales, and can be used to estimate daily concentrations or 

### 13 longer

- term averages. This discussion will focus on short

- term concentrations estimated 

### 14
across urban areas. 

### 15

The 2006 O 3 AQCD discussed concentration surface models, focusing on chemistry



### 16 transport models as well as geospatial and spatiotemporal interpolation techniques (e.g., 

### 17 Christakos and Vyas, 1998a

, b ; Georgopoulos et al., 1997 ) . Recent research has 

### 18

c ontinued to refine and extend the modeling approaches. A few recent papers have 

### 19 compared different approaches for the same urban area. 

### 20

Marshall et al. ( 2008 ) compared four spatial interpolation techniques for estimation of O 3 

### 21 concentrations in Vancouver, BC. The investigators assigned a daily average O 3 

### 22 concentration to each of the 51,560 postal

- code centroids using one of the follow ing 

### 23 techniques: (1) the concentration from the nearest monitor within 10 km; (2) the average 

### 24 of all monitors within 10 km; (3) the inverse

- distance

- weighted (IDW) average of all 

### 25 monitors in the area; and (4) the IDW average of the 3 closest monitors within 50 km. 

### 26 Method 1 (the nearest

- monitor approach) and Method 4 (IDW

- 50 km) had similar mean 

### 27
and median estimated annual

- and monthly

- average concentrations, although the 10th



### 28 90th percentile range was smaller for IDW

- 50. This is consistent with the averaging of 

### 29
extreme values inherent in IDW methods. The Pearson correlation coefficient between 

### 30 the two methods was 0.93 for monthly

- average concentrations and 0.78 for annual



### 31 average concentrations. Methods 2 and 3 were considered sub

- optimal and were excluded 

### 32
fr om further analysis. In the case of Method 2, a single downtown high

- concentration 

### 33 monitor skewed the results in the vicinity, partially as a result of the asymmetric layout of 

### 34 the coastal city of Vancouver. Method 3 was too spatially homogenous because it 

### 35 assigned most locations a concentration near the regional average, except for locations 

### 36Draft

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- 26 September 2011 immediately adjacent to a monitoring site. CMAQ concentration estimates using a 4 

### 1 km×4 km grid were also compared to the interpolation techniques in this study. Mean a nd 

### 2 median concentrations from CMAQ were approximately 50% higher than Method 1 and 

### 3
Method 4 estimates for both annual and monthly average concentrations. This may be 

### 4 due in part to the CMAQ grid size, which was too coarse to reveal near

- roadway 

### 5
decrements in O 3 concentration due to titration by NO. The IQR for the annual average 

### 6 was similar between CMAQ and the interpolation techniques, but the monthly average 

### 7 CMAQ IQR was approximately twice as large, indicating a seasonal effect. 

### 8

Bell ( 2006 ) compared CMAQ estimates for no rthern Georgia with nearest

- monitor and 

### 9 spatial interpolation techniques, including IDW and kriging. The area

- weighted 

### 10 concentration estimates from CMAQ indicated areas of spatial heterogeneity that were 

### 11 not captured by approaches based on the monitoring n etwork. The author concluded that 

### 12 some techniques, such as spatial interpolation, were not suitable for estimation of 

### 13 exposure in certain situations, such as for rural areas. Using the concentration from the 

### 14
nearest monitor resulted in an overestimation of exposure relative to model estimates. 

### 15

Land use regression (LUR) models have been developed to estimate levels of air 

### 16 pollutants, predominantly NO 2 , as a function of several land use factors, such as land use 

### 17 designation, traffic counts, home heating usage, point source strength, and population 

### 18 density ( Ryan and LeMasters, 2007 ; Gilliland et al., 2005 ; Briggs et al., 1997 ) . LUR, 

### 19 initially termed regression mapping ( Briggs et al., 1997 ) , is a regression derived from 

### 20
monitored concentrations as a function of data from a combination of the land use 

### 21 factors. The regression is then used for pred icting concentrations at multiple locations 

### 22 based on the independent variables at those particular locations without monitors. Hoek 

### 23
et al. ( 2008 ) warn of several limitations of LUR, including distinguishing real 

### 24 associations between pollutants and covariates from those of correlated co

- pollutants, 

### 25 limitations in spatial resolution from monitor data, applicability of the LUR model under 

### 26 changing temporal conditions, and introducti on of confounding factors when LUR is used 

### 27 in epidemiologic studies. These limitations may partially explain the lack of LUR models 

### 28 that have been developed for O 3 at the urban scale. Brauer et al. ( 2008 ) evaluated the use 

### 29
of LUR and IDW

- based spatial

- interpolation models in epidemiologic analyses for 

### 30 several different pollutants in Vancouver, BC and suggested that LUR is appropriate for 

### 31 directly

- emitted pollutants with high spatial variability, such as NO and BC, while IDW 

### 32 is appropriate for secondary pollutants such as NO 2 and PM 2.5 with less spatial 

### 33 variability. Although O 3 is also a secondary pollutant, its reactivity and high small

- scale 

### 34 spatial variability near high

- traffic roa dways indicates this conclusion may not apply for 

### 35
O 3 . 

### 36Draft

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- 27 September 2011 At a much larger spatial scale, EU

- wide, Beelen et al. ( 2009 ) compared a LUR model for 

### 1
O 3 with ordinary kriging and universal kriging, which incorporated meteorological, 

### 2 topographical, and land use v ariables to characterize the underlying trend. The LUR 

### 3 model performed reasonably well at rural locations (5

- km resolution), explaining a higher 

### 4 percentage of the variability (R 2 = 0.62) than for other pollutants. However, at the urban 

### 5 scale (1

- km resoluti on), only one variable was selected into the O 3 LUR model (high



### 6 density residential land use), and the R 2 value was very low (0.06). Universal kriging was 

### 7 the best method for the large

- scale composite EU concentration map, for O 3 as well as 

### 8
for NO 2 and PM 1 0 , with an R 2 value for O 3 of 0.70. The authors noted that these methods 

### 9 were not designed to capture spatial variation in concentrations that are known to occur 

### 10 within tens of meters of roadways (Section 3.6.2.1), which could partially explain poor 

### 11 model performance at the urban s cale. 

### 12

Titration of O 3 with NO emitted by motor vehicles tends to reduce O 3 concentrations 

### 13 near roadways. McConnell et al. ( 2006 ) developed a regression model to predict 

### 14
residential O 3 concentrations in southern Califo rnia using estimates of residential NO X 

### 15

c alculated from traffic data with the CALINE4 line source dispersion model. The authors 

### 16
estimated that local traffic contributes 18% of NO X concentrations measured in the study 

### 17 communities, with the remainder coming from regional background. Their regression 

### 18
model indicates that residential NO X reduces residential O 3 concentrations by 0.51 ppb 

### 19
O 3 per 1 ppb NO X , and that a 10th

- 90th percentile increase in local NO X results in a 

### 20
7.5 ppb decrease in local O 3 concentratio ns. This intra

- urban traffic

- related variability in 

### 21
O 3 concentrations suggests that traffic patterns are an important factor in the relationship 

### 22
between central site monitor and residential O 3 , and that differences in traffic density 

### 23
between the central si te monitor and individual homes could result in either an 

### 24
overestimate or underestimate of residential O 3 . 

### 25

A substantial number of researchers have used geostatistical methods and chemistry



### 26 transport models to estimate O 3 concentrations at urban, regional, national, and 

### 27 continental scales, both in the U.S. and in other countries (Section 3.3). In addition to 

### 28
short

- term exposure assessment for epidemiologic studies, such models may also be used 

### 29 for long

- term exposure assessment, O 3 forecasts, or evaluating e mission control 

### 30 strategies. It is difficult to determine the utility of these methods for exposure assessment; 

### 31 while improved local

- scale estimates of outdoor concentrations may contribute to better 

### 32 assignment of exposures, information on activity patterns is needed to produce estimates 

### 33 of personal exposure. 

### 34Draft

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- 28 September 2011 

### 4.5.2 Residential Air Exchange Rate Modeling

The residential air exchange rate (AER), which is the airflow into and out of a home, is 

### 1 an important mechanism for entry of ambient O 3 . As described in Section 4. 3.2, the 

### 2
indoor

- outdoor relationship is greatly affected by the AER. Since studies show that 

### 3 people spend approximately 66% of their time indoors at home ( Leech et al., 2002 ; 

### 4 Klepeis et al., 2001 ) , the residential AER is a critical parameter for exposure models, 

### 5 such as APEX, SHEDS, and EMI (discussed in Section 4.5.3) ( U.S. EPA, 2011b

, 2009b ; 

### 6 Burke et al., 2001 ) . Since the appropriate AER measurements may not be available for 

### 7 exposure models, mechanistic and empirical (i.e., regression

- based) AER models can be 

### 8 used for exposure assessments. The input data for the AER models can include building 

### 9 characteristics (e.g., age, number of stories, wind sheltering), occupant behavior (e.g., 

### 10 window opening), climatic region, and meteorology (e.g., local temperature and wind 

### 11 speed). Mechanistic AER models use these meteorol ogical parameters to account for the 

### 12 physical driving forces of the airflows due to pressure differences across the building 

### 13 envelope from wind and indoor

- outdoor temperature differences ( ASHRAE, 2009 )

. 

### 14 Empirical AER models do not consider the driving forces from the wind and indoor



### 15 outdoor temperature differences. Instead, a scaling constant can be used based on factors 

### 16 such as building age and floor area ( Chan et al., 2005b )

. 

### 17

Single

- zone mechanistic models represent a whole

- building as a single, well

- mixed 

### 18 compartment. These AER models, such as the Lawrence Berkeley Laborator y (LBL) 

### 19 model, can predict residential AER using input data from whole

- building pressurization 

### 20

tests ( Sherman and Grimsrud, 1980 ) , or leakage area models ( Breen et al., 2010 ; Sherman 

### 21 and McWilliams, 2007 ) . Recently, the LBL air infiltration model was linked with a 

### 22 leakage area model using population

- level census and residential survey data ( Sherman 

### 23 and McWilliams, 2007 ) and individual

- level questionnaire data ( Breen et al., 2010 ) . The 

### 24 LBL model, which predicts the AER from air i nfiltration (i.e., small uncontrollable 

### 25 openings in the building envelope) was also extended to include airflow from natural 

### 26 ventilation (LBLX), and evaluated using window opening data ( Breen et al., 2010 ) . The 

### 27 AER predictions from the LBL and LBLX models were compared to d aily AER 

### 28 measurements on seven consecutive days during each season from detached homes in 

### 29
central North Carolina ( Breen et al., 2010 ) . For the individual model

- predicted and 

### 30 measured AER, the median absolute difference was 43% (0.17 h - 1 ) and 40% (0.17 h - 1 ) for 

### 31
the LBL and L BLX models, respectively. Given the uncertainty of the AER 

### 32 measurements (accuracy of 20

- 25% for occupied homes), these results demonstrate the 

### 33 feasibility of using these AER models for both air infiltration (e.g., uncontrollable 

### 34 openings) and natural venti lation (e.g., window opening) to help reduce the AER 

### 35 uncertainty in exposure models. The capability of AER models could help support the 

### 36 exposure modeling needs, as described in Section 4.5.3, which includes the ability to 

### 37Draft

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- 29 September 2011 predict indoor concentrations of ambient O 3 that may be substantial for conditions of 

### 1 high AER such as open windows. 

### 2

4.5.3 Microenvironment

- Based Models

Population

- based methods, such as the Air Pollution Exposure (APEX) and Stochastic 

### 3 Human Exposure and Dose Simulation (SHEDS) integrated micro environmental 

### 4 exposure and dose models, involve stochastic treatment of the model inputs ( U.S. EPA, 

### 5
2009b ; Burke et al., 2001 ) . These are described in detail in the 2008 NO X ISA ( U.S. 

### 6 EPA, 2008b ) , in AX3.6.1. Stochastic models utilize distributions of pollutant

- related and 

### 7
individual

- level variables, such as ambient and local O 3 concentration contributions and 

### 8 breathing rate respectively, to compute t he distribution of individual exposures across the 

### 9 modeled population. The models also have the capability to estimate received dose 

### 10 through a dosimetry model. Using distributions of input parameters in the model 

### 11 framework rather than point estimates allow s the models to incorporate uncertainty and 

### 12 variability explicitly into exposure estimates ( Zidek et al., 2007 ) . These models estimate 

### 13
time

- weighted exposure for modeled individuals by sum ming exposure in each 

### 14 microenvironment visited during the exposure period. 

### 15

The initial set of input data for population exposure models is ambient air quality data, 

### 16 which may come from a monitoring network or model estimates. Estimates of 

### 17
concentrations i n a set of microenvironments are generated either by mass balance 

### 18 methods, which can incorporate AER models (Section 4.5.3), or microenvironmental 

### 19 factors. Microenvironments modeled include indoor residences; other indoor locations, 

### 20 such as schools, office s, and public buildings; and vehicles. The sequence of 

### 21 microenvironments and exertion levels during the exposure period is determined from 

### 22 characteristics of each modeled individual. The APEX model does this by generating a 

### 23 profile for each simulated indiv idual by sampling from distributions of demographic 

### 24 variables such as age, gender, and employment; physiological variables such as height 

### 25 and weight; and situational variables such as living in a house with a gas stove or air 

### 26 conditioning. Activity and loc ation (microenvironmental) patterns from a database such 

### 27 as CHAD are assigned to the simulated individual in a longitudinal manner, using age, 

### 28 gender, and biometric characteristics ( U.S. EPA, 2009a ; Glen et al., 2008 ) . Breathing 

### 29 rates for each individual are calculated for each activity based on predicted energy 

### 30 expenditures, and the corresponding received intake or blood dose may then be 

### 31 computed. APEX has an algorithm to estimate O 3 dose and changes in FEV 1 resulting 

### 32
from O 3 exposure. Summaries of individual

- and population

- level metrics are produced, 

### 33 such as maximum exposure or dose, number of individuals excee ding a specified 

### 34 exposure/dose, and number of person

- days at or above benchmark exposure levels. The 

### 35Draft

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- 30 September 2011 models also consider the nonambient contribution to total exposure. Nonambient source 

### 1 terms are added to the infiltration of ambient pollutants to calculat e the total 

### 2 concentration in the microenvironment. Output from model runs with and without 

### 3 nonambient sources can be compared to estimate the ambient contribution to total 

### 4 exposure and dose. 

### 5

Georgopoulos et al. ( 2005 ) used a version of the SHEDS model as the exposure 

### 6 component of a modeling framework known as MENTOR (Modeling Environment for 

### 7 Total Risk Studies) in a simulation of O 3 exposure in Philadelphia over a 2

- week period 

### 8 in July 1999. 500 individuals we re sampled from CHAD in each of 482 census tracts to 

### 9 match local demographic characteristics from U.S. Census data. Outdoor concentrations 

### 10 over the modeling domain were calculated from interpolation of photochemical modeling 

### 11
results and fixed

- site monitor concentrations. These concentrations were then used as 

### 12 input data for SHEDS. Median microenvironmental concentrations predicted by SHEDS 

### 13 for nine simulated microenvironments were strongly correlated with outdoor 

### 14 concentrations, a result consistent with the lack of indoor O 3 sources in the model. A 

### 15 regression of median microenvironmental concentrations against outdoor concentrations 

### 16 indicated that the microenvironmental concentrations were appreciably lower than 

### 17 outdoor concentrations (regression slope = 0.2 6). 95th percentile microenvironmental 

### 18
concentrations were also well correlated with outdoor concentrations and showed a 

### 19 regression slope of 1.02, although some microenvironmental concentrations were well 

### 20 below the outdoor values. This suggests that in mos t cases the high

- end concentrations 

### 21 were associated with outdoor microenvironments. Although the authors did not report 

### 22 exposure statistics for the population, their dose calculations also indicated that O 3 dose 

### 23 due to time spent outdoors dominated the upp er percentiles of the population dose 

### 24 distribution. They found that both the 50th and 95th percentile O 3 concentrations were 

### 25
correlated with census

- tract level outdoor concentrations estimated by photochemical 

### 26 modeling combined with spatiotemporal interpol ation, and attributed this correlation to 

### 27
the lack of indoor sources of O 3 . Relationships between exposure and concentrations at 

### 28
fixed

- site monitors were not reported. 

### 29

As part of the previous NAAQS review completed in 2008, EPA ’ s Office of Air Quality 

### 30
Plan ning and Standards used APEX

- O3 to estimate O 3 exposures in 12 cities during the 

### 31
O 3 monitoring seasons of 2002

- 04 and reported the results in the 2007 O 3 Staff Paper 

### 32 ( U.S. EPA, 2007b ) . Exposures were modeled for the general population, school

- age 

### 33 children (ages 5

- 18), and asthmatic school

- age children. Hourly air quality input data 

### 34
from monitors in each ci ty were adjusted to simulate just meeting various alternative 

### 35 standards, ranging from 65 to 85 ppb (8

- h average), to demonstrate the effect of different 

### 36
standards on O 3 exposure metrics. O 3 decay (i.e., reaction) in indoor microenvironments 

### 37 was modeled, bu t no indoor O 3 sources were included. 

### 38Draft

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- 31 September 2011 Results of the model runs indicated that children and asthmatic children had similar 

### 1 exposures, with the general population experiencing lower exposure. For example, in 

### 2 Boston using 2002 air quality data adjusted to me et the then

- current 8

- h standard of 0.08 

### 3 ppm (fourth

- highest maximum averaged over 3 yr), approximately 28% of children or 

### 4 asthmatic children were estimated to experience one or more 8

- h avg exposures of 70 ppb 

### 5 or greater during an 8

- h period in which they engaged in moderate exercise. In 

### 6 comparison, about 10% of the general population (including children) would experience a 

### 7 70 ppb

- 8h or greater exposure under the same conditions (Exhibit 2 and Figure 4

- 7 of the 

### 8 Staff Paper). A similar pattern was observed in other cities, although the magnitude of 

### 9 exposure was different. In most cases, exposures were substantially higher in 2002 than 

### 10 2004, with 2003 exposures in between the estimates for the other two years (Figure 4

- 8 of 

### 11 the Staff Paper). 

### 12

Exposures were qu ite variable across cities due primarily to differing air quality 

### 13 distributions that resulted in a differential result from the air quality adjustment 

### 14 procedure. For example, the same 74 ppb

- 8h (fourth maximum) alternative standard 

### 15
scenario for 2002 estima ted that 10% of Boston children but very few (<0.5%) of Los 

### 16 Angeles children experience exposures above 70 ppb

- 8h while engaged in moderate 

### 17 exertion. The relationship between the fourth

- highest concentrations (the basis for the air 

### 18 quality adjustment) and the remainder of the air quality distribution is quite different 

### 19 between the two cities, with the result that more of the upper range of the air quality data 

### 20 was rolled back in Los Angeles than in Boston. This substantially reduced the occurrence 

### 21
of modele d high

- end exposures. 

### 22

Simulations indicate that meeting O 3 air quality standards would reduce the fraction of 

### 23 individuals experiencing high

- end exposures, as expected. Using unadjusted 2004 air 

### 24 quality data (the lowest of the three years simulated), the es timate of the fraction of 

### 25 children experiencing a 60 ppb

- 8h exposure while engaging in moderate exertion ranged 

### 26 from 12% (Chicago) to 69% (Los Angeles). Adjusting air quality data to meet fourth



### 27 maximum alternative standards of 85, 75, and 65 ppb reduced t hat range to 1

- 26%, 0

- 

### 28 11%, and 0

- 1%, respectively (Exhibit 9 of the Staff Paper). 

### 29

An analysis has been conducted for the APEX model to evaluate the contribution of 

### 30 uncertainty in input parameters and databases to the uncertainty in model outputs 

### 31 ( Langstaff, 2007 ) . The Monte Carlo analysis indicates that the uncertainty in model 

### 32 exposure estimates for asthmatic children during moderate exercise is small to moderate, 

### 33 with 95% confidence intervals of at most ± 6 percentage points at exposures above 60, 

### 34 70, and 80 ppb (8

- h avg) However, APEX appears to substantially underestimate the 

### 35 frequency of multiple high

- exposure events for a single individual. The two ma in sources 

### 36 of uncertainty identified were related to the activity pattern database and the spatial 

### 37Draft

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- 32 September 2011 interpolation of fixed

- site monitor concentrations to other locations. One area of potential 

### 1 improvement in the activity pattern database is additional infor mation on children ’ s 

### 2 activities, including longitudinal patterns. Improved information on spatial variation of 

### 3
O 3 concentrations, including in near

- roadway and indoor microenvironments, would also 

### 4 contribute to reduced uncertainty. Another area of need is for improved personal 

### 5 exposure monitors with shorter averaging times to capture peak exposures and lower 

### 6 detection limits to capture low indoor concentrations. A similar modeling approach is 

### 7 currently being developed which is suitable for panel epidemiolog ic studies or for 

### 8 controlled human exposure studies, in which activity pattern data specific to the 

### 9 individuals in the study can be collected. Time

- activity data is combined with 

### 10 questionnaire data on housing characteristics, presence of indoor or personal sources, and 

### 11 other information to develop a personalized set of model input parameters for each 

### 12 individual. This model, the Exposure Model for Individuals, is under development by 

### 13
EPA ’ s National Exposure Research Laboratory ( U.S. EPA, 2011b ; Zartarian and Schultz, 

### 14
2010 )

. 

### 15



## 4.6 Implications for Epidemiologic Studies

Exposure measurement error, which refers to the uncertainty associated with using 

### 16

ex posure metrics to represent the actual exposure of an individual or population, can be 

### 17 an important contributor to variability in epidemiologic study results. Time

- series studies 

### 18 assess the daily health status of a population of thousands or millions of pe ople over the 

### 19 course of multiple years (i.e., thousands of days) across an urban area by estimating their 

### 20 daily exposure using a short monitoring interval (hours to days). In these studies, the 

### 21 community

- averaged concentration of an air pollutant measured at central

- site monitors 

### 22 is typically used as a surrogate for individual or population ambient exposure. In 

### 23 addition, panel studies, which consist of a relatively small sample (typically tens) of 

### 24 study participants followed over a period of days to months, have been used to examine 

### 25
the health effects associated with short

- term exposure to ambient concentrations of air 

### 26 pollutants ( Delfino et al., 1996 ) . Panel studies may also apply a microenvironmental 

### 27 model to represent exposure to an air pollutant. A longitudinal cohort epidemiologic 

### 28 study, such as the A CS cohort study, typically involves hundreds or thousands of subjects 

### 29 followed over several years or decades ( Jerrett et al., 2009 ) . Concentrations are generally 

### 30 aggregated over time and by community to estimate exposures. 

### 31

Exposure error can under

- or ov er

- estimate epidemiologic associations between ambient 

### 32 pollutant concentrations and health outcomes by biasing effect estimates toward or away 

### 33 from the null. Exposure misclassification can also tend to obscure the presence of 

### 34 thresholds for health effects, as demonstrated by a simulation study of nondifferential 

### 35Draft

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- 33 September 2011 exposure misclassification ( Brauer et al., 2002 ) . The importance of exposure 

### 1 misclassification varies with study design and is dependent on the spatial and temporal 

### 2 aspects of the design. For example, the use of a community

- averaged O 3 concentration in 

### 3
a time

- seri es epidemiologic study may be adequate to represent the day

- to

- day temporal 

### 4 concentration variability used to evaluate health effects, but may not capture differences 

### 5 in the magnitude of exposure due to spatial variability. Other factors that could influen ce 

### 6 exposure estimates include nonambient exposure, topography of the natural and built 

### 7 environment, meteorology, measurement errors, use of ambient O 3 concentration as a 

### 8 surrogate for ambient O 3 exposure, and the presence of O 3 in a mixture of pollutants. The 

### 9 following sections will consider various sources of error and how they affect the 

### 10 interpretation of results from epidemiologic studies of different designs. 

### 11

4.6.1 Nonambient Ozone Exposure

For other criteria pollutants, nonambient sources can be an important contributor to total 

### 12 personal exposure. There are relatively few indoor sources of O 3 ; as a result, personal O 3 

### 13 exposure is expected to be dominated by ambient O 3 in outdoor microenvironments and 

### 14 in indoor microenvironments with high air exchange rates (e.g., with open windows). 

### 15 Even in microenvironments where nonambient exposure is substantial, such as in a room 

### 16
with an O 3 generator, this nonambient exposure is un likely to be temporally correlated 

### 17
with ambient O 3 exposure ( Wilson and Suh, 1997 ) , and therefore would not affect 

### 18 epidemiologic associations between O 3 and a health effect ( Sheppard et al., 2005 ) . In 

### 19 simulations of a nonreactive pollutant, Sheppard et al. ( 2005 ) concluded that nonambient 

### 20 exposure does not influence the health outcome effect estimate if ambient and 

### 21

n onambient concentrations are independent. Since personal exposure to ambient O 3 is 

### 22 some fraction of the ambient concentration, it should be noted that effect estimates 

### 23 calculated based on personal exposure rather than ambient concentration will be 

### 24
increase d in proportion to the ratio of ambient concentration to ambient exposure, and 

### 25 daily fluctuations in this ratio can widen the confidence intervals in the ambient 

### 26 concentration effect estimate, but uncorrelated nonambient exposure will not bias the 

### 27
effect e stimate. 

### 28

4.6.2 Spatiotemporal Variability

Spatial and temporal variability in O 3 concentrations can contribute to exposure error in 

### 29 epidemiologic studies, whether they rely on central

- site monitor data or concentration 

### 30 modeling for exposure assessment. Spatial variability in the magnitude of concentrations 

### 31 may affect cross

- sectional and large

- scale cohort studies by undermining the assumption 

### 32Draft

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- 34 September 2011 that intra

- urban concentration and exposure differences are less important than inter



### 1 urban differences. This issue may b e less important for time

- series studies, which rely on 

### 2 day

- to

- day temporal variability in concentrations to evaluate health effects. Low inter



### 3 monitor correlations contribute to exposure error in time

- series studies, including bias 

### 4
toward the null and inc reased confidence intervals. 

### 5

The averaging time of the daily exposure metrics used to evaluate daily aggregated health 

### 6 data (e.g., 1

- h or 8

- h daily maximum vs. 24

- h avg concentration) may also impact 

### 7 epidemiologic results, since different studies report di fferent daily metrics. Correlations 

### 8
between 1

- h daily max, 8

- h daily max, and 24

- h avg concentrations for U.S. monitoring 

### 9 sites are presented in Section 3.6.1 (Figure 3

- 18 and accompanying text). The two daily 

### 10 peak values (1

- h max and 8

- h max) are well cor related, with a median (IQR) correlation 

### 11 of 0.97 (0.96

- 0.98). The correlation between the 8

- h max and 24

- h avg are somewhat less 

### 12 well correlated with a median (IQR) correlation of 0.89 (0.86

- 0.92). While this may 

### 13 complicate quantitative comparisons between epidemiologic studies using different daily 

### 14 metrics, as well as the interpretation of studies using metrics other than the current 8

- h 

### 15 standard, the high inter

- metric correlations suggest it is a relatively small source of 

### 16 uncertainty in comparing the res ults of studies using different metrics. This is supported 

### 17 by a study comparing each of these metrics in a time

- series study of respiratory ED visits 

### 18 ( Darrow et al., 2011b ) , which found positive associations for all metrics, with the 

### 19 strongest association for the 8

- h daily max exposure metric ( Section 6.7.3.2). 

### 20

The ratios of 1

- h daily max, 8

- h daily max, and 24

- h avg concentrations to one another 

### 21
have been found to differ across communities and across time within individual 

### 22
communities ( Anderson and Bell , 2010 ) . For example, 8:24 hour ratios ranged from 1.23

- 

### 23 1.83, with a median of 1.53. Lower ratios were generally observed in the spring and 

### 24 summer compared to fall and winter. O 3 concentration was identified as the most 

### 25 important predictor of ozone metr ic ratios, with higher overall O 3 concentrations 

### 26 associated with lower ratios. In communities with higher long

- term ozone concentrations, 

### 27
the low 8:24 h our ratio is attributed to high baseline O 3 , which results in elevated 24

- h 

### 28 average values. Differences in the representativeness of O 3 metrics introduces uncertainty 

### 29 into epidemiologic results and complicates comparison of studies using different metrics. 

### 30 Preferably, studies will report results using multiple metrics. In cases where this does not 

### 31 occur, the results of this study can inform the uncertainty associated with using a standard 

### 32 increment to adjust effect estimates based on different metrics so that they are 

### 33 comparable (Chapter 6). 

### 34

A study compared measures of spatial and temporal variability for 1

- h daily max and 24

- 

### 35 h daily avg O 3 concentrations in Brazil ( Bravo and Bell, 2011 ) . The 1

- h daily max value 

### 36 was found to have higher correlation between monitors (i.e., lower temporal variability) 

### 37Draft

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- 35 September 2011 and lower COD (a measure of spatiotemporal variability which incorporates differences 

### 1 in concentration magnitude, with lower values indicatin g lower variability; see Chapter 3) 

### 2
than the 24

- h avg value. The range of correlation coefficients and COD values was 

### 3 similar between the two metrics, although the variation was lower for the 1

- h daily max, 

### 4 as indicated by the R 2 value for the regression o f correlation coefficient on inter

- monitor 

### 5
distance. 

### 6

Long

- term exposure epidemiologic studies use concentrations averaged over months, 

### 7 years, or decades to evaluate health effects of extended O 3 exposure. A study in Canada 

### 8 comparing exposure assessment met hods for long

- term O 3 exposure found that the 

### 9 annual average concentration in the census tract of a subject ’ s residence during 1980 and 

### 10
1994 was well

- correlated (0.76 and 0.83, respectively) with a concentration metric 

### 11 accounting for movement among census subdivisions during 1980

- 2002 ( Guay et al., 

### 12
2011 ) . This may have been due in part to a relatively low rate of movement, with subjects 

### 13 residing on average for 71% of the 22

- year period in the same census subdivision they 

### 14

were in during 1980. This suggests that an exposure metric based on a single year can 

### 15 represent exposure over a multi

- decade period. 

### 16

4.6.2.1 Spatial Variability

Spatial variability of O 3 concentrations is highly dependent on spatial scale; in effect, O 3 

### 17 is a regional pollutant subject to varying degrees of local variability. In the immediate 

### 18 vicinity of roadways, O 3 concentrations are reduced due to reaction with NO and other 

### 19 species (Section 4.3.4.2); over spatial scales of a few kilometers, O 3 may be m ore 

### 20 homogeneous due to its formation as a secondary pollutant; over scales of tens of 

### 21 kilometers, atmospheric processing can result in higher concentrations downwind of an 

### 22
urban area than in the urban core. Local

- scale variations have a large impact on the 

### 23 relative magnitude of concentrations among urban monitors, while conditions favoring 

### 24 high or low rates of O 3 formation (e.g., temperature) vary over large spatial scales. This 

### 25 suggests that neighborhood monitors are likely to track one another temporally, but miss 

### 26
small

- scale spatial variability in magnitude. In rural areas, a lower degree of fluctuation 

### 27
in O 3 precursors such as NO and VOCs is likely to make the diel concentration profile 

### 28 less variable than in urban areas, resulting in more sustained ambie nt levels. Spatial 

### 29 variability contributes to exposure error if the ambient O 3 concentration measured at the 

### 30 central site monitor is used as an ambient exposure surrogate and differs from the actual 

### 31
ambient O 3 concentration outside a subject ’ s residence an d/or worksite (in the absence of 

### 32
indoor O 3 sources). Averaging data from a large number of samplers will dampen 

### 33 intersampler variability, and use of multiple monitors over smaller land areas may allow 

### 34 for more variability to be incorporated into an epidemi ologic analysis. 

### 35Draft

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- 36 September 2011 Community exposure may not be well represented when monitors cover large areas with 

### 1 several subcommunities having different sources and topographies, such as the 

### 2
Los Angeles CSA (Section 3.6.2.1). Ozone monitors in Los Angeles had a much wider 

### 3 range of intermonitor correlations (

- 0.06 to 0.97) than Atlanta (0.61 to 0.96) or Boston 

### 4 (0.56 to 0.97) using 2007

- 2009 data. Although the negative and near

- zero correlations in 

### 5
Los Angeles were observed for monitors located some distance apart (>150 km), some 

### 6 closer monitor pairs had low positive correlations, likely due to changes in topography 

### 7 and airflow patterns over short distances. Lower COD values, which indicate less 

### 8 variability among monitors in the magnitude of O 3 concentrations, were obser ved in 

### 9 Atlanta (0.05

- 0.13) and Boston (0.05

- 0.19) than Los Angeles (0.05

- 0.56), although a 

### 10 single monitor (AM) was responsible for all Los Angeles COD values above 0.40. The 

### 11 spatial and temporal variability in O 3 concentration in 24 MSAs across the U.S. wa s also 

### 12
examined in the 2006 O 3 AQCD by using Pearson correlation coefficients, values of the 

### 13 90th percentile of the absolute difference in O 3 concentrations, and CODs. No clear 

### 14 discernible regional differences across the U.S. were found in the ranges of pa rameters 

### 15 analyzed. 

### 16

An analysis of the impact of exposure error due to spatial variability and instrument 

### 17 imprecision on time

- series epidemiologic study results indicated that O 3 has relatively 

### 18 low exposure error compared to other routinely monitored pollu tants, and that the 

### 19 simulated impact on effect estimates is minor. Goldman et al. ( 2011 ) computed 

### 20 population

- weighted scaled semivariances and Pearson correlation coefficients for daily 

### 21 concentration metrics of twelve pollutants measured at multiple central

- site monitors in 

### 22
Atlanta. 8

- h daily max O 3 exhibited the lowest semiv ariance and highest correlation of 

### 23 any of the pollutants. Although this indicates some degree of urban

- scale homogeneity 

### 24
for O 3 , the analysis did not account for near

- road effects on O 3 concentrations. 

### 25

Studies evaluating the influence of monitor selection on epidemiologic study results have 

### 26
found that O 3 effect estimates are similar across different spatial averaging scales and 

### 27 monitoring sites. A study in Italy compared approaches for using fixed

- site monitoring 

### 28
data in a case

- crossover epidemiologic stud y of daily O 3 and mortality ( Zauli Sajani et 

### 29 al., 2011 ) . O 3 effect estimates were found to be similar whether the nearest monitor was 

### 30 used, or whether single

- city, three

- city, or six

- city regional averages were used for 

### 31 exposure assessment. In contrast, effect estimates for PM 10 and NO 2 increased with 

### 32 increasing s cale of spatial averaging. Confidence intervals increased with increasing 

### 33 spatial scale for all pollutants. The authors attributed the consistency of O 3 effect 

### 34 estimates to the relative spatial homogeneity of O 3 over multi

- km spatial scales, and 

### 35 pointed to the high (0.85

- 0.95) inter

- monitor correlations to support this. The use of 

### 36 background monitors rather than monitors influenced by local sources in this study 

### 37 suggests that local

- scale spatial variation in O 3 , such as that due to titration by traffic 

### 38Draft

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- 37 September 2011 emis sions, was not captured in the analyses. Sarnat et al. ( 2010 ) studied the spatial 

### 1 variability of O 3 , along with PM 2.5 , NO 2 , and CO, in the Atlanta, GA, metropolitan area 

### 2 and evaluated how spatial variability affects interpretation of epidemiologic results, using 

### 3
time

- series data for circulatory disease ED visits. The authors found that associations 

### 4
with ambient 8

- h daily maximum O 3 concentration were similar among all sites tested, 

### 5 including multiple urban sites and a rural site some 38 miles from the city center. This 

### 6
result was also observed for 24

- h PM 2.5 concentrations. In contrast, hourly CO and NO 2 

### 7 showed different associations for the rural site than the urban sites, although the urban 

### 8 site associations were similar to one another for CO. This suggests that the choice of 

### 9 monitor may have little im pact on the results of O 3 time

- series studies, consistent with 

### 10 the moderate to high inter

- monitor correlations observed in Atlanta (Chapter 3). 

### 11

One potential explanation for this finding from the study by Sarnat et al. ( 2010 ) is that 

### 12 although spatial variability at different scales contributes to a com plicated pattern of 

### 13 variations in the magnitude of O 3 concentrations between near

- road, urban core, and 

### 14 urban downwind sites, day

- to

- day fluctuations in concentrations may be reflected across 

### 15 multiple urban microenvironments. In addition, time

- averaging of O 3 and PM 2.5 

### 16 concentrations may smooth out some of the intra

- day spatial variability observed with the 

### 17 hourly CO and NO 2 concentrations. However, some uncertainty in observed effect 

### 18 estimates due to spatial variability and associated exposure error is exp ected to remain, 

### 19 including a potential bias towards the null. 

### 20

4.6.2.2 Seasonality

The relationship between personal exposure and ambient concentration has been found to 

### 21 vary by season, with at least three factors potentially contributing to this variation: 

### 22
differ ences in building ventilation (e.g., air conditioning or heater use versus open 

### 23 window ventilation), higher O 3 concentrations during the O 3 season contributing to 

### 24 increased exposure and improved detection by personal monitors; and changes in activity 

### 25
patte rn resulting in more time spent outside. Evidence has been presented in studies 

### 26 conducted in several cities regarding the effect of ventilation on personal

- ambient and 

### 27
indoor

- outdoor O 3 relationships (see Sections 4.3.2 and 4.3.3). More limited evidence is 

### 28 available regarding the specific effects of O 3 detection limits and activity pattern changes 

### 29
on O 3 relationships. 

### 30

Several studies have found increased summertime correlations or ratios between personal 

### 31 exposure and ambient concentration ( Sarnat et al., 2005 ; Sarnat et al., 2000 ) or between 

### 32
indoor and outdoor O 3 concentrations ( Geyh et al., 2000 ; Avol et al., 1998b ) . However, 

### 33 others have found higher ratios in fall than in summer ( Sarnat et al., 2006b ) or equivalent, 

### 34Draft

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- 38 September 2011 near

- zero ratios in winter and summer ( Sarnat et al., 2001 ) , possibly because summertime 

### 1 use of air conditioners decreases building air exchange rates. It should be noted that O 3 

### 2

concentr ations during winter are generally much lower than summertime concentrations, 

### 3 possibly obscuring wintertime relationships due to detection limit issues. Studies 

### 4 specifically evaluating the effect of ventilation conditions on O 3 relationships have found 

### 5
inc reased correlations or ratios for individuals or buildings experiencing higher air 

### 6 exchange rates ( Sarnat et al., 2006b ; Geyh et al., 2000 ; Sarnat et al., 2000 ; Romieu et al., 

### 7
1998b )

. 

### 8

Increased correlations or ratios between personal exposure and ambient concentration, or 

### 9 between indoor and outdoor concentration, are likel y to reduce error in exposure 

### 10 estimates used in epidemiologic studies. This suggests that studies conducted during the 

### 11
O 3 season or in periods when communities are likely to have high air exchange rates 

### 12 (e.g., during mild weather) may be less prone to expo sure error than studies conducted 

### 13 only during winter. Year

- round studies that include both the O 3 and non

- O 3 seasons may 

### 14 have an intermediate level of exposure error. 

### 15

4.6.3 Exposure to Co

- pollutants and Ozone Reaction Products

Although indoor O 3 concentrations a re usually well below ambient concentrations, the 

### 16
same reactions that reduce O 3 indoors form particulate and gaseous species, including 

### 17 other oxidants, as summarized in Section 4.3.4.3. Exposures to these reaction products 

### 18 would therefore be expected to be correlated with ambient O 3 concentrations, although 

### 19 no evidence was identified regarding personal exposures. Such exposure could 

### 20 potentially contribute to health effects observed in epidemiologic studies. 

### 21

4.6.4 Averting Behavior

As described in Section 4.4.2, s everal recent studies indicate that some populations alter 

### 22 their behavior on high ozone days to avoid exposure. Such behavioral responses to 

### 23 information about forecasted air quality may introduce systematic measurement error in 

### 24 air pollution exposure, lead ing to biased estimates of the impact of air pollution on health. 

### 25 For example, studies have hypothesized that variation in time spent outdoors may be a 

### 26 driving factor behind the considerable heterogeneity in ozone mortality impacts across 

### 27
communities ( Bell et al., 2004 ) . If averting behavior in fact results in smaller, in 

### 28 magnitude, effect estimates, then studies that do not account for averting behavior may 

### 29 produce effect estimates that are biased towards the null (Section 6.2.7.5). 

### 30Draft

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- 39 September 2011 This is supported by an epidemiologic study that e xamined the association between 

### 1 exposure to ambient ozone concentrations and asthma hospitalizations in Southern 

### 2
California during 1989

- 1997 , which indicate s that controlling for avoidance behavior 

### 3
increases the effect estimate for both children and older adults, but not for adults aged 20

- 

### 4
64 ( Neidell and Kinney, 2010 ; Neidell, 2009 ) . Figure 4

- 4 and Figure 4

- 5, reproduced 

### 5
from Neidell ( 2009 ) , show covariate

- adjusted asthma hospital admissions as a function of 

### 6 daily maximum 1

- h O 3 concentration for all days (gray line) and days when no O 3 alert 

### 7 was issued (black line). Stage 1 smog alerts were issued by the State of California for 

### 8 days when ambient O 3 concentrations were forecast to be above 0.20 ppm; however, the 

### 9
concentration

- response functions are based on measured O 3 concentrations. For children 

### 10 aged 5

- 19 (Figure 4

- 4), hospital admissions were higher on high

- O 3 days when no ale rt 

### 11 was issued, especially on days with O 3 concentrations above 0.15 ppm (150 ppb). The 

### 12
concentration

- response curves for all days and days with no alert diverge at measured O 3 

### 13 concentrations between 0.10 and 0.15 ppm because smog alerts begin to be issued more 

### 14 frequently in this range. This suggests that in the absence of information that would 

### 15 enable averting behavior, children experience higher ozone exposure and subsequently a 

### 16 greater number of asthma hospital admissions than on alert days with similar O 3 

### 17 concentrations. The lower rate of admissions observed when alert days were included in 

### 18 the analysis suggests that averting behavior reduced O 3 exposure and asthma hospital 

### 19 admissions. In both cases, O 3 was found to be associated with asthma hospital 

### 20
admi ssions, although the strength of the association is underestimated when not 

### 21 accounting for averting behavior. A similar result was not observed when examining 

### 22 associations for adults aged 20

- 64 (Figure 4

- 5), who had similar rates of hospital 

### 23
admissions on non

- alert days as on all days. The lack of change for adults aged 20

- 64, 

### 24 which is primary employment age, may reflect lower response to air quality alerts due to 

### 25 the increased opportunity cost of behavior change. The finding that air quality 

### 26
information re duces the daily asthma hospitalization rate in these populations provides 

### 27 additional support for a link between ozone and health effects. 

### 28Draft

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- 40 September 2011 Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisco nsin Press ( Neidell, 2009 )



### Figure 4

- 4 Adjusted a sthma h ospital a dmissions by a ge on l agged o zone by
a lert s tatus, a ges 5

- 19.

Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Pres s; Neidell, ( 2009 )



### Figure 4

- 5 Adjusted a sthma h ospital a dmissions by a ge on l agged o zone by
a lert s tatus, a ges 20

- 64Draft

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- 41 September 2011 

### 4.6.5 Exposure Estimation Methods in Epidemiologic Studies

The use of O 3 measurements from central ambient monitoring sites is the most common 

### 1 method for assigning exposure in epidemiologic studies. However, fixed

- site 

### 2

measurem ents do not account for the effects of spatial variation in O 3 concentration, 

### 3
ambient and non

- ambient concentration differences, and varying activity patterns on 

### 4 personal exposures ( Brown et al., 2009 ; Chang et al., 2000 ; Zeger et al., 2000 ) . T he use 

### 5
of fixed

- site concentrations results in minimal exposure error when: (1) O 3 

### 6 concentrations are uniform across the region; (2) personal activity patterns are similar 

### 7 across the population; and (3) housing characteristics, such as air exchange rate an d 

### 8 indoor reaction rate, are constant over the study area. Since these factors vary by location 

### 9 and population, there will be errors in the magnitude of total exposure based solely on 

### 10 ambient monitoring data. 

### 11

Modeling approaches can also be used to estimate exposures for epidemiologic studies, 

### 12 as discussed in Section 4.5. Geostatistical spatial interpolation techniques can provide 

### 13
finer

- scale estimates of local concentration over urban areas. A microenvironment al 

### 14 modeling approach simulates exposure using empirical distributions of concentrations in 

### 15 specific microenvironments together with human activity pattern data. The main 

### 16 advantage of the modeling approach is that it can be used to estimate exposures over a 

### 17 wide range of population and scenarios. A main disadvantage of the modeling approach 

### 18 is that the results of modeling exposure assessment must be compared to an independent 

### 19 set of measured exposure levels ( Klepeis, 1999 )

. In addition, resource

- intensive 

### 20 development of validated and representative model inputs is required, such as human 

### 21 activity patterns, distributions of air exchange rate, and deposition rate. Therefore, 

### 22 modeled exposures are used much less frequently in e pidemiologic studies. 

### 23



## 4.7 Summary and Conclusions

This section will briefly summarize and synthesize the main points of the chapter, with 

### 24 particular attention to the relevance of the material for the interpretation of epidemiologic 

### 25
studies. 

### 26

Passive badge samplers are the most widely used technique for measuring personal O 3 

### 27 exposure (Section 4.3.1). The detection limit of the badges for a 24

- h sampling period is 

### 28 approximately 5

- 10 ppb, with lower detection limits at longer sampling durations. In low



### 29

concent ration conditions this may result in an appreciable fraction of 24

- h samples being 

### 30 below the detection limit. The use of more sensitive portable active monitors, including 

### 31Draft

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- 42 September 2011 some that have recently become available, may help overcome this issue and improve 

### 1

p ersonal monitoring in the future. 

### 2

Since there are relatively few indoor sources of O 3 , indoor O 3 concentrations are often 

### 3 substantially lower than outdoor concentrations due to reactions of O 3 with indoor 

### 4 surfaces and airborne constituents (Section 4.3.2). Air exchange rate is a key determinant 

### 5 of the I/O ratio, which is generally in the range of 0.1

- 0.4 (Table 4

- 1), with some 

### 6 evidence for higher ratios during the O 3 season when concentrations are higher. 

### 7

Personal exposure is moderately correlated with ambi ent O 3 concentration, as indicated 

### 8 by studies reporting correlations generally in the range of 0.3

- 0.8 (Table 4

- 2). Hourly 

### 9 concentration correlations are more variable than those averaged over 24 hours or longer, 

### 10
with correlations in outdoor microenvironme nts (0.7

- 0.9) much higher than those in 

### 11 residential indoor (0.1) or other indoor (0.3

- 0.4) microenvironments. Some studies report 

### 12 substantially lower personal

- ambient correlations, a result attributable in part to low air 

### 13 exchange rate and O 3 concentration s below the sampler detection limit, conditions often 

### 14 encountered during wintertime. Low correlations may also occur for individuals or 

### 15 populations spending increased time indoors. 

### 16

The ratio between personal exposure and ambient concentration varies widel y depending 

### 17 on activity patterns, housing characteristics, and season, with higher personal

- ambient 

### 18 ratios generally observed with increasing time spent outside, higher air exchange rate, 

### 19 and in seasons other than winter (Table 4

- 3). Personal

- ambient ratio s are typically 0.1

- 

### 20 0.3, although individuals spending substantial time outdoors (e.g., outdoor workers) may 

### 21 have much higher ratios (0.5

- 0.9). Thus, applying personal

- ambient ratios for outdoor 

### 22 workers to the general population or susceptible populations spending substantial time 

### 23 indoors can result in overestimates of the magnitude of personal exposure for these 

### 24

groups. 

### 25

Personal exposure to other pollutants shows variable association with personal exposure 

### 26
to O 3 , with differences in copollutant relationshi ps depending on factors such as season, 

### 27 city

- specific characteristics, activity pattern, and spatial variability of the copollutant 

### 28 (Section 4.3.4). In near

- road and on

- road microenvironments, correlations between O 3 

### 29
and traffic

- related pollutants are mode rately to strongly negative, with the most strongly 

### 30 negative correlations observed for NO 2 (

- 0.8 to

- 0.9). This is consistent with the 

### 31 chemistry of NO oxidation, in which O 3 is consumed to form NO 2 . The more moderate 

### 32 negative correlations observed for PM 2. 5 , PM 1.0 , and VOC may reflect reduced 

### 33
concentrations of O 3 in polluted environments due to other scavenging reactions. A 

### 34 similar process occurs indoors, where infiltrated O 3 reacts with airborne or surface



### 35 associated materials to form secondary compounds, such as formaldehyde. Although such 

### 36Draft

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- 43 September 2011 reactions decrease indoor O 3 exposure, they result in increasing exposure to other species 

### 1 which may themselves have health effects. 

### 2

Variations in ambient O 3 concentrations occur over multiple spatial and temporal scales

. 

### 3 Near roadways, O 3 concentrations are reduced due to reaction with NO and other species 

### 4 (Section 4.3.4.2). Over spatial scales of a few kilometers and away from roads, O 3 may 

### 5 be somewhat more homogeneous due to its formation as a secondary pollutant, whil e 

### 6 over scales of tens of kilometers, additional atmospheric processing can result in higher 

### 7 concentrations downwind of an urban area. Although local

- scale variability impacts the 

### 8 magnitude of O 3 concentrations, O 3 formation rates are influenced by factors that vary 

### 9 over larger spatial scales, such as temperature (Section 3.2), suggesting that urban 

### 10 monitors may track one another temporally but miss small

- scale variability in magnitude. 

### 11 The resulting uncertainty in exposure contributes to exposure measuremen t error in 

### 12 epidemiologic studies. 

### 13

Another factor that may influence epidemiologic results is the tendency for people to 

### 14
avoid O 3 exposure by altering their behavior (e.g., reducing time spent outdoors) on high



### 15
O 3 days. Activity pattern has a substantial e ffect on ambient O 3 exposure, with time 

### 16 spent outdoors contributing to increased exposure (Section 4.4.2). Averting behavior has 

### 17 been predominantly observed among children, older adults, and people with respiratory 

### 18 problems. Such effects are less pronounce d in the general population, possibly due to the 

### 19 opportunity cost of behavior modification. Preliminary epidemiologic evidence reports 

### 20 increased asthma hospital admissions among children and older adults when O 3 alert 

### 21 days were excluded from the analysis ( presumably thereby eliminating averting behavior 

### 22 based on high O 3 forecasts). The lower rate of admissions observed when alert days were 

### 23 included in the analysis suggests that estimates of health effects based on dose

- response 

### 24
functions which do not accoun t for averting behavior may be biased towards the null. 

### 25

The range of personal

- ambient correlations reported by most studies (0.3

- 0.8) is similar 

### 26
to that for NO 2 ( U.S. EPA, 2008b ) and somewhat lower than that for PM 2.5 ( U.S. EPA, 

### 27
2009d ) . To the extent that relative changes in central

- site monitor concentration are 

### 28 associated with relative changes in exposure concentration, this indicates that ambient 

### 29
monitor con centrations are representative of day

- to

- day changes in average total personal 

### 30 exposure and in personal exposure to ambient O 3 . The lack of indoor sources of O 3 , in 

### 31
contrast to NO 2 and PM 2.5 , is partly responsible for low indoor

- outdoor ratios (generally 

### 32
0 .1

- 0.4) and low personal

- ambient ratios (generally 0.1

- 0.3), although it contributes to 

### 33 increased personal

- ambient correlations. The lack of indoor sources also suggests that 

### 34
fluctuations in ambient O 3 may be primarily responsible for changes in personal 

### 35

e xposure, even under low

- ventilation, low

- concentration conditions. Nevertheless, low 

### 36 personal

- ambient correlations are a source of exposure error for epidemiologic studies, 

### 37Draft

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- 44 September 2011 tending to obscure the presence of thresholds, bias effect estimates toward the nul l, and 

### 1 widen confidence intervals, and this impact may be more pronounced among populations 

### 2 spending substantial time indoors. The impact of this exposure error may tend more 

### 3 toward widening confidence intervals than biasing effect estimates, since epidemi ologic 

### 4 studies evaluating the influence of monitor selection indicate that effect estimates are 

### 5 similar across different spatial averaging scales and monitoring sites. 

### 6



## 4.8 References

Anderson, GB; Bell, ML. (2010). Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology. J Expo Sci Environ Epidemiol 20: 2

- 11. http://dx.doi.org/10.1038/jes.2008.69

. Anderson, SE; Wells, JR; Fedorowicz, A; Butterworth, LF; Meade, BJ; Munson, AE. (2007). Evaluation of t he contact and respiratory sensitization potential of volatile organic compounds generated by simulated indoor air chemistry. Toxicol Sci 97: 355

- 363. http://dx.doi.org/10.1093/toxsci/kfm043

. Aoki, T; Tanabe, S. (2007). Generation of sub

- micron particles and secondary pollutants from building materials by ozone reaction. Atmos Environ 41: 3139

- 3150. http://dx.doi.org/10.1016/j.atmosenv.2006.07.053

. ASHRAE. (American Society of Heating, Refrigerating and Air

- Conditioning Engineers Inc.). (2009). The 2009 ASHRAE Handbook

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