# Chapter 4. Exposure to Ambient Ozone

4.1. In tro d u c tio n

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



### 2 microenvironments, discussed methods for estimating personal and population exposure via



### 3 monitoring and modeling, analyzed rela tionships between personal exposure and ambient



### 4 concentrations, and discussed the implications of using ambient O 3 concentrations as an estimate of



### 5 exposure in epidemiologic studies. This chapter presents new information regarding exposure to



### 6 ambient O 3 in the context of existing relevant information summarized in the 2006 O 3 AQCD, which



### 7 in many areas remains definitive. A brief summary of findings from the 2006 O 3 AQCD is presented



### 8 at the beginning of each section as appropriate.



### 9 Section 4.2 presents gener al exposure concepts describing the relationship between ambient



### 10 pollutant concentrations and personal exposure. Section 4.3 describes exposure measurement



### 11 techniques and studies that measured personal, ambient, indoor, and outdoor concentrations of O 3


### 12 and related pollutants. Section 4.4 describes techniques for modeling local O 3 concentrations,



### 13 microenvironmental concentrations, and personal and population exposure. Section 4.5 discusses the



### 14 imp lications of using ambient O 3 concentrations to estimate expos ure in epidemiologic studies,



### 15 including several factors that contribute to exposure error.



# 4.2. General Expos ure Concepts

16 A theoretical model of personal exposure is presented to highlight measurable quantities and



### 17 the uncertainties that exist in this framework

. An individual ’ s time

- integrated total exposure to O 3


### 18 can be described based on a compartmentalization of the person ’ s activities throughout a given time



### 19 period:



### E
T 

### = 

# ∫ C j 

### dt

Equation 4-1



### 20 where E T = total ( T ) exposu re over a time

- period of interest

, C j = airborne O 3 concentration at



### 21 microenvironment j , and dt = portion of the time

- period spent in microenvironment j. Equation 4-1



### 22 can be decomposed into a model that accounts for exposure to O 3 , of ambient ( E

a ) and nona mbient



### 23 ( E

na ) origin of the form:

Note: Hyperlinks to the reference citations throughout this document will take you to the NCEA HERO database (Health and Environmental Research Online) at http://epa.gov/hero . HERO is a database of scientific literature used by U.S. EPA in the process of developing science assessments such as the Integrated Science Assessments (ISAs) and the Integrated Risk Informatio n System (IRIS).

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### E **=** E **+** E
T a na Equation 4-2



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



### 2 compounds, as described in Chapter 3. Although nonambient sources of O 3 e xist, such as O 3


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



### 4 important sources of population exposure . Ozone concentrations generated by ambient and



### 5 nonambient sources are subject to spatial and temporal v ariability that can affect estimates of



### 6 exposure and influence epidemiologic effect estimates. Exposure parameters affecting interpretation



### 7 of epidemiolog ic studies are discussed in Section 4.5.



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



### 9 to the NAAQS review. E

a can be expressed in terms of the fraction of time spent in various outdoor



### 10 and indoor microenvironments (Wallace et al., 2006, 089190 ; Wilson et al., 2000, 010288 ):



### E

a 

### = 

# ∑ f

o 

### C

o 

### + 

# ∑ f
i 

### F

inf, i 

### C

o
, i
Equation 4-3



### 11 where f = fraction of the relevant time period (equivalent to dt in Equation 4-1) , subscript o = index



### 12 of outdoor microenviron ments , subscript i = index of indoor microenvironments, subscript o,i =



### 13 index of outdoor microenvironments adjacent to a given indoor microenvironment i , and F inf, i =



### 14 infiltration factor for indoor microenvironment (i)

. Equation 4-3 is subject to the const raint Σ f

o +



### 15 Σ f

i = 1 to reflect the total exposure over a specified time period , and each term on the right hand side



### 16 of the equation has a summation because it reflects various microenvironmental exposures. Here,



### 17 “ indoors ” refers to being inside any aspect of the built environment, e.g.

, home, office buildings,



### 18 enclosed vehicles (automobiles, trains, buses), and/or recreational facilities (movies, restaurants,



### 19 bars). “ Outdoor ” exposure can occur in parks or yards, on sidewalks, and on bicycles or motorcycle s.



### 20 F inf is a function of the building air exchange characteristics. Assuming steady state ventilation



### 21 conditions, the infiltration factor is a function of the penetration ( P ) of O 3 , the air exchange rate ( a )



### 22 of the microenvironment, and the rate of O 3 loss ( k ) in the microenvironment; F inf = Pa /( a + k ).



### 23 In epidemiologic studies, C

a is often used in lieu of outdoor microenvironmental data to



### 24 represent these exposures based on the availability of data . T hus it is often assumed that C

o = C

a and



### 25 that the fraction of time spent outdoors can be expressed cumulatively as f

o ; the indoor terms still



### 26 retain a summation because infiltration differs among different microenvironments. If an



### 27 epidemiologic study employs only C

a , then the assumed model of an individual’s expo sure to



### 28 ambient O 3 , first given in Equation 4-3, is re

- expressed solely as a function of C

a :



### E

a 

### = 

# ( f

o 

### + 

# ∑ f
i 

### P 

# ) C

a Equation 4-4



### 29 Meteorology, varying precursor emissions and O 3 formation rates, spatia l variability of O 3


### 30 concentration, design of the epidemiologic study, and other factors determine whether or not



### 31 Equation 4-4 is a reasonable approximation for Equation 4-3

. Errors and uncertainties inherent in use

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### 1 of Equation 4-4 in lieu of Equation 4-3 a re described in Section 4.5 with respect to implications for



### 2 interpreting epidemiologic studies. Epidemiologic studies often use concentration measured at a



### 3 central site monitor to represent ambient concentration ; th us α , the ratio between personal exposur e



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



### E

a 

### α **=** C

a Equation 4-5



### 5 Combination of Equation 4-4 and Equation 4-5 yield s:



### α **=** f

o 

### + 

# ∑ f
i 

### P

Equation 4-6



### 6 whe re α varies between 0 and 1. If a person ’ s exposure occurs in a single microenvironment, the



### 7 ambient component of a microenvironmental O 3 concentration can be represented as the product of



### 8 the ambient concentration and P

. Wallace et al. (2006, 089190 ) n ote that time

- activity data and



### 9 corresponding estimates of P for each microenvironmental exposure are needed to compute an



### 10 individual’s α with accuracy. If local sources and sinks exist and are significant but not captured by



### 11 central site monitors , then the ambient component of the local outdoor concentration may be



### 12 estimated using dispersion models, land use regression models, receptor mo dels, fine scale CTM s or



### 13 some combination of these techniques . These techniques are described in Section 4.4.



# 4.3. Exp o s u re Me a s u re m e n t

4.3.1. Pe rs o nal Mo nito rin g Tec h niq ue s

14 As described in the 2006 O 3 AQCD (U.S. EPA, 2006, 088089 ) , a passive sampler has been



### 15 developed and deployed to me asure personal exposure to O 3 . This sampler uses a fi lter coated with



### 16 nitrite, which is converted to nitra te by O 3 and then quantified by a technique such as ion



### 17 chromatography (Koutrakis et al., 1993, 202808 ). This method has been licensed and marketed by



### 18 Ogawa, Inc., Japan (Ogawa & Company, 2007, 090937 ) . The cumulative sampling and the detection



### 19 limit of the passive badges makes them suitab le for monitoring periods of 24 h ours or greater, which



### 20 limits their ability to measure short

- term dail y fluctuations in personal O 3 exposure. Longer



### 21 sampling periods give lower detection limits; use of the badges for a 6

- day sampling period yields a



### 22 detec tion limit of 1 ppb, while a 24 -hour sampling period gives a detection limit of approximately 5

­ 

### 23 10 ppb. This can result in a substantial fraction of daily samples being below the detection limit



### 24 (Sarnat et al., 200 5, 087531 ; Sarnat et al., 2006, 089784 ).



### 25 The nitrite

- nitrat e conversion reaction has also been used as the basis for an active sampler



### 26 consisting of a nitrite

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



### 27 65 mL/min (Geyh et al., 1997, 086151 ; Geyh et al., 1999, 016908 ) . The reported detection limit is

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### 5

10

15

20

25

30

35

1 10 ppb

- h, enabling the quantification of O 3 concentrations measured over a few hour s rather than a



### 2 full day (Geyh et al., 1999, 016908 )

. 

### 3 A portable active O 3 monitor based on the UV photometric technique used for st ationary



### 4 monitors (Chapter 3) has recently been approved as a FEM (75 FR 22126) (2010, 687659 ) . This

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



### 6 changes in different microenvironments (Wilson and Birks, 2006, 595155 ) . Its size and weight



### 7 (approximately 10 × 20 × 30 cm; 2 kg) make it suitable for use in a backpack configuration. The



### 8 monitors are currently used by the U.S. National Park service as stationary monitors to measure O 3


### 9 in several national parks (Chapter 3).



## 4.3.2. Ind oo r

- Outdoor Concentration Relations hips

The 2006 O 3 AQCD (U.S. EPA, 2006, 088089 ) presented results from several studies on the



### 11 relationship between indoor O 3 concentration and the O 3 concentration immediately outside the



### 12 indoor mic roenvironment. These studies show that the indoor concentration is often substantially



### 13 lower than the outdoor concentration unless indoor sources are present. Low indoor O 3


### 14 concentrations can be explained by reactions of O 3 with surfaces and airborne const ituents. Studies

have shown that O 3 is deposited onto indoor surfaces where reactions produce secondary pollutants



### 16 such as formaldehyde (Reiss et al., 1995, 078727 ; Reiss et al., 1995, 078743 ) . However, the indoor


### 17 outdoor relationship is greatly affected by the air exchange rate; under conditions of high air



### 18 exchange rate, such as open windows, the ind oor O 3 concentration m ay approach the outdoor



### 19 concentration. Geyh et al. (2000, 001775 ) measured 6

- day (approximately 14 4 h ours ) indoor and

outdoor concentrations at 116 homes in south ern California, approximately equally divided between



### 21 the community of Upland and several mountain communities. The extended sampling period



### 22 resulted in a relatively low detection limit (1 ppb ) for the passive samplers used. The Upland homes



### 23 were nearly all air

- conditioned, while the mountain community homes were ventilated by o pening



### 24 windows. During the O 3 season, the indoor O 3 concentration averaged over all homes was

approximately 24% of the overall mean outdoor concentration in Upland (11.8 versus 48.2 ppb),



### 26 while in the mountain communities, the indoor concentration was 36% of the outdoor concentration



### 27 (21.4 versus 60.1 ppb). This is consistent with the increased air exchange rate expected in homes



### 28 using window ventilation. In the non

- ozone season, when homes are likely to be more tightly closed



### 29 to conserve heat, the ratios of indoor to outdoor concentration were 0.15 (3.2 versus 21.1 ppb) and

0.08 (2.8 versus 35.7 ppb) in Upland and the mou ntain communities, respectively. Avol et al. ( 1998,



### 31 018270 ) observed a mean (standard deviation) indoor

- outdoor (I/O) ratio of 0.37 (0.25) for 239



### 32 matched 24

- h samples collected between February and Dece mber at homes in the Los Angeles area.



### 33 The I/O ratio during summer was higher than the non

- summer I/O ratio (0.43 versus 0.32). The



### 34 authors also reported a correlation of 0.58 between the indoor concentration and the outdoor

concentration, which was only s lightly higher than the correlation between the indoor concentration



### 36 and the concentration at the neighborhood fixed

- site monitor (0.49). Romieu et al. (1998, 049834 )



### 37 reported a mean I/O ratio of 0.20 (SD = 0.18) in 145 homes in Mexico City for 14

- day cumulative

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### 1 samples, with the highest ratios observed in homes where windows were usually open during the day



### 2 and where there was no carpeting or air filters. Studies conducted in Nashville, TN and Toronto,



### 3 Canada both reported mean residential I/O ratios of approximately 0.1 (Lee et al., 2004, 055599 ; L iu



### 4 L- JS; Koutrakis et al., 1995, 039061 ).



### 5 Investigators have also measured I/O ratios for non

- residential microenvironments, including



### 6 schools and vehicles. Romieu et al. (1998, 049834 ) reported that O 3 concentrations measured during



### 7 school hours (10

- day cu mulative sample, 5 h/day) were 30

- 40% of concentrations immediately



### 8 outside the schools, while overall I/O ratios (14

- day cumulative sample, 24 h/day) were



### 9 approximately 15%. The authors attribute this discrepancy to increased air exchange during the



### 10 schoo l day due to opening doors and windows. Air exchange was also identified as an important



### 11 factor in the I/O ratios measured at eight French schools (Blondeau et al., 2005, 07804 4 ) . In this



### 12 study, the I/O ratios based on simultaneous continuous measurements ranged from 0

- 0.45, increasing



### 13 with decreasing building tightness. Although no indoor measurements were made, Rundell et al.



### 14 (2006, 089785 ) report a mean daytime continuous outdoor O 3 concentration of 106 ppb at one



### 15 university and four elementary school playing fields, indicating the potential for elevated O 3


### 16 exposure in the school outdoor microenvironment.



### 17 Gradients in O 3 concentrations observed near roadways provide evidence of the NO -O 3


### 18 titration reaction that takes place in the ambient environment in which NO emitted from vehicles



### 19 reacts with O 3 to produce NO 2 (Finlayson

- Pitts and Pitts, 1986, 035054 ) . Oz one concentration has



### 20 been observed in several studies to increase with increasing distance from the roadway, both upwind



### 21 and downwind of the road (Section 3.6.2.1). Depending on wind direction, O 3 concentrations near



### 22 the roadway have been found to be 20 -8 0% of ambient concentrations at sites 400 m or more distant



### 23 from roads. This indicates that exposures in near

- road, on

- road and in

- vehicle microenvironments,



### 24 while highly variable, may be lower than those in other microenvironments. A study on patrol cars



### 25 during trooper work shifts reported in

- vehicle 9

- h concentrations that were approximately 51% of



### 26 simultaneously measured roadside concentrations (mean of 11.7 versus 28.3 ppb ) (Riediker et al.,



### 27 2003, 043761 ).



## 4.3.3. Personal

- Ambient Concentration Relations hips

4.3.3.1. Personal

- Ambie nt Correlations

28 The relationship between personal exposure and ambient O 3 concentrations has been evaluated



### 29 in several research studies, many of which were conducted prior to 2005 and are discussed in the



### 30 2006 O 3 AQCD (U.S. EPA, 2006, 088089 ). Some studies evaluated subject

- specific, or longitudinal



### 31 correlations, which describe multiple daily measurements for a single individual. These studies



### 32 indicate the inter

- individual variability of perso nal- ambient correlations. Another type of correlation



### 33 is a pooled correlation, which combines data from multiple individuals over multiple monitoring



### 34 periods (e.g., days), providing an overall indicator of the personal

- ambient relationship for all study

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### 1

2

3

4

5

6

7

8

9

10

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12

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19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38 su bjects. A third type of correlation is a community

- average correlation, which correlates average

exposure across all study subjects with fixed

- site monitor concentrations. Community

- average
correlations are particularly informative for interpreting time

- se ries epidemiologic studies, in which

ambient concentrations are used as a surrogate for community

- average exposure.
The results of these studies indicate that personal exposures are moderately well correlated

with ambient concentrations, and that the ratio of personal exposure to ambient concentration is

higher in outdoor microenvironments and during the summer season. In situations where a lack of

correlation was observed, this may be due in part to a high proportion of personal measurements

below the dete ction limit. Chang et al. (2000, 001276 ) measured hourly personal exposures in

multiple microenvironments and found that the pooled correlation b etween personal exposure and

ambient concentration was highest for outdoor microenvironments (r = 0.68- 0.91). Correlations in

residential indoor microenvironments were very low (r = 0.05

- 0.09), with moderate correlations

( 0.34- 0.46 ) in other indoor microen vironments such as restaurants and shopping malls. Liard et al.

(1999, 001426 ) evaluated community

- average correlations based on 4 -da y mean personal O 3 exposure measurements for adults and children and found a relatively high correlation (r = 0.83)

with ambient concentrations, even though 31

- 82% of the personal measurements were below the

detection limit. Sarnat et al. (2000, 001852 ) studied a population of older adults in Baltimore and

found that longitudinal correlations between 24

- h personal exposure and ambient concentr ation

varied by subject and season, with somewhat higher correlations observed during summer (mean =

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

ventilated indoor environments have higher correlations than those living in poorly ventilated indoor

environments, although exceptions to this were also observed. A moderate pooled correlation of 0.61

was reported between 24

- h avg personal and central

- site measurements by Linn et al. (1996, 082508 )

for a population of south ern California schoolchildren who spent an average of 101

- 136 min utes

outdoors. The authors also report a correlation of 0.70 between central

- site measurements and

concentrations outside the children ’ s schools. Although the average school outdoor concentration

(34 ppb) was higher than the average central

- site concentration (23 ppb) and the average personal

expo sure concentration was lower (5 ppb) than the central

- site value, the similarity between the

correlations indicate that central

- site monitor concentrations can represent personal exposures in

addition to representing local outdoor concentrations. A similar result was observed in a study in

Vancouver, BC comparing three groups spending different amounts of time outdoors: health clinic

workers (0

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

- 45% of time outdoors), and farm workers

(100% of time outdoors) (Brauer and Brook, 1997, 083339 ). Health clinic workers and camp

counselors were monitored 24 h/d ay , while farm workers were monitored during their work shift

(6

- 14 hours ). In this study, the pooled correlations between personal exposure and fixed

- site

concentration were similar among the groups, without a clear trend (r = 0.60, 0.42, and 0.64,

respectively), although the ratios of personal exposure to fixed

- site monitor concentration increase d

among the groups with increasing amount of time spent outdoors (0.28, 0.48, and 0.96, respectively).

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### 5

10

15

20

25

30

35

1 This indicates that temporal variations in personal exposure to O 3 are driven by variations in ambient



### 2 concentration, even for individuals that spend lit tle time outdoors.



## 4.3.3.2. Personal

- Ambie nt Ra tios

3 Additional studies were summarized in the 2006 O 3 AQCD (U.S. EPA, 2006, 088089 )



### 4 evaluating the ratio of personal O 3 exposure to ambient concentration or regressing personal

exposure on ambient concentration. O’Neill et al. (2003, 042752 ) studied a population of shoe



### 6 cleaners working outdoors in Mexico City and presented a regression model indicating a 0.56 ppb



### 7 increase in 6

- h personal exposure for each 1 ppb increase in ambient concentration (95% CI: 0.43

, 

### 8 0.69). Regression analyses by Sarnat et al. for 24

- h data from mixed populations of children and



### 9 older adults in Baltimore (2001, 019401 ) and Boston (2005, 087531 ) found differing results between

the two cities, with Baltimo re subjects showing a near

- zero slope (0.01) during the summertime



### 11 while Boston subjects showed a positive slope of 0.27 ppb personal exposure per 1 ppb ambient



### 12 concentration. In both cities, the winter slope was near zero. Differences between the study ar eas



### 13 may be due to differences in time spent outdoors, residential ventilation conditions, or other factors.



### 14 Xue et al. (2005, 087532 ) measured 6

- day personal exposure of children in south ern California and

found that the average ratio of personal exposure to ambient concentration was relatively stable



### 16 throughout the year at 0.3. These authors also regressed personal exposures on ambient



### 17 concentration after adjusting for time

- activity patterns and housing characteristics and found a slope



### 18 of 0.54 ppb/ppb, with an R 2 value of 0.58. Unadjusted regression slopes were not presented.



### 19 A few studies have been published since the 2006 O 3 AQCD comparing personal exposures

with ambient concentrations, and these findings generally confirm the conclusions of the 2006 O 3


### 21 AQCD. Sarnat et al. (2006, 089784 ) measured 24

- h personal exposures for a panel of older adults in



### 22 Steubenville, OH during summer and fall 2000. Subjects were classified as high

- ventilation or low


### 23 ventilation based on whether they spent time in indoor environments with open windows. Regression



### 24 of personal exposures on ambient concentration found a higher slope for high

- ventilation subjects

compared with low

- ventilation subjects in both summer (0.18 versus 0.08) and fall (0.27 versus



### 26 0. 20). Although no personal exposures were measured, McConnell et al. (2006, 089256 ) found that



### 27 average 24

- h home outdoor O 3 concentrations were within 6 ppb of O 3 concen trations measured at



### 28 central

- site monitors in each of three south ern California communities, with a combined average



### 29 home outdoor concentration of 33 ppb compared to the central

- site average of 36 ppb. Ramirez

Aguilar et al. (2008, 098930 ) measured 48 - to 72

- h personal exposures of four groups of asthmatic



### 31 children aged 6

- 14 in Mexico City during 1998

- 2000. A moderate pooled correlation (r = 0.35) was



### 32 observed between these exposures and corresponding ambient concentrations. Regression of



### 33 personal exposures on ambient concentrations yielded a slope of 0.17 ppb/ppb (95% CI: 0.13

, 0.21)



### 34 after adjustment for distance to the fixed

- site monitor, time spent outdoors, an interaction term

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



### 36 Taken together, resul ts from previous and recently published studies indicate that while the



### 37 relationship between personal exposures and ambient concentrations varies due to a number of

March 2011 4-7 DRAFT – DO NOT CITE OR QUOTE

### 1 factors, such as activity patterns, housing characteristics, and season, O 3 concentrations measured at



### 2 central

- site monitors are representative of day

- to

- day changes in average personal O 3 exposure,



### 3 which is the important parameter for time

- series epidemiologic studies. Another important finding is



### 4 that the magnitude of personal exposures is smaller than concentrations reported at fixed

- site



### 5 monitors due to time spent indoors and the low indoor penetration of O 3

.



## 4.3.4. Co

- Expo s ure to Oth er Pollu ta nts an d En viron me ntal Stres s o rs

4.3.4.1. Pers onal Expos ure to Ozone and Co

- polluta nt s

6 Personal exposure to O 3 shows variable correlation with personal exposure to other pollutants,



### 7 with differences in correlation depending on factors such as season, city

- specific characteristics, and



### 8 spatial variability of the co -pollutant . Suh and Zanobetti (2010, 677202 ) reported Spearman rank



### 9 correlation coefficients during spring and fall between 24

- h avg O 3 measurements and co

- pollutants



### 10 of 0.14, 0.00 , and

- 0.03 for PM 2.5 , EC, and NO 2 , respectively. Note the higher correlation observed



### 11 for PM 2.5 , a regional pollutant, in con trast with the extremely low correlations for the traffic

- related



### 12 and more spatially variable pollutants EC and NO 2

. Chang et al. (2000, 001276 ) measured hourly



### 13 personal exposures to PM 2.5 and O 3 in summer and winter in Baltimore, Maryland. Correlations



### 14 between PM 2.5 and O 3 were 0.05 and

- 0.28 in summer and winter, respectively. Results indicate



### 15 personal O 3 exposures were not significantly associated with personal PM 2.5 exposures in either



### 16 summer or winter. These non

- significant correlations may be attributed in part to the relatively low



### 17 personal O 3 exposures observed in this study.



### 18 Studies conducted in Baltimore (Sarnat et al., 2001, 019401 ) and Boston (Sarnat et al., 2005,



### 19 087531 ) found differing results for the correlation between 24

- h avg personal O 3 and personal PM 2.5


### 20 exposures, par ticularly during the winter season. Sarnat et al. (2001, 019401 ) found a positive slope



### 21 when regressing personal exposures of both total PM 2.5 (0.21) and PM 2.5 of ambient origin (0.22)



### 22 against personal O 3 exposures during the summer season, but negative slopes (

- 0.05 and -0.18,



### 23 respectively) during the winter season. The summertime slope for personal PM 2.5 exposure versus



### 24 personal O 3 exposure was much higher for children (0.37) than for adults (0.07), which may be the



### 25 result of different activity patterns. This team of researchers also found a positive, although higher,



### 26 summer slope between 24

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



### 27 2005, 087531 ) . However, the winter slope was positive (1.25) rather than negative, as in Baltimore.



### 28 In both citi es during both seasons, there was a wide range of subject

- specific correlations between



### 29 personal O 3 and personal PM 2.5 PM 2.5 , with some subjects showing relatively strong positive



### 30 correlations (>0.75) and others showing strong negative correlations (< -0.50 ). The median



### 31 correlation in both cities was slightly positive in the summer and near zero (Boston) or slightly



### 32 negative (Baltimore) in the winter. These results indicate the potential effects of city

- specific



### 33 characteristics, such as housing stock and building ventilation patterns, on relationships between O 3


### 34 and co -pollutant s.

March 2011 4-8 DRAFT – DO NOT CITE OR QUOTE

## 4.3.4.2. Ne a r

- Road Expos ure to Ozone and Co

- polluta nt s

1 Beckerman et al. (2008, 096484 ) measured both 1- week and continuous concentrations of O 3 , 

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



### 3 in the vicinity of heavily traveled ( annual average daily traffic [AADT] >340,000) roadwa ys in



### 4 Toronto, Canada. Passive samplers were deployed for one week in August 2004. Ozone



### 5 concentrations were negatively correlated with all pollutants, with the exception of VOCs at one of



### 6 the monitoring sites which were suspected of being influenced by sm all area sources. Site specific



### 7 cor relations are given in Figure 4 -1. Correlations were

- 0.77 to

- 0.85 for NO 2 , - 0.48 to - 0.62 for NO,



### 8 and - 0.55 to

- 0.63 for NO X

. Pooled correlations using data from both sites were somewhat lower in



### 9 magnitude

. PM 2.5 and PM 1.0 correlations were

- 0.35 to

- 0.78 and

- 0.34 to

- 0.58, respectively

. At the



### 10 monitoring site not influenced by small area sources, O 3 - VOC cor relations ranged from

- 0.41 to



### 11 -0.66

. 

### 12 Beckerman et al. (2008, 096484 ) also made on

- road measurements of multiple pollutants with



### 13 a instrumented vehicle. Concentrations were not reported, but correlations between O 3 and other



### 14 pollutants were negat ive and somewhat greater in magnitude (i.e., more negative) than the near -road



### 15 correlations. SO 2 , CO, and BC were measured in the mobile laboratory, although not at the roadside,



### 16 and they all showed negative correlations with O 3 when the data were controlled for site.



### 17 Correlations for continuous concentrations between O 3 and co -pollutant s were somewhat lower th an



### 18 the 1

- week correlations, e xcept for O 3 - PM 2.5 correlations. C orrelations were -0.90, -0.66, - 0.77, and

­


### 19 0.89 for NO 2 , NO, NO X , and PM 1.0 respective ly. The continuous O 3 - PM 2.5 correlation wa s

- 0.62,



### 20 which is in the range of the 1- week correlation.

Source: Beckerman et al. (2008, 096484 )



### Figure 4 -1 . Correlations between 1- week o zone concentrations and co

- pollutants

.

March 2011 4-9 DRAFT – DO NOT CITE OR QUOTE

### 5

10

15

20

25

30 

## 4.3.4.3. Ind oor Expos ure to Ozone and Co

- polluta nt s

1 Ambient O 3 that infiltrates indoors reacts with organic compounds and other chemicals to



### 2 form oxidized products, as descri bed in the 2006 O 3 AQCD (U.S. EPA, 2006, 088089 ) . It is



### 3 anticipated that individuals are exposed to these reaction products, although little evidence is



### 4 available on personal exposures. The reactions are similar to those occu r ring in the ambient air, as

summarized in Chapter 3. For example, O 3 can react with terpenes and other compounds from



### 6 cleaning products, air fresheners, and wood products to form particulate and gaseous species, such as



### 7 formaldehyde (Aoki and Tanabe, 2007, 449034 ; Reiss et al., 1995, 078743 )

. Ozone has also been



### 8 shown to react with material trapped on HVAC filters and generate airborne products (Bekö et al.,



### 9 2007, 601190 ; Hyttinen et al., 2006, 134392 ) . It is possible that these re action products may have

health effects in addition to, or greater than, those from O 3 itself (Anderson et al., 2007, 600123 ;



### 11 Weschler and S hields, 1997, 084530 )

. O zone may also react to form other oxidants, which then go



### 12 on to participate in additional reactions. White et al. (2010, 633874 ) found evidence that HONO or



### 13 other oxidants may have been present during experiments to estimate indoor OH concentrations,



### 14 indicating complex indoor oxidant chemistry. Rates of these reactions are dependent on indoor O 3 concentration, temperature, and air exchange ra te, making estimation of exposures to reaction



### 16 products difficult.



## 4.3.5. Population Proximity to Fixed

- Site Ozone Monito rs

17 The distribution of O 3 monitors across urban areas varies between cities (Section 3.6.2.1), and



### 18 the population living near each monitor var ies as well. It is not necessarily true that proximity to a



### 19 monitor determines the degree to which that monitor represents an individual's ambient exposure,

but proximity is one indicator. One way to calculate monitor representativeness is to calculate the



### 21 fraction of the urban population living within a certain radius of a monitor. Table 4

- 1 presents the



### 22 fraction of the population in selected cities living within 1, 5, 10, and 20 km of an O 3 monitor. Values



### 23 are presented for both total population and for t hose under 18 years of age, a potentially susceptible



### 24 population to the effects of O 3 . The data indicate that relatively few people live within 1 km of an O 3 monitor, while nearly all of the population in most cities lives within 20 km of a monitor. Many O 3


### 26 monitors are sited at “ neighborhood scale, ” intended to represent an area of the city with dimensions



### 27 in the 0.5

- 4 km range (Section 3.5.6.1). Looking at the results for a 5

- km radius, generally 20

- 30%



### 28 of the population lives wi thin this distance from an O 3 monitor. Some cities have a greater



### 29 population in this buffer, such as Salt Lake City, while others have a lower percentage, such as

Minneapolis and Seattle. Percentages for children are generally similar to the total population, with



### 31 no clear trend.

March 2011 4- 10 DRAFT – DO NOT CITE OR QUOTE

### T able 4

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

Population 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% 4 7% 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% 1 7% 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 C ity 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%



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



### 2 such that every person in the sector lives closer to that monitor than any o ther. This facilitates



### 3 calculation of the fraction of the city's population represented (according to proximity) by each



### 4 monitor. In Atlanta, for example, the population fraction represented by each of the 11 monitors in



### 5 the city ranged from 2.9

- 22%. The t wo monitors closest to the city center (sites A and B on



### 6 Fig ure 3-24 ) accounted for 16% and 8% of the population, respectively. Site B has two listed



### 7 monitoring objectives, highest concentration and population exposure. The other monitor in Atlanta



### 8 with a listed objective of highest concentration is Site C, which represents the largest fraction of the



### 9 population (22%). The eight monitors with a primary monitoring objective of population exposure



### 10 account for 2.9

- 17% of the population per monitor.



### 11 Atlanta po pulation fractions for children (<18 years of age) are similar to those for the general



### 12 population, but other populations show a different pattern of monitor representativeness. Older adults



### 13 (age 65 and up) were somewhat differently distributed with respec t to the monitors, with most



### 14 monitors showing a difference of more than a percentage point compared to the general population.



### 15 Based on 2000 population data, the fraction of older adults closest to the two city center monitors (A



### 16 and B) was 4% higher and 2 % lower, respectively, than the fraction for the population as a whole.



### 17 Site C showed the highest differential, with 21% of the total population but only 15% of the older

March 2011 4- 11 DRAFT – DO NOT CITE OR QUOTE

### 1 adult population. This indicates the potential for monitors to differentially represent potentially



### 2 susceptible populations.



# 4.4. Exp o s u re Mod e lin g

4.4.1. Co nc e ntra tio n S urfac e Mo delin g

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



### 4 concentration surface over a geographic area , with concentration at locations betw een monitors



### 5 estimated using a model to compensate for missing data

. The calculated O 3 concentration surface can



### 6 then be used to estimate exposures outside residences, schools, workplaces, roadways, or other



### 7 locations of interest. This technique does not estimate exposure directly because it does not account



### 8 for activity patterns or concentrations in different microenvironments. There are three main types of



### 9 approaches: spatial interpola tion of measured concentrations; statistical models using meteorologica l



### 10 variables, pollutant concentrations, and other predictors to estimate concentrat ions at receptors in the



### 11 domain; and r igorous first

- principle models, such as chemistry

- transport models or dispersion



### 12 models incorporating O 3 chemistry. Some researchers hav e developed models that combine these



### 13 techniques. The models may be applied over urban, regional, or national spatial scales, and can be



### 14 used to estimate daily concentrations or longer

- term averages. This discussion will focus on short


### 15 term concentrations estimated across urban areas.



### 16 The 2006 O 3 AQCD (U.S. EPA, 2006, 088089 ) discussed concentration surface models,



### 17 focusing on chemistry

- transport models as well as geospatial and spatiotemporal interpolation



### 18 techniques (Christakos and Vyas, 1998, 029728 ; Christakos and Vyas, 1998, 011987 ; e.g.,



### 19 Georgopoulos et al., 1997, 083591 ) . Recent research has continued t o refine and extend the modeling



### 20 approaches. A few recent papers have compared different approaches for the same urban area.



### 21 Marshall et al

. (2008, 193983 ) compared four spatial interpolation techniques for estimation of



### 22 O 3 concentrations in Vancouver, BC. The investigators assigned a daily average O 3 concentration to



### 23 each of the 51,560 postal

- code centroids using one of the following techniques: (1) the concentration



### 24 from the nearest monitor within 10 km ; (2) the average of all monitors within 10 km ; (3) the inverse


### 25 distance- weighted ( IDW ) average of all monitors in the area ; and (4) the IDW average of the 3



### 26 closest monitors within 5 0 km. Method 1 (the nearest

- monitor approach) and Method 4 (IDW

- 50



### 27 km) had similar mean and median estimated annual

- and monthly

- average concentrations, although



### 28 the 10th

- 90th percentile range was smaller for IDW

- 50. This is consistent with the averaging o f



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



### 30 methods was 0.93 for monthly

- average concentrations and 0.78 for annual

- average concentrations

. 

### 31 Methods 2 and 3 were considered sub

- optimal and were excluded from further analysis. In the case



### 32 of M ethod 2, a single downtown high

- concentration monitor skewed the results in the vicinity,



### 33 partially as a result of the asymmetric layout of the coastal city of Vancouver. Method 3 was too



### 34 spatially homogenous because it assigned most locations a concentration near the regional average,

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39 except for locations immediately adjacent to a monitoring site. CMAQ conce ntration estimates using

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

median concentrations from CMAQ were approximately 50% higher than Method 1 and Method 4

estimates for both annual and monthly average concentrations. This may be due in part to the CMAQ

grid size, which was too coarse to reveal near

- roadway decrements in O 3 concentration due to

titration by NO. The IQR for the annual average was similar between CMAQ and the interpolation

techniques, but the monthly average CMAQ IQR was approximately twice as large, indicating a

seasonal effect. Bell (2006, 194358 ) compared CMAQ estimates for north ern Georgia with

nearest

- mo nitor and spatial interpolation techniques, including IDW and krig ing. The area -weighted

concentration estimates from CMAQ indicated areas of spatial heterogeneity that were not captured

by approaches based on the monitoring network. The author concluded that some techniques , such as

spatial interpolation, were not suita ble for estimation of exposure in certain situations, such as for

rural areas. Using the concentration from the nearest monitor resulted in an overestimation of

exposure relative to model estimates.

Land use regression (LUR) models have been developed to e stimate levels of air pollutants,

predominantly NO 2 , as a function of several land use factors, such as land use designation, traffic

counts, home heating usage, point source strength, and population density (Briggs et al., 1997,

025950 ; Gilliland et al., 2005, 098820 ; Ryan and LeMasters, 2007, 156063 ) . LUR , initially termed

regression mapping (Briggs et al., 1997, 025950 ), is a regression derived fr om monitor ed

concentration s as a function of data from a combination of the land use factors . T he regression is

then used for predicting concentrations at multiple locations based on the independent variables at

those particular locations without monitors. Hoek et a l. (2008, 195851 ) warn of several limitations of

LUR, including distinguishing real associations between pollutants and covariat es from those of

correlated co -pollutant s, limitations in spatial resolution from monitor data, applicability of the LUR

model under changing temporal conditions, and introduction of confounding factors when LUR is

used in epidemiologic studies. These limi tations may partially explain the lack of LUR models that

have been developed for O 3 at the urban scale. Brauer et al. (2008, 156292 ) evaluated the us e of

LUR and IDW

- based spatial

- interpolation models in epidemiologic analyses for Vancouver, BC and

suggested that LUR is appropriate for directly

- emitted pollutants with high spatial variability, such

as NO and BC, while IDW is appropriate for secondary p ollutants such as NO 2 and PM 2.5 with less

spatial variability. Although this study did not evaluate an LUR model for O 3 , possibly due to the

lack of the required dedicated sampling campaign, the IDW approach would be expected to be

favored since O 3 is a se condary pollutant. At a much larger spatial scale, EU

- wide, Beelen et al.

(2009, 601157 ) compared a LUR model for O 3 with ordinary kriging and universal kriging, which

incorporated mete o rlogical, topographical, and land use variables to characterize the underlying

trend. The LUR model performed reasonably well at rural locations (5

- km resolut ion), explaining a

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

(1

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

- density residential

land use), and the R 2 value was ver y low (0.06). Universal kriging was the best method for the large

March 2011 4- 13 DRAFT – DO NOT CITE OR QUOTE

### 5

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1 scale composite EU concentration map, for O 3 as well as for NO 2 and PM 10 , with an R 2 value for O 3


### 2 of 0.70. The authors noted that these methods were not designed to capture spatial variation in



### 3 concentrations that are known to occur within tens of meters of roadways (Section 3.6.2.1), which



### 4 could partially explain poor model performance at the urban scale.

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



### 6 roadways. McConnell et al. (2006, 089256 ) developed a regression model to predict residential O 3



### 7 concentrations in southern California using estimates of residential NO X calculated from traffic data



### 8 with the CALINE4 line source dispersion model

. The authors estimated that local traffic contributes



### 9 18% of NO X concentrations measured in the study communities, with the remainder coming from

regional background

. Their regr ession model indicates that residential NO X reduces residential O 3


### 11 concentrations by 0.51 ppb O 3 per 1 ppb NO X , and that a 10th

- 90th percentile increase in local NO X


### 12 results in a 7.5 ppb decrease in local O 3 concentrations

. This intra

- urban traffic

- related variability in



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



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

monitor and individual homes coul d result in either an overestimate or underestimate of residential



### 16 O 3

. 

### 17 A substantial number of researchers have used geostatis t ical methods and chemistry -transport



### 18 models to estimate O3 concentrations at urban, regional, national, and continental scales, b oth in the



### 19 U.S. and in other countries (Hooyberghs et al., 2006, 608180 ; e.g., Pakalapati e t al., 2009, 615781 )

. In addition to short

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



### 21 used for long

- term exposure assessmen t, O 3 forecasts (Sahu et al., 2009, 618189 ) , or evaluating



### 22 emission control strategies (Gabusi and Volta, 2005, 606112 ) . It is difficult to determine the utility of



### 23 these methods for exposure assessment; while improved local

- scale estimates of outdoor



### 24 concentrations may contribute to b etter assignment of exposures, information on activity patterns is

needed to produce estimates of personal exposure.



## 4.4.2. Mic ro en viro n me ntal Mo dels

26 Population

- based methods, such as the Air Pollution Exposure (APEX) and S tochastic Human



### 27 Exposure and Dose S imulation (SHEDS) models, involve stochastic treatment of the model input s



### 28 (Burke et al., 2001, 014050 ; U.S. EPA, 2009, 194009 )

. These are described in detail in the 2008 NO X


### 29 ISA (U.S. EPA, 2008, 157073 ) , in AX 3.6.1 . Stochastic models utilize distribution s of pollut antrelated and individual

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



### 31 breathing rate respectively, to compute the distribution of indiv idual exposure s across the modeled



### 32 population

. The models also have the capability to estimate received dose through a dosimetry



### 33 model. Using distributions of input parameters in the model framework rather than point estimates



### 34 allows the models to incorpor ate uncertainty and variability explicitly into exposure estimates (Zidek

et al., 2007, 190076 )

. These models estimate time

- weighted exposure for mod eled individuals by



### 36 summing exposure in each microenvironment visited during the exposure period.

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

may come from a monitoring network or model estimates. Estimates of concentrations in a set of

microenvironments are generated either by mass balance methods or microenvironmental factors.

Microenvironments modeled include indoor residences; other indoor locations, such as schools,

offices, and public buildings; and vehicles. The sequence of microenvironments and exertion levels

during the exposure period is determined from characteristics of each modeled individual. The

APEX model does this by generating a profile for each simulated individual by sampling from

distributions of demog raphic variables such as age, gender, and employment; physiological variables

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

conditioning. Activity patterns from a database such as Consolidated Human Activity Database

(CHAD) are assigned to the simulated individual using age, gender, and biometric characteristics

(U.S. EPA, 2009, 194010 ) . Breathing rates are calculated for each activity based on exertion level,

and the corresponding received dose may then be computed. Summaries of individual - and

population

- level metrics are produced, such as maximum exposure or dose, number of individuals

exceeding a specified exposure/dose threshold, and number of person

- days at or above benchm ark

exposure levels. The models also consider the nonambient contribution to total exposure.

Nonambient source terms are added to the infiltration of ambient pollutants to calculate the total

concentration in the microenvironment. Output from model runs wi th and without nonambient

sources can be compared to estimate the ambient contribution to total exposure and dose.

An analysis has been conducted for the APEX model to evaluate the contribution of

uncertainty in input parameters and databases to the uncert ainty in model outputs (Langstaff, 2007,

090315 ) . The Monte Carlo analysis indicates that the uncertainty in model exposure est imates for

asthmatic children during moderate exercise is small to moderate, with 95% confidence intervals of

at most ± 6 percentage points at exposures above 60, 70, and 80 ppb (8

- h avg) However, APEX

appears to substantially underestimate the frequency o f multiple high

- exposure events for a single

individual. The two main sources of uncertainty identified were related to the activity pattern

database and the spatial interpolation of fixed

- site monitor concentrations to other locations. One

area of potential improvement in the activity pattern database is additional information on children's

activities. Improved information on spatial variation of O 3 concentrations, including in near -roadway

and indoor microenvironments, would also contribute to reduced unc ertainty. Another area of need is

for improved personal exposure monitors with shorter averaging times to capture peak exposures and

lower detection limits to capture low indoor concentrations. A similar modeling approach has

recently been developed which is suitable for panel epidemiologic studies or for controlled human

exposure studies, in which activity pattern data specific to the individuals in the study can be

collected. Time

- activity data is combined with questionnaire data on housing characteristic s,
presence of indoor or personal sources, and other information to develop a personalized set of model

input parameters for each individual. This model, the Exposure Model for Individuals, is being

developed by EPA's National Exposure Research Laboratory.

March 2011 4- 15 DRAFT – DO NOT CITE OR QUOTE

### 1 Recent larger

- scale human activity databases, such as those developed for the CHAD or the



### 2 National Human Activity Pattern Survey (NHAPS), have been designed to characterize exposure



### 3 patterns among much larger population subsets than can be examined during individual panel studies



### 4 (Klepeis et al., 2001, 002437 ; McCurdy et al., 2000, 000782 ). CHA D consists of a consolidation of



### 5 human activity data obtained during several panel studies in which diary or retrospective activity



### 6 data were obtained, while NHAPS acquired s ample population time

- activity data through surveys



### 7 about human activity (Klepeis et al., 2001, 002437 )

. The complex human activity patterns across the



### 8 population (all ages) are illustrated in Figure 4--2 (Klepeis et al., 2001, 002437 ) , which is presented



### 9 to illustrate the diversity of daily activities among the entire population as well as the proportion of



### 10 time spent in each microenvironment. For example, about 25% of the individuals reported being



### 11 outdoors or in a vehicle between 2:00 and 3:00 pm, when daily O 3 levels are peaking, although about



### 12 half of this time was spent in or near a vehicle, where O 3 concentrations are likely to be lower than



### 13 ambient concentrations. Different patterns would be anticipated when breaking down activity



### 14 patterns only for subgroups such as children or the elderly. Population exposures can be estima ted



### 15 using O 3 concentration data in each microenvironment.

Source: Reprinted with permission from Nature Publishing Group, Klepeis et al

. (2001, 002437 )

.



### Figure 4-2. Distribution of time that NHAPS respondents spent in ten microenvironments based
on smoothed 1

- min diary data

.

March 2011 4- 16 DRAFT – DO NOT CITE OR QUOTE

## 4.4.3. Hyb rid Mod e l Approaches

1 Georgopoulos et al. (2005, 080269 ) used the ME NTOR/SHEDS modeling framework to



### 2 estimate O 3 exposure in Philadelphia over a 2

- week period in July 1999. They found that both the



### 3 50th and 95th percentile O 3 concentrations were correlated with census

- tract level outdoor



### 4 concentrations estimated by photoch emical modeling combined with spatiotemporal interpolation,



### 5 and attributed this correlation to the lack of indoor sources of O 3 . Relationships between exposure



### 6 and concentrations at fixed

- site monitors were not reported.



# 4.5. Im p lic a tio n s for Ep id e mio lo g ic Stu d ies

4.5.1. Expo s ure Me as u re me n t Erro r

7 Exposure error can be an important contributor to variability in epidemiologic study results.



### 8 Community

- level time

- series studies may involve a population of thousands or millions of people



### 9 across an urban area whose exposure and health status is estimated over the course of a few years



### 10 using a short monitoring interval (hours to days). Community

- averaged concentration is typically



### 11 used as a surrogate for ambient exposure in community time

- series studies. Exposures and health



### 12 effects are spatially aggregated over the time intervals of interest because community time

- series



### 13 studies are designed to examine health effects and their potential causes at the community level. A



### 14 longitudinal cohort epidemiology study , such as the ACS c ohort study, typically involves hundreds



### 15 or thousands of subjects followed over several years or decades (e.g., Jerrett et al., 2009, 194160 )

. 

### 16 Concentrations are generally aggregated over time and by community to estimate exposures. In



### 17 addition, panel studies, which consist of a relatively small sample (typically tens) of study



### 18 participants followed over a period of days to months, have been used to examine the health effects



### 19 associated with exposure to ambient concentrations of air pollutants (e.g., Delfino et al., 1996,



### 20 080788 ) . Panel studies may also apply a microenvironmental model to represent exposure to an air



### 21 pollutant.



### 22 Exposure error can mask epidemiologic associations between pollutants and health outcomes,



### 23 although this may be less of an issue for O 3 because it is a regional pollutant with relatively low



### 24 spatial variability. For example, a study in Atlanta observed associations between HRV parameters



### 25 and two traffic

- related pollutants (EC and NO 2 ), but the associations were only statistically



### 26 significant when personal exposures to the pollutants were used in the regression model (Suh and



### 27 Zanobetti, 2010, 677202 ) . No association was observed with ambient concentrations of these two



### 28 pollutants. In con trast, associations were similar for ambient concentrations and personal exposures



### 29 of O 3 and PM 2.5 , another regional pol lutant. Further discussion of the effect of spatial variability is



### 30 provided in Section 4.5.3.



### 31 The importance of exposure misclassification varies with study design and is dependent on the



### 32 spatial and temporal aspects of the design. For example, the use of a community

- averaged O 3

March 2011 4- 17 DRAFT – DO NOT CITE OR QUOTE

### 5

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1 concentration in a community time

- series epidemiologic study may not allow for adequate



### 2 examination of the role of spatial variability. Other factors that could influence exposure estimates



### 3 include nonambient exposure; spatial and t emporal variability, topography of the natural and built



### 4 environment, and meteorology; measurement errors; use of ambient O 3 concentration as a surrogate

for ambient O 3 exposure; and the presence of O 3 in a mixture of pollutants. The following sections



### 6 wil l consider various sources of error and how they affect the interpretation of results from



### 7 epidemiologic studies of different designs.



## 4.5.2. No na mbien t Ozon e Expo s ure

8 For other criteria pollutants, nonambient sources can be an important contributor to total



### 9 pers onal exposure. There are relatively few ind oor sources of O 3 , so personal O 3 exposure is

expected to be dominated by ambient O 3 in outdoor microenvironments and in indoor



### 11 microenvironments with high air exchange rates (e.g., with open windows). Even in



### 12 mic roenvironments where nonambient exposure is substantial, such as in a room with an O 3


### 13 generator, this nonambient exposure is unlikely to be temporally correlated with ambient O 3


### 14 exposure (Wilson and Suh, 1997, 077408 ) , and therefore would not affect epidemiologic associations

between O 3 and the health effect (Sheppard et al., 2005, 079176 ) . In simulations of a nonreactive



### 16 pollutant, Sheppard et al. (2005, 079176 ) concluded that nonambient exposure does not influence the



### 17 health out c ome effect estimate if ambient and nonambient concentrations are independent. It should



### 18 be noted that the effect estimate calculated from using personal exposure to ambient O 3 rather than



### 19 ambient concentration will be inc reased in inverse proportion to the ratio of ambient exposure to

ambient concentration, and daily fluctuations in this ratio can widen the confidence intervals in the



### 21 ambient concentrati on effect estimate, but uncorrelated nonambient exposure will not bias the effect



### 22 estimate.



## 4.5.3. Spatiotemporal Variability

4.5.3.1. Spatial Varia bility

23 Compared with directly emitted pollutants such as CO and NO X , O 3 exhibits relatively low



### 24 spatial variability across urban areas, as discussed in Chapter 3. Spatial variability contribute s to

exposure error if the ambient O 3 concentration measured at the central site monitor is used as an



### 26 ambient exposure surrogate and differs from the actual ambient O 3 concentration outside a subject’s



### 27 residence and/or worksite (in the absence of indoor O 3 sources). Averaging data from a large number



### 28 of samplers will dampen intersampler variability , and use of multiple monitors over smaller land



### 29 areas may allow for more variability to be incorporated into an epidemiologic analysis

. Community exposure may not be well represented when monitors cover large areas with



### 31 several subcommunities having different sources and topographies , such as the Los Angeles CSA



### 32 (Chapter 3) . O zone monitors in Los Angeles had a much wider range of intermonitor correlations

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1 (-0.06 to 0.97) than Atlanta (0.61 to 0.96) or Boston (0.56 to 0.97) using 2007

- 2009 data. Although



### 2 the negative and near

- zero correlations in Los Angeles were observed for monitors located some



### 3 distance apart (>150 km), some closer monitor pairs had low positive correlations, likely due to



### 4 changes in topography and airflow patterns over short distances. The spatial variability in O 3

concentration in 24 MSAs across the U.S. was examined in the 2006 O 3 AQCD (U.S. EPA, 2006,



### 6 088089 ) . Spatial variability was examined by using Pearson correlation coefficients, values of the



### 7 90th percentil e of the absolute difference in O 3 concentrations, and COD s . No clear discernible



### 8 regional differences across the U.S. were found in the ranges of parameters analyzed.



### 9 However, spatial variation in atmospheric constituents that participate in O 3 formation and

titration reactions contribute to s patial variability in some areas , particularly near roadways (Section



### 11 3.6.2.1) . Liu et al. (1995, 039061 ) conducted a O 3 exposure assessment study in Toronto, Canada



### 12 during the winter and summer of 1992. Results indicated that outdoor O 3 concentrations exhibited



### 13 spatial variation over the study area. The spatial variation is believed to result from population and



### 14 traffic density. The results are consistent with a smaller

- scale study by McKendry (1993, 677201 ) in

Montreal, Canada, in which the investigator examined spatial variation using measurements from



### 16 nine ambient monitoring sites and found the spatial pattern of O 3 concentration to be more distinct in



### 17 the winter than in the summer.



### 18 Sarnat et al

. (2010, 385852 ) studied the spatial variability of O 3 , along with PM 2.5 , NO 2 , and



### 19 CO , in the Atlanta, GA

, metropolitan area and evaluated how spatial variability affects interpretation

of epidemiologic results, using time

- series data for circulatory disease ED visits. The authors found



### 21 that associations with ambient O 3 concentration were similar among all sites tested, including



### 22 multiple urban sites and a rural site some 3 8 miles from the city center. This result was also observed



### 23 for PM 2.5 , another regional pollutant. In contrast, the more spatially variable pollutants CO and NO 2


### 24 showed different associations for the rural site than the urban sites, although the urban site

associations were similar to one another for CO. This suggests that choice of monitor may have little



### 26 impact on the results of O 3 epidemiologic studies.



## 4.5.3.2. Se a s ona lity

27 The relationship between personal exposure and ambient concentration has been found to var y



### 28 by season, with at least three factors potentially contributing to this variation: differences in building



### 29 ventilation ( e.g., air conditioning or heater use versus open window ventilation), higher O 3 concentrations during the O 3 season contributing to increased exposure and improved detection by



### 31 personal monitors; and changes in activity pattern resulting in more time spent outside. Evidence has



### 32 been presented in studies conducted in several cities regarding the effect of ventilation on personal



### 33 ambient a nd indoor

- outdoor O 3 relationships (see S ections 4.3.3 and 4.3.4). More limited evidence is



### 34 available regarding the specific effects of O 3 detection limits and activity pattern changes on O 3 relationships.



### 36 Several studies have found increased summertime co rrelations or ratios between personal



### 37 exposure and ambient concentration (Sarnat et al., 2000, 001852 ; Sarnat et al., 2005, 087531 ) or

March 2011 4- 19 DRAFT – DO NOT CITE OR QUOTE

### 5

10

15

20

25

30

1 between indoor and outdoor O 3 concentrations (Avol et al., 1998, 018270 ; Geyh et al., 2000,



### 2 001775 ) . However, others have found higher ratios in fall than in summer (Sarnat et al., 2006,



### 3 089784 ) or equivalent, near

- zero ratios in winter and summer (Sarnat et al., 2001, 019401 ) , possibly



### 4 because summertime use of air conditioners decreases building air exchange rates. It should be noted

that O 3 concentrations during winter are generally much lower than summertime concentrations,



### 6 possibly obscuring wintertime rela tionships due to detection limit issues. Studies specifically



### 7 evaluating the effect of ventilation conditions on O 3 relationships have found increased correlations



### 8 or ratios for individuals or buildings experiencing higher air exchange rates (Geyh et al., 2000,



### 9 001775 ; Romieu et al., 1998, 049834 ; Sarnat et al., 2000, 001852 ; Sarnat et al., 2006, 089784 )

. Increased correlations or ratios between personal exposure and ambient concentration, or



### 11 between indoor and outdoor concentration, are likely to reduce error in exposure estimates used in



### 12 epidemiologic studies. This sug gests that studies conducted during the O 3 season or in periods when



### 13 communities are likely to have high air exchange rates (e.g., during mild weather) may be less prone



### 14 to exposure error than studies conducted only during winter. Year

- round studies that include both the

O 3 and non -O 3 seasons may have an intermediate level of exposure error.



## 4.5.4. Expos ure to Co

- pollutant s and Ozone Reaction Products

16 Although indoor O 3 concentrations are usually well below ambient concentrations, the same



### 17 reactions that consume O 3 indoors form particulate and gaseous species, including other oxidants, as



### 18 summarized in Section 4.3. 4 .3. Exposures to these reaction products would therefore be expected to



### 19 be correlated with ambient O 3 concentrations, and could potentially contribute t o health effects

observed in epidemiologic studies. Current evidence regarding personal exposures to these reaction



### 21 products is extremely limited.



## 4.5.5. Expo s ure Es tima tio n Meth od s in Epid e miolo gic Stu die s

22 The use of O 3 measurements from central ambient monitori ng sites is the most common



### 23 method for assigning exposure in epidemiologic studies. However, fixed

- site measurements do not



### 24 account for the effects of spatial variation in O 3 concentration, ambient and non

- ambient

concentration differences, and varying act ivity patterns on personal exposures (Brown et al., 2009,



### 26 190895 ; Chang et al., 2000, 001276 ; Zeger et al., 2000, 001949 )

. The use of fixed

- site



### 27 concentrations results in minimal exposure error when : (1) O 3 concentr ations are uniform across the



### 28 region; (2) personal activity patter n s are similar across the population; and (3) housing



### 29 characteristic s, such as air exchange rate and indoor reaction rate, are constant over the study area.

Since these factors vary by locat ion and population, there will be errors in the magnitude of total



### 31 exposure based solely on ambient monitoring data.



### 32 As described in Section 4.3.4, results from previous and recently published studies indicate



### 33 that while the relationship between personal exposures and ambient concentrations varies due to a



### 34 number of factors, such as activity patterns, housing characteristics, and season, O 3 concentrations

March 2011 4- 20 DRAFT – DO NOT CITE OR QUOTE

### 1 measured at central

- site monitors are representative of day

- to

- day changes in average personal O 3


### 2 expos ure, which is the important parameter for time

- series epidemiologic studies. Another important



### 3 finding is that the magnitude of personal exposures is smaller than concentrations reported at fixed


### 4 site monitors due to time spent indoors and the low indoor p enetration of O 3

. his tends to support the



### 5 use of fixed

- site concentrations as exposure estimates in epidemiologic studies.



### 6 Modeling approaches can also be used to estimate exposures for epidemiologic studies, as



### 7 discussed in Section 4.4. Geostatistical spatial interpolation techniques can provide finer

- scale



### 8 estimates of local concentration over urban areas. A microenvironmental modeling approach



### 9 simulates exposure using empirical distributions of concentrations in specific microenvironments



### 10 together with human activity pattern data

. The main advantage of the modeling approach is that it



### 11 can be used to estimate exposures over a wide range of population and scenarios. A main



### 12 disadvantage of the modeling approach is that the results of modeling exposure assessment must be



### 13 compared to an independent set of measured exposure level s (Klepeis, 1999, 001697 ) . In addition,



### 14 res ource- intensive development of validated and representative model inputs is required, such as



### 15 human activity patterns, distributions of air exchange rate, and deposition rate. Therefore, modeled



### 16 exposures are used much less frequently in epidemiologic stud ies.



# 4.6. S ummary and Conclus ions

4.6.1. Expo s ure Me as u re me n t

4.6.1.1. Me a s ure me n t o f Ozone Exposure

17 Passive badge samplers are the most widely used technique for measuring personal O 3


### 18 exposure. The operate on the nitrite

- nitrate conversion principle, and are convenient since they



### 19 require no pumps or wet chemistry in the field. They represent a cumulative (rather than continuous)



### 20 sample, and their detection limit makes them suitable for monitoring periods of 24 h ours or greater.



### 21 his limits their applicability in measuring short

- term daily fluctuations in personal exposure. Over a



### 22 24

- h period, the detection limit of the badges is approximately 5

- 10 ppb, which may result in an



### 23 appreciable fraction of the samples being below the detection limit. An active sampler based on the



### 24 nitri te

- nitrate conversion reaction is also available, with a reported detection limit of 10 ppb -h,



### 25 enabling measurement of sub

- daily O 3 concentrations. A portable continuous O 3 monitor based on a



### 26 different principle, UV absorption, has recently become availabl e. Its size and weight make it



### 27 suitable for use in a backpack configuration, although its use for personal exposure measurements



### 28 has been limited.



### 29 Several studies described in the 2006 O 3 AQCD, along with a few new studies published since,



### 30 describe the rel ationship between indoor O 3 concentration and the O 3 concentration immediately



### 31 outside the indoor microenvironment. T hese studies show that the indoor concentration is often



### 32 substantially lower than the outdoor concentration unless indoor sources are present. Low indoor O 3

March 2011 4- 21 DRAFT – DO NOT CITE OR QUOTE

### 1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38 concentrations can be explained by reaction of O 3 with surfaces and airborne constituents. H owever,

the indoor

- outdoor relationship is greatly affected by the air exchange rate; under conditions of high

air exchange rate, such as open win dows, the indoor O 3 concentration may approach the outdoor

concentration. In residential microenvironments, studies report indoor

- outdoor ratios ranging from

approximately 0.1

- 0.4, with the highest ratios observed in the summer O 3 season and for homes with

increased window ventilation. A correlation of 0.58 was reported between indoor and outdoor O 3 concentrations, indicating that variations in outdoor concentration may be reflected indoors, though

the magnitude of the concentration is lower. Indoor

- outdoor ratios at schools were similar, with

higher ratios observed during the school day when opening doors and windows may lead to

increased air exchange rates. In vehicles, high air exchange rates that would normally lead to high

interior- exterior concentration ratios are offset by O 3 scavenging through vehicle

- emitted NO,

resulting in reported in

- vehicle concentrations that were approximately 50% of those measured at the

roadside.

The relationship between personal exposure and ambient O 3 concentrations has bee n evaluated

in several research studies, many of which were conducted prior to 2005 and are discussed in the

2006 O 3 AQCD. The results of these studies indicate that personal exposures are moderately well

correlated with ambient concentrations, and that th e ratio of personal exposure to ambient

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

where a lack of correlation was observed, this may be due in part to a high proportion of personal

measurements below the detection limit. Correlations reported for daily or multi

- day measurements

range from approximately 0.3

- 0.8, with the upper end of the range reflecting longer

- duration (4 -day)

community average measurements that may limit the influence of inter

- individu al variability in

exposure. Hourly measurements in specific microenvironments show greater variability in

correlations between personal exposure and ambient concentration, with residential indoor

correlations <0.1 and outdoor correlations of 0.7

- 0.9. Slope s from regression analyses of personal

exposure on ambient concentration generally ranged from approximately 0.1

- 0.3. Higher slopes were

observed in studies that either adjusted for activity pattern and air exchange rate (0.54) or focused on

outdoor shoe c leaners (0.56), who may have increased exposure due to spending a substantial

fraction of the day outdoors. Ratios of personal exposure to ambient concentration showed similar

results, with a ratio of 0.3 reported for a year

- round study in southern Califor nia, while ratios ranged

from 0.28

- 0.96 for outdoor workers, increasing with time spent outdoors.

Taken together, results from previous and recently published studies indicate that while the

relationship between personal exposures and ambient concentration s varies due to a number of

factors, such as activity patterns, housing characteristics, and season, O 3 concentrations measured at

central

- site monitors are representative of day

- to

- day changes in average personal O 3 exposure,
which is the important parame ter for time

- series epidemiologic studies. Another important finding is

that the magnitude of personal exposures is smaller than concentrations reported at fixed

- site

monitors due to time spent indoors and the low indoor penetration of O 3

.

March 2011 4- 22 DRAFT – DO NOT CITE OR QUOTE

### 5

10

15

20

25

30

35 

## 4.6.1.2. Co

- Exposure to O zone a nd Othe r Polluta nts

1 Individuals may be exposed to other pollutants in conjunction with exposure to O 3 . Personal



### 2 exposure to O 3 shows variable association with personal exposure to other pollutants, with



### 3 differences in association depending on factors such as season, city

- specific characteristics, and



### 4 spatial variability of the co -pollutant . For PM 2.5 , a rank correlation of 0.14 was reported between

daily O 3 and PM 2.5 exposures during spring and fall in Atlanta. Positive slopes were reported during



### 6 sum mer in both Baltimore and Boston, although the slopes were somewhat different (0.21 and 0.72,



### 7 respectively). The summertime slope in Baltimore was higher for children (0.37) than for adults



### 8 (0.07), which may be the result of different activity patterns and time spent outdoors. Additional



### 9 evidence of variation by season and city is provided by the differing signs of the wintertime slopes,

with Baltimore sho w ing a negative slope and Boston showing a positive slope. Interindividual



### 11 variability likely played a role as well, since both cities showed a wide range (including both



### 12 negative and positive values) for individual

- specific personal O 3 - PM 2.5 slopes. For EC and NO 2 , 

### 13 near

- zero correlations were reported with O 3 during spring and fall in Atlanta. These extre mely low



### 14 correlations for the traffic

- related and spatially variable pollutants EC and NO 2 contrast with the

higher correlation observed for PM 2.5 , a regional pollutant.



### 16 In near

- road and on

- road microenvironments, correlations between O 3 and traffic

- relate d



### 17 pollutants are moderately to strongly negative, with the most strongly negative correlations observed



### 18 for NO 2 (- 0.8 to

- 0.9). This is consistent with the chemistry of NO oxidation, in which O 3 is



### 19 consumed to form NO 2 . The more moderate negative correlati ons observed for PM 2.5 , PM 1.0 , and

VOC may reflect reduced concentrations of O 3 in more polluted environments due to other



### 21 scavenging reactions. A similar process occurs indoors, where infiltrated O 3 reacts with airborne or



### 22 surface

- associated materials to form secondary compounds, such as formaldehyde. Although such



### 23 reactions decrease indoor O 3 exposure, they result in increasing exposure to other species which may



### 24 themselves have health effects.



## 4.6.2. Expo s ure Mo delin g

Exposure s estimates in urban areas may be i mproved by constructing a concentration surface



### 26 over a geographic domain using a model to compensate for missing data

. The calculated



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



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



### 29 directly because it does not account for activity patterns or concentrations in different

microenvironments. Most such modeling efforts have focused on the less

- reactive pollutants PM or



### 31 NO 2

. In a study that extended CALINE4 NO X modeling results to evaluate the impact on residential



### 32 O 3 concentrations (see Section 4.4.1), O 3 concentrations were reduced by 0.51 ppb O 3 per 1 ppb



### 33 NO X

. This intra

- urban traffic

- related variability in O 3 concentration s suggests that differences in



### 34 traffic density between the central site monitor and individual homes could result in either an

overestimate or underestimate of residential O 3

.

March 2011 4- 23 DRAFT – DO NOT CITE OR QUOTE

### 5

10

15

20

25

30

35

1 A separate class of models, known as microenvironmental models, estimate time -weighted



### 2 exposure for modeled individuals by summing exposure in each microenvironment visited during the



### 3 exposure period. Stochastic microenvironmental models , such as APEX and SHEDS, utilize



### 4 distribution s of pollut ant- related and individual

- level variable s, such as ambient and local O 3 concentration contributions and breathing rate respectively, to compute the distribution of individual



### 6 exposure s across the modeled population

. The models also have the capability to estimate received



### 7 dose through a dosimetr y model. Using distributions of input parameters in the model framework



### 8 rather than point estimates allows the models to incorporate uncertainty and variability explicitly into



### 9 exposure estimates

. For the APEX model, an analysis has been conducted indicati ng that the

uncertainty in model exposure estimates for asthmatic children during moderate exercise is small to



### 11 moderate; however, APEX appears to substantially underestimate the frequency of multiple high


### 12 exposure events for a single individual. Microenvironmental models, such as EMI, are also being



### 13 developed to use individual

- specific information derived from measurements or questionnaires,



### 14 rather than population distributions, to estimate exposures. This approach is particularly suitable for

panel health studies where information is available for each participant, and may reduce uncertainty



### 16 in health effect estimates by improving exposure estimates.



## 4.6.3. Implic a tio ns fo r Epide miolo gic Stu die s

17 Exposure error can be an important contributor to variability in epi demiologic study results

, 

### 18 although this may be less of an issue for O 3 because it is a secondary pollutant with relatively low



### 19 spatial variability across an urban area

. For example, an epidemiologic study in Atlanta observed

similar associations between HR V parameters and either ambient concentrations or personal



### 21 exposures of O 3 and PM 2.5 , another regional pollutant. The importance of exposure error varies with



### 22 study design and is dependent on the spatial and temporal aspects of the design. Several factors that



### 23 could influence exposure estimates include nonambient exposure, spatial and temporal variability,



### 24 and the presence of O 3 in a mixture of pollutants. Nonambient exposure is unlikely to influence

health effect estimates because of the lack of indoor O 3 sources and because indoor

- generated O 3


### 26 exposures are unlikely to be correlated with ambient O 3 exposure. Compared with directly emitted



### 27 pollutants such as CO and NO X , O 3 exhibits relatively low spatial variability across urban areas, as



### 28 discussed in Chapt er 3. Averaging data from a large number of samplers will dampen intersampler



### 29 variability , and use of multiple monitors over smaller land areas may allow for more variability to be

incorporated into an epidemiologic analysis

. Evidence from a study comparin g the effect of spatial



### 31 variability on effect estimates for O 3 , PM 2.5 , NO 2 , and CO suggests that choice of monitor for more



### 32 spatially homogenous pollutants such as O 3 may have little impact on the results of epidemiologic



### 33 studies. Season, however, may have a substantial effect due to much lower O 3 concentrations during



### 34 the winter, along with the higher correlations between ambient concentrations and personal

exposures observed during the summer. Studies conducted during the O 3 season or in periods when



### 36 comm unities are likely to have high air exchange rates (e.g., during mild weather) may be less prone



### 37 to exposure error than studies conducted only during winter. Year

- round studies that include both the

March 2011 4- 24 DRAFT – DO NOT CITE OR QUOTE

### 1

2

3

4

5

6

7

8

9

10

11 O 3 and non -O 3 seasons may have an intermediate level of e xposure error. Exposure to mixtures of

pollutants containing O 3 also complicates interpretation of epidemiologic results. Moderate to strong

negative correlations between O 3 and traffic

- related pollutants, particularly NO 2 , make it difficult to

determine t o what extent O 3 - based effect estimates quantitatively reflect the independent effect of O 3 itself, or the effect of another pollutant or pollutants in the mixture. Interpretation of O 3 effects in the

presence of PM is additionally complicated by the highly variable correlations observed, which

differ by city, season, and population characteristics (e.g., children versus adults). Although these

sources of exposure error should be considered in evaluating epidemiologic results, previous and

recently publishe d exposure research indicate that O 3 concentrations measured at central

- site

monitors are indicative of day

- to

- day changes in average personal O 3 exposure, making ambient

concentrations a useful parameter for epidemiologic studies.

March 2011 4- 25 DRAFT – DO NOT CITE OR QUOTE

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