Chapter 4. Exposure to Ambient Ozone
 

4.1. In tro d u c tio n 

1 The 2006 O3 AQCD evaluated O3 concentrations and exposures in multiple 

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

3 monitoring and modeling, analyzed relationships between personal exposure and ambient 

4 concentrations, and discussed the implications of using ambient O3 concentrations as an estimate of 

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

6 ambient O3 in the context of existing relevant information summarized in the 2006 O3 AQCD, which 

7 in many areas remains definitive. A brief summary of findings from the 2006 O3 AQCD is presented 

8 at the beginning of each section as appropriate. 

9 Section 4.2 presents general 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 O3 

12 and related pollutants. Section 4.4 describes techniques for modeling local O3 concentrations, 

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

14 implications of using ambient O3 concentrations to estimate exposure 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 O3 

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

19 period: 

ET = ∫C jdt 

Equation 4-1 

20 where ET = total (T) exposure over a time-period of interest, Cj = airborne O3 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 O3, of ambient (Ea) and nonambient 

23 (Ena) 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 Information System (IRIS). 

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E T a na = E + E

Equation 4-2 

1 Ambient O3 is formed through photochemical reactions involving NOX, VOCs, and other 

2 compounds, as described in Chapter 3. Although nonambient sources of O3 exist, such as O3 

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 variability that can affect estimates of 

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

7 of epidemiologic 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. Ea 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): 

Ea = ∑ foCo +∑ fiFinf, iCo,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 microenvironments, subscript i = index of indoor microenvironments, subscript o,i = 

13 index of outdoor microenvironments adjacent to a given indoor microenvironment i, and Finf,i = 

14 infiltration factor for indoor microenvironment (i). Equation 4-3 is subject to the constraint Σfo + 

15 Σfi = 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 motorcycles. 

20 Finf 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 O3, the air exchange rate (a) 

22 of the microenvironment, and the rate of O3 loss (k) in the microenvironment; Finf = Pa/(a+k). 

23 In epidemiologic studies, Ca is often used in lieu of outdoor microenvironmental data to 

24 represent these exposures based on the availability of data. Thus it is often assumed that Co = Ca and 

25 that the fraction of time spent outdoors can be expressed cumulatively as fo; the indoor terms still 

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

27 epidemiologic study employs only Ca, then the assumed model of an individual’s exposure to 

28 ambient O3, first given in Equation 4-3, is re-expressed solely as a function of Ca: 

Ea = (fo +∑ fiP)Ca 

Equation 4-4 

29 Meteorology, varying precursor emissions and O3 formation rates, spatial variability of O3 

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 are 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; thus α, the ratio between personal exposure 

4 to ambient O3 and the ambient concentration of O3, is defined as: 

Ea α = Ca 

Equation 4-5 

5 Combination of Equation 4-4 and Equation 4-5 yields: 

α = fo +∑ fiP 

Equation 4-6 

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

7 ambient component of a microenvironmental O3 concentration can be represented as the product of 

8 the ambient concentration and P. Wallace et al. (2006, 089190) note 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 models, fine scale CTMs 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 O3 AQCD (U.S. EPA, 2006, 088089), a passive sampler has been 

15 developed and deployed to measure personal exposure to O3. This sampler uses a filter coated with 

16 nitrite, which is converted to nitrate by O3 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 suitable for monitoring periods of 24 hours or greater, which 

20 limits their ability to measure short-term daily fluctuations in personal O3 exposure. Longer 

21 sampling periods give lower detection limits; use of the badges for a 6-day sampling period yields a 

22 detection 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., 2005, 087531; Sarnat et al., 2006, 089784). 

25 The nitrite-nitrate 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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1 10 ppb-h, enabling the quantification of O3 concentrations measured over a few hours rather than a 

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

3 A portable active O3 monitor based on the UV photometric technique used for stationary 

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 O3 

9 in several national parks (Chapter 3). 

4.3.2. Ind oo r-Outdoor Concentration Relations hips 

The 2006 O3 AQCD (U.S. EPA, 2006, 088089) presented results from several studies on the 

11 relationship between indoor O3 concentration and the O3 concentration immediately outside the 

12 indoor microenvironment. These studies show that the indoor concentration is often substantially 

13 lower than the outdoor concentration unless indoor sources are present. Low indoor O3 

14 concentrations can be explained by reactions of O3 with surfaces and airborne constituents. Studies 

have shown that O3 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 indoor17 outdoor relationship is greatly affected by the air exchange rate; under conditions of high air 

18 exchange rate, such as open windows, the indoor O3 concentration may approach the outdoor 

19 concentration. Geyh et al. (2000, 001775) measured 6-day (approximately 144 hours) indoor and 

outdoor concentrations at 116 homes in southern 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 opening 

24 windows. During the O3 season, the indoor O3 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 mountain 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 December 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 slightly 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; Liu 

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 O3 concentrations measured during 

7 school hours (10-day cumulative 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 school 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, 078044). 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 O3 concentration of 106 ppb at one 

15 university and four elementary school playing fields, indicating the potential for elevated O3 

16 exposure in the school outdoor microenvironment. 

17 Gradients in O3 concentrations observed near roadways provide evidence of the NO-O3 

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

19 reacts with O3 to produce NO2 (Finlayson-Pitts and Pitts, 1986, 035054). Ozone 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, O3 concentrations near 

22 the roadway have been found to be 20-80% 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 O3 concentrations has been evaluated 

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

30 2006 O3 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 personal-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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subjects. 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-series 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 detection limit. Chang et al. (2000, 001276) measured hourly personal exposures in 

multiple microenvironments and found that the pooled correlation between 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 microenvironments such as restaurants and shopping malls. Liard et al. 

(1999, 001426) evaluated community-average correlations based on 4-day mean personal O3 

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 concentration 

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 wellventilated 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 southern California schoolchildren who spent an average of 101-136 minutes 

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 

exposure 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/day, 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 increased 

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

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1 This indicates that temporal variations in personal exposure to O3 are driven by variations in ambient 

2 concentration, even for individuals that spend little time outdoors. 

4.3.3.2. Personal-Ambie nt Ra tios 

3 Additional studies were summarized in the 2006 O3 AQCD (U.S. EPA, 2006, 088089) 

4 evaluating the ratio of personal O3 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 Baltimore 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 areas 

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 southern 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 R2 value of 0.58. Unadjusted regression slopes were not presented. 

19 A few studies have been published since the 2006 O3 AQCD comparing personal exposures 

with ambient concentrations, and these findings generally confirm the conclusions of the 2006 O3 

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 low23 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 O3 concentrations were within 6 ppb of O3 concentrations measured at 

28 central-site monitors in each of three southern California communities, with a combined average 

29 home outdoor concentration of 33 ppb compared to the central-site average of 36 ppb. RamirezAguilar 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, results from previous and recently published studies indicate that while the 

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

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1 factors, such as activity patterns, housing characteristics, and season, O3 concentrations measured at 

2 central-site monitors are representative of day-to-day changes in average personal O3 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 O3. 

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 nts 

6 Personal exposure to O3 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 O3 measurements and co-pollutants 

10 of 0.14, 0.00, and -0.03 for PM2.5, EC, and NO2, respectively. Note the higher correlation observed 

11 for PM2.5, a regional pollutant, in contrast with the extremely low correlations for the traffic-related 

12 and more spatially variable pollutants EC and NO2. Chang et al. (2000, 001276) measured hourly 

13 personal exposures to PM2.5 and O3 in summer and winter in Baltimore, Maryland. Correlations 

14 between PM2.5 and O3 were 0.05 and -0.28 in summer and winter, respectively. Results indicate 

15 personal O3 exposures were not significantly associated with personal PM2.5 exposures in either 

16 summer or winter. These non-significant correlations may be attributed in part to the relatively low 

17 personal O3 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 O3 and personal PM2.5 

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

21 when regressing personal exposures of both total PM2.5 (0.21) and PM2.5 of ambient origin (0.22) 

22 against personal O3 exposures during the summer season, but negative slopes (-0.05 and -0.18, 

23 respectively) during the winter season. The summertime slope for personal PM2.5 exposure versus 

24 personal O3 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 O3 and personal PM2.5 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 cities during both seasons, there was a wide range of subject-specific correlations between 

29 personal O3 and personal PM2.5 PM2.5, 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 O3 

34 and co-pollutants. 

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4.3.4.2. Ne a r-Road Expos ure to Ozone and Co -polluta nts 

1 Beckerman et al. (2008, 096484) measured both 1-week and continuous concentrations of O3, 

2 NO, NO2, NOX, PM2.5, PM1.0, and several VOCs (the BTEX compounds, MTBE, hexane, and THC) 

3 in the vicinity of heavily traveled (annual average daily traffic [AADT] >340,000) roadways 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 small area sources. Site specific 

7 correlations are given in Figure 4-1. Correlations were -0.77 to -0.85 for NO2, -0.48 to -0.62 for NO, 

8 and -0.55 to -0.63 for NOX. Pooled correlations using data from both sites were somewhat lower in 

9 magnitude. PM2.5 and PM1.0 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, O3-VOC correlations 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 O3 and other 

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

15 correlations. SO2, CO, and BC were measured in the mobile laboratory, although not at the roadside, 

16 and they all showed negative correlations with O3 when the data were controlled for site. 

17 Correlations for continuous concentrations between O3 and co-pollutants were somewhat lower than 

18 the 1-week correlations, except for O3-PM2.5 correlations. Correlations were -0.90, -0.66, -0.77, and ­

19 0.89 for NO2, NO, NOX, and PM1.0 respectively. The continuous O3-PM2.5 correlation was -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 ozone concentrations and co-pollutants. 

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4.3.4.3. Ind oor Expos ure to Ozone and Co-polluta nts 

1 Ambient O3 that infiltrates indoors reacts with organic compounds and other chemicals to 

2 form oxidized products, as described in the 2006 O3 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 occurring in the ambient air, as 

summarized in Chapter 3. For example, O3 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 reaction products may have 

health effects in addition to, or greater than, those from O3 itself (Anderson et al., 2007, 600123; 

11 Weschler and Shields, 1997, 084530). Ozone 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 O3 

concentration, temperature, and air exchange rate, making estimation of exposures to reaction 

16 products difficult. 

4.3.5. Population Proximity to Fixed-Site Ozone Monito rs 

17 The distribution of O3 monitors across urban areas varies between cities (Section 3.6.2.1), and 

18 the population living near each monitor varies 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 O3 monitor. Values 

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

24 population to the effects of O3. The data indicate that relatively few people live within 1 km of an O3 

monitor, while nearly all of the population in most cities lives within 20 km of a monitor. Many O3 

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 within this distance from an O3 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. 

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Table 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% 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% 

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 other. 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 two monitors closest to the city center (sites A and B on 

6 Figure 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 population 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 respect 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 

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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 between monitors 

5 estimated using a model to compensate for missing data. The calculated O3 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 interpolation of measured concentrations; statistical models using meteorological 

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

11 domain; and rigorous first-principle models, such as chemistry-transport models or dispersion 

12 models incorporating O3 chemistry. Some researchers have 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 short15 term concentrations estimated across urban areas. 

16 The 2006 O3 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 to 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 O3 concentrations in Vancouver, BC. The investigators assigned a daily average O3 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 inverse25 distance-weighted (IDW) average of all monitors in the area; and (4) the IDW average of the 3 

26 closest monitors within 50 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 of 

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 Method 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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except for locations immediately adjacent to a monitoring site. CMAQ concentration 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 O3 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 northern Georgia with 

nearest-monitor and spatial interpolation techniques, including IDW and kriging. 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 suitable 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 estimate levels of air pollutants, 

predominantly NO2, 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 from monitored 

concentrations as a function of data from a combination of the land use factors. The regression is 

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

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

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

correlated co-pollutants, 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 limitations may partially explain the lack of LUR models that 

have been developed for O3 at the urban scale. Brauer et al. (2008, 156292) evaluated the use 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 pollutants such as NO2 and PM2.5 with less 

spatial variability. Although this study did not evaluate an LUR model for O3, possibly due to the 

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

favored since O3 is a secondary pollutant. At a much larger spatial scale, EU-wide, Beelen et al. 

(2009, 601157) compared a LUR model for O3 with ordinary kriging and universal kriging, which 

incorporated meteorlogical, topographical, and land use variables to characterize the underlying 

trend. The LUR model performed reasonably well at rural locations (5-km resolution), explaining a 

higher percentage of the variability (R2 = 0.62) than for other pollutants. However, at the urban scale 

(1-km resolution), only one variable was selected into the O3 LUR model (high-density residential 

land use), and the R2 value was very low (0.06). Universal kriging was the best method for the largeMarch 2011 4-13 DRAFT – DO NOT CITE OR QUOTE 
 

 

 

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1 scale composite EU concentration map, for O3 as well as for NO2 and PM10, with an R2 value for O3 

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 O3 with NO emitted by motor vehicles tends to reduce O3 concentrations near 

6 roadways. McConnell et al. (2006, 089256) developed a regression model to predict residential O3 

7 concentrations in southern California using estimates of residential NOX calculated from traffic data 

8 with the CALINE4 line source dispersion model. The authors estimated that local traffic contributes 

9 18% of NOX concentrations measured in the study communities, with the remainder coming from 

regional background. Their regression model indicates that residential NOX reduces residential O3 

11 concentrations by 0.51 ppb O3 per 1 ppb NOX, and that a 10th-90th percentile increase in local NOX 

12 results in a 7.5 ppb decrease in local O3 concentrations. This intra-urban traffic-related variability in 

13 O3 concentrations suggests that traffic patterns are an important factor in the relationship between 

14 central site monitor and residential O3, and that differences in traffic density between the central site 

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

16 O3. 

17 A substantial number of researchers have used geostatistical methods and chemistry-transport 

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

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

In addition to short-term exposure assessment for epidemiologic studies, such models may also be 

21 used for long-term exposure assessment, O3 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 better 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 Stochastic Human 

27 Exposure and Dose Simulation (SHEDS) models, involve stochastic treatment of the model inputs 

28 (Burke et al., 2001, 014050; U.S. EPA, 2009, 194009). These are described in detail in the 2008 NOX 

29 ISA (U.S. EPA, 2008, 157073), in AX3.6.1. Stochastic models utilize distributions of pollutantrelated and individual-level variables, such as ambient and local O3 concentration contributions and 

31 breathing rate respectively, to compute the distribution of individual exposures 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 incorporate uncertainty and variability explicitly into exposure estimates (Zidek 

et al., 2007, 190076). These models estimate time-weighted exposure for modeled individuals by 

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

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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 demographic 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 benchmark 

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 with 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 uncertainty in model outputs (Langstaff, 2007, 

090315). The Monte Carlo analysis indicates that the uncertainty in model exposure estimates 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 of 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 O3 concentrations, including in near-roadway 

and indoor microenvironments, would also contribute to reduced uncertainty. 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 characteristics, 

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. 

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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). CHAD 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 sample 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 O3 levels are peaking, although about 

12 half of this time was spent in or near a vehicle, where O3 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 estimated 

15 using O3 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. 

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4.4.3. Hyb rid Mod el Approaches 

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

2 estimate O3 exposure in Philadelphia over a 2-week period in July 1999. They found that both the 

3 50th and 95th percentile O3 concentrations were correlated with census-tract level outdoor 

4 concentrations estimated by photochemical modeling combined with spatiotemporal interpolation, 

5 and attributed this correlation to the lack of indoor sources of O3. 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 dies 

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 cohort 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 O3 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 NO2), 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 contrast, associations were similar for ambient concentrations and personal exposures 

29 of O3 and PM2.5, another regional pollutant. 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 O3 

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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 temporal variability, topography of the natural and built 

4 environment, and meteorology; measurement errors; use of ambient O3 concentration as a surrogate 

for ambient O3 exposure; and the presence of O3 in a mixture of pollutants. The following sections 

6 will 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 personal exposure. There are relatively few indoor sources of O3, so personal O3 exposure is 

expected to be dominated by ambient O3 in outdoor microenvironments and in indoor 

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

12 microenvironments where nonambient exposure is substantial, such as in a room with an O3 

13 generator, this nonambient exposure is unlikely to be temporally correlated with ambient O3 

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

between O3 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 outcome 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 O3 rather than 

19 ambient concentration will be increased 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 concentration 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 NOX, O3 exhibits relatively low 

24 spatial variability across urban areas, as discussed in Chapter 3. Spatial variability contributes to 

exposure error if the ambient O3 concentration measured at the central site monitor is used as an 

26 ambient exposure surrogate and differs from the actual ambient O3 concentration outside a subject’s 

27 residence and/or worksite (in the absence of indoor O3 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). Ozone 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 O3 

concentration in 24 MSAs across the U.S. was examined in the 2006 O3 AQCD (U.S. EPA, 2006, 

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

7 90th percentile of the absolute difference in O3 concentrations, and CODs. 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 O3 formation and 

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

11 3.6.2.1). Liu et al. (1995, 039061) conducted a O3 exposure assessment study in Toronto, Canada 

12 during the winter and summer of 1992. Results indicated that outdoor O3 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 O3 concentration to be more distinct in 

17 the winter than in the summer. 

18 Sarnat et al. (2010, 385852) studied the spatial variability of O3, along with PM2.5, NO2, 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 O3 concentration were similar among all sites tested, including 

22 multiple urban sites and a rural site some 38 miles from the city center. This result was also observed 

23 for PM2.5, another regional pollutant. In contrast, the more spatially variable pollutants CO and NO2 

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 O3 epidemiologic studies. 

4.5.3.2. Se a s ona lity 

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

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 O3 

concentrations during the O3 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 personal33 ambient and indoor-outdoor O3 relationships (see Sections 4.3.3 and 4.3.4). More limited evidence is 

34 available regarding the specific effects of O3 detection limits and activity pattern changes on O3 

relationships. 

36 Several studies have found increased summertime correlations or ratios between personal 

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

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1 between indoor and outdoor O3 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 O3 concentrations during winter are generally much lower than summertime concentrations, 

6 possibly obscuring wintertime relationships due to detection limit issues. Studies specifically 

7 evaluating the effect of ventilation conditions on O3 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 suggests that studies conducted during the O3 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 

O3 and non-O3 seasons may have an intermediate level of exposure error. 

4.5.4. Expos ure to Co-pollutants and Ozone Reaction Products 

16 Although indoor O3 concentrations are usually well below ambient concentrations, the same 

17 reactions that consume O3 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 O3 concentrations, and could potentially contribute to 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 O3 measurements from central ambient monitoring 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 O3 concentration, ambient and non-ambient 

concentration differences, and varying activity 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) O3 concentrations are uniform across the 

28 region; (2) personal activity patterns are similar across the population; and (3) housing 

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

Since these factors vary by location 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, O3 concentrations 

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1 measured at central-site monitors are representative of day-to-day changes in average personal O3 

2 exposure, 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 fixed4 site monitors due to time spent indoors and the low indoor penetration of O3. 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 levels (Klepeis, 1999, 001697). In addition, 

14 resource-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 studies. 

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 O3 

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

25 enabling measurement of sub-daily O3 concentrations. A portable continuous O3 monitor based on a 

26 different principle, UV absorption, has recently become available. 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 O3 AQCD, along with a few new studies published since, 

30 describe the relationship between indoor O3 concentration and the O3 concentration immediately 

31 outside the indoor microenvironment. These studies show that the indoor concentration is often 

32 substantially lower than the outdoor concentration unless indoor sources are present. Low indoor O3 

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concentrations can be explained by reaction of O3 with surfaces and airborne constituents. However, 

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

air exchange rate, such as open windows, the indoor O3 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 O3 season and for homes with 

increased window ventilation. A correlation of 0.58 was reported between indoor and outdoor O3 

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 O3 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 O3 concentrations has been evaluated 

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

2006 O3 AQCD. 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 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-individual 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. Slopes 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 cleaners (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 California, 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 concentrations varies due to a number of 

factors, such as activity patterns, housing characteristics, and season, O3 concentrations measured at 

central-site monitors are representative of day-to-day changes in average personal O3 exposure, 

which is the important parameter 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 O3. 

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4.6.1.2. Co-Exposure to Ozone a nd Othe r Polluta nts 

1 Individuals may be exposed to other pollutants in conjunction with exposure to O3. Personal 

2 exposure to O3 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 PM2.5, a rank correlation of 0.14 was reported between 

daily O3 and PM2.5 exposures during spring and fall in Atlanta. Positive slopes were reported during 

6 summer 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 showing 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 O3- PM2.5 slopes. For EC and NO2, 

13 near-zero correlations were reported with O3 during spring and fall in Atlanta. These extremely low 

14 correlations for the traffic-related and spatially variable pollutants EC and NO2 contrast with the 

higher correlation observed for PM2.5, a regional pollutant. 

16 In near-road and on-road microenvironments, correlations between O3 and traffic-related 

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

18 for NO2 (-0.8 to -0.9). This is consistent with the chemistry of NO oxidation, in which O3 is 

19 consumed to form NO2. The more moderate negative correlations observed for PM2.5, PM1.0, and 

VOC may reflect reduced concentrations of O3 in more polluted environments due to other 

21 scavenging reactions. A similar process occurs indoors, where infiltrated O3 reacts with airborne or 

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

23 reactions decrease indoor O3 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 

Exposures estimates in urban areas may be improved 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 NO2. In a study that extended CALINE4 NOX modeling results to evaluate the impact on residential 

32 O3 concentrations (see Section 4.4.1), O3 concentrations were reduced by 0.51 ppb O3 per 1 ppb 

33 NOX. This intra-urban traffic-related variability in O3 concentrations 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 O3. 

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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 distributions of pollutant-related and individual-level variables, such as ambient and local O3 

concentration contributions and breathing rate respectively, to compute the distribution of individual 

6 exposures across the modeled population. The models also have the capability to estimate received 

7 dose through a dosimetry 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 indicating 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 high12 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 epidemiologic study results, 

18 although this may be less of an issue for O3 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 HRV parameters and either ambient concentrations or personal 

21 exposures of O3 and PM2.5, 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 O3 in a mixture of pollutants. Nonambient exposure is unlikely to influence 

health effect estimates because of the lack of indoor O3 sources and because indoor-generated O3 

26 exposures are unlikely to be correlated with ambient O3 exposure. Compared with directly emitted 

27 pollutants such as CO and NOX, O3 exhibits relatively low spatial variability across urban areas, as 

28 discussed in Chapter 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 comparing the effect of spatial 

31 variability on effect estimates for O3, PM2.5, NO2, and CO suggests that choice of monitor for more 

32 spatially homogenous pollutants such as O3 may have little impact on the results of epidemiologic 

33 studies. Season, however, may have a substantial effect due to much lower O3 concentrations during 

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

exposures observed during the summer. Studies conducted during the O3 season or in periods when 

36 communities 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 

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O3 and non-O3 seasons may have an intermediate level of exposure error. Exposure to mixtures of 

pollutants containing O3 also complicates interpretation of epidemiologic results. Moderate to strong 

negative correlations between O3 and traffic-related pollutants, particularly NO2, make it difficult to 

determine to what extent O3-based effect estimates quantitatively reflect the independent effect of O3 

itself, or the effect of another pollutant or pollutants in the mixture. Interpretation of O3 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 published exposure research indicate that O3 concentrations measured at central-site 

monitors are indicative of day-to-day changes in average personal O3 exposure, making ambient 

concentrations a useful parameter for epidemiologic studies. 

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