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4 EXPOSURE TO AMBIENT OZONE

4.1 Introduction

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

2 microenvironments, discussed methods for estimating personal and population exposure 

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

4 ambient concentrations, and discussed the implications of using ambient O3

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

6 new information regarding exposure to ambient O3 which builds upon the body of 

7 evidence presented in the 2006 O3 AQCD. A brief summary of findings from the 2006 O3

8 AQCD is presented at the beginning of each section as appropriate.

9 Section 4.2 presents general exposure concepts describing the relationship between 

10 ambient pollutant concentrations and personal exposure. Section 4.3 describes exposure 

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

12 outdoor concentrations of O3 and related pollutants. Section 4.4 presents material on 

13 parameters relevant to exposure estimation, including activity patterns, averting behavior, 

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

15 modeling local O3 concentrations, air exchange rates, microenvironmental concentrations, 

16 and personal and population exposure. Section 4.6 discusses the implications of using 

17 ambient O3 concentrations to estimate exposure in epidemiologic studies, including 

18 several factors that contribute to exposure error.

4.2 General Exposure Concepts

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

20 and the uncertainties that exist in this framework. An individual’s time-integrated total 

21 exposure to O3 can be described based on a compartmentalization of the person’s

22 activities throughout a given time period: 

Equation 4-1

23 where ET = total exposure over a time-period of interest, Cj = airborne O3 concentration at 

24 microenvironment j, and dt = portion of the time-period spent in microenvironment j.
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1 Equation 4-1 can be decomposed into a model that accounts for exposure to O3, of 

2 ambient (Ea) and nonambient (Ena) origin of the form: 

Equation 4-2

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

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

5 O3 generators and laser printers, these sources are specific to individuals and may not be 

6 important sources of population exposure. Ozone concentrations generated by ambient 

7 and nonambient sources are subject to spatial and temporal variability that can affect 

8 estimates of exposure and influence epidemiologic effect estimates. Exposure parameters 

9 affecting interpretation of epidemiologic studies are discussed in Section 4.5.

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

11 relevant to the NAAQS review. Assuming steady-state outdoor conditions, Ea can be 

12 expressed in terms of the fraction of time spent in various outdoor and indoor 

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

Equation 4-3

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

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

16 microenvironments, subscript o,i = index of outdoor microenvironments adjacent to a 

17 given indoor microenvironment i, and Finf,i = infiltration factor for indoor 

18 microenvironment i. Equation 4-3 is subject to the constraint Σfo + Σfi = 1 to reflect the 

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

20 equation has a summation because it reflects various microenvironmental exposures. 

21 Here, “indoors” refers to being inside any aspect of the built environment, e.g., home, 

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

23 facilities (movie theaters, restaurants, bars). “Outdoor” exposure can occur in parks or 

24 yards, on sidewalks, and on bicycles or motorcycles. Assuming steady state ventilation 

25 conditions, the infiltration factor is a function of the penetration (P) of O3 into the 

26 microenvironment, the air exchange rate (a) of the microenvironment, and the rate of O3

27 loss (k) in the microenvironment; Finf = Pa/(a + k).
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1 In epidemiologic studies, the central-site ambient concentration, Ca, is often used in lieu 

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

3 of data. Thus it is often assumed that Co = Ca and that the fraction of time spent outdoors 

4 can be expressed cumulatively as fo; the indoor terms still retain a summation because 

5 infiltration differs among different microenvironments. If an epidemiologic study 

6 employs only Ca, then the assumed model of an individual’s exposure to ambient O3, first 

7 given in Equation 4-3, is re-expressed solely as a function of Ca: 

Equation 4-4

8 The spatial variability of outdoor O3 concentrations due to meteorology, topography, 

9 varying precursor emissions and O3 formation rates; the design of the epidemiologic 

10 study; and other factors determine whether or not Equation 4-4 is a reasonable 

11 approximation for Equation 4-3. These equations also assume steady-state 

12 microenvironmental concentrations. Errors and uncertainties inherent in use of Equation 

13 4-4 in lieu of Equation 4-3 are described in Section 4.6 with respect to implications for 

14 interpreting epidemiologic studies. Epidemiologic studies often use concentration 

15 measured at a central site monitor to represent ambient concentration; thus α, the ratio 

16 between personal exposure to ambient O3 and the ambient concentration of O3, is defined 

17 as: 

Equation 4-5

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

Equation 4-6

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

20 microenvironment, the ambient component of a microenvironmental O3 concentration 

can be represented as the product of the ambient concentration and Finf 21 . Wallace et al. 

22 (2006) note that time-activity data and corresponding estimates of Finf for each 

23 microenvironmental exposure are needed to compute an individual’s α with accuracy. In 
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1 epidemiologic studies, α is assumed to be constant in lieu of time-activity data and 

estimates of Finf 2 , which can vary with building and meteorology-related air exchange 

3 characteristics. If important local outdoor sources and sinks exist that are not captured by 

4 central site monitors, then the ambient component of the local outdoor concentration may 

5 be estimated using dispersion models, land use regression models, receptor models, fine 

6 scale CTMs or some combination of these techniques. These techniques are described in 

7 Section 4.5.

4.3 Exposure Measurement

8 This section describes techniques that have been used to measure microenvironmental 

9 concentrations of O3 and personal O3 exposures as well as results of studies using those 

10 techniques. Previous studies from the 2006 O3 AQCD are described along with newer 

11 studies that evaluate indoor-outdoor concentration relationships, associations between 

12 personal exposure and ambient monitor concentration, and multipollutant exposure to 

13 other pollutants in conjunction with O3. Tables are provided to summarize important 

14 study results.

4.3.1 Personal Monitoring Techniques

15 As described in the 2006 O3 AQCD, passive samplers have been developed and deployed 

16 to measure personal exposure to O3 (Grosjean and Hisham, 1992; Kanno and 

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

18 nitrite, which is converted to nitrate by O3 and then quantified by a technique such as ion 

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

20 Ogawa, Inc., Japan (Ogawa & Co, 2007). The cumulative sampling and the detection 

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

22 hours or greater, which limits their ability to measure short-term daily fluctuations in 

23 personal O3 exposure. Longer sampling periods give lower detection limits; use of the 

24 badges for a 6-day sampling period yields a detection limit of 1 ppb, while a 24-hour 

25 sampling period gives a detection limit of approximately 5-10 ppb. This can result in a 

26 substantial fraction of daily samples being below the detection limit (Sarnat et al., 2006a; 

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

28 Development of improved passive samplers capable of shorter-duration monitoring with 

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

30 multiple microenvironments with relatively low participant burden.
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1 The nitrite-nitrate conversion reaction has also been used as the basis for an active 

2 sampler consisting of a nitrite-coated glass tube through which air is drawn by a pump 

3 operating at 65 mL/min (Geyh et al., 1999; Geyh et al., 1997). The reported detection 

4 limit is 10 ppb-h, enabling the quantification of O3 concentrations measured over a few 

5 hours rather than a full day (Geyh et al., 1999). 

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

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

8 includes a Nafion tube in the inlet line to equilibrate humidity, reducing the effect of 

9 humidity changes in different microenvironments (Wilson and Birks, 2006). Its size and 

10 weight (approximately 10×20×30 cm; 2 kg) make it suitable for use in a backpack 

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

12 stationary monitors to measure O3 in several national parks (Chapter 3). Future 

13 improvements and continued miniaturization of real-time O3 monitors can yield highly 

14 time-resolved personal measurements to further evaluate O3 exposures in specific 

15 situations, such as near roadways or while in transit.

4.3.2 Indoor-Outdoor Concentration Relationships

16 Several studies summarized in the 2006 O3 AQCD, along with some newer studies, have 

17 evaluated the relationship between indoor O3 concentration and the O3 concentration 

18 immediately outside the indoor microenvironment. These studies show that the indoor 

19 concentration is often substantially lower than the outdoor concentration unless indoor 

20 sources are present. Low indoor O3 concentrations can be explained by reactions of O3

21 with surfaces and airborne constituents. Studies have shown that O3 is deposited onto 

22 indoor surfaces where reactions produce secondary pollutants such as formaldehyde 

23 (Reiss et al., 1995b; Reiss et al., 1995a). However, the indoor-outdoor relationship is 

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

25 as open windows, the indoor O3 concentration may approach the outdoor concentration. 

26 Thus, in rooms with open windows, the indoor-outdoor (I/O) ratio may approach 1.0. 

27 Table 4-1 summarizes I/O ratios and correlations reported by older and more recent 

28 studies, with discussion of individual studies in the subsequent text. In general, I/O ratios 

29 range from about 0.1 to 0.4, with some evidence for higher ratios during the O3 season 

30 when concentrations are higher.

31 O3 concentrations near and below the monitor detection limit cause uncertainty in I/O 

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

33 resulting ratios. This problem is particularly acute in the non-ozone season when ambient 

34 O3 concentrations are low. Further improvements in characterization of 
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1 microenvironmental O3 concentrations and I/O ratios will rely on improved monitoring. 

2 Until new monitoring techniques are available and can be used in the field, past studies 

3 summarized in the 2006 O3 AQCD remain relevant to consider along with more recent 

4 studies in evaluating the relationship between indoor and outdoor O3 concentrations.

Table 4-1 Relationships between indoor and outdoor ozone concentration

Study Location Years/Season Population Sample 

duration

Ratioa Correlation Micro-environment Comment

Geyh 

et al. 

(2000)

Upland, 

Southern 

California

June -

September 

1995 and May 

1996

Children 6 days 0.24 NR Home Airconditioned

Ratio: Indoor 

mean/outdoor 

mean October 1995-

April 1996

0.15

Mountain 

Communities, 

Southern 

California

June -

September 

1995 and May 

1996

0.36 Opening 

windows

Ratio: indoor 

mean/outdoor 

mean October 1995-

April 1996

0.08

Avol et 

al. 

(1998a)

Southern 

California

FebruaryDecember, 

1994

NR 24 h 0.37

SD: 

0.25

0.58 Home Ratio: each 

pair of 

measurements

Summer 0.43

SD: 

0.29

NR

Non-summer 0.32

SD: 

0.21

NR

Romieu 

et al. 

(1998a)

Mexico City, 

Mexico

September 

1993 - July 

1994

Children 7 or 14 

days

0.20

SD: 

0.18

0.15b

Range: 

0.01-

1.00

NR Home Ratio: each 

pair of 

measurements

Lee et 

al. 

(2004a)

Nashville, TN Summer 1994 Children 1 week 0.1

SD: 

0.18

NR Home Ratio: Indoor 

mean/outdoor 

mean
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Study Location Years/Season Population Sample 

duration

Ratioa Correlation Micro-environment Comment

Héroux 

et al. 

(2010)

Regina, 

Saskatchewa, 

Canada

Summer 2007 All age 

groups

5 days 0.13 NR Home Ratio: Indoor 

mean/outdoor 

mean

Liu et al. 

(1995)

Toronto, 

Canada

Winter, 1992 All age 

groups

1 week 0.07

SD: 

0.10

NR Home Ratio: each 

pair of 

measurements

Summer, 1992 0.40

SD: 

0.29

Summer, 1992 12 h 0.30

SD: 

0.32

Daytime

Ratio: each 

pair of 

measurements

Summer, 1992 0.43

SD: 

0.54

Nighttime

Ratio: each 

pair of 

measurements

Romieu 

et al. 

(1998a)

Mexico City, 

Mexico

September 

1993 - July 

1994

Children 24 h/day, 

14 days

0.15 NR School Ratio: each 

pair of 

measurements

Children 

(during 

school 

hours)

5 h/day, 

5 days, 

10 days

0.30-

0.40

Immediately 

outside the 

schools

Blondeau 

et al. 

(2005)

La Rochelle, 

France

Spring, 2000 Children 2 weeks Range: 

0.00-

0.45

NR School No air 

conditioning

Ratio: Indoor 

mean/outdoor 

mean

LópezAparicio 

et al. 

(2011)

Prague, 

Czech 

Republic

July 2009 All age 

groups

1 month 0.10 NR Historic

Library

No heating or 

air 

conditioning

Ratio: Indoor 

mean/outdoor 

mean

Dec 2009 0.30

Riediker 

et al. 

(2003)

North 

Carolina

August -

October 2001

Adults 9 h 0.51

pvalue: 

0.000

NR Vehicle Ratio: Indoor 

mean/outdoor 

mean

a

Mean value unless otherwise indicated

b

Median

NR = not reported 

SD = standard deviation.

1 Geyh et al. (2000) measured 6-day indoor and outdoor concentrations at 116 homes in 

2 southern California, approximately equally divided between the community of Upland 

3 and several mountain communities. The extended sampling period resulted in a relatively 

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

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

6 windows. During the O3 season, the indoor O3 concentration averaged over all homes was 
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1 approximately 24% of the overall mean outdoor concentration in Upland (11.8 versus 

2 48.2 ppb), while in the mountain communities, the indoor concentration was 36% of the 

3 outdoor concentration (21.4 versus 60.1 ppb). This is consistent with the increased air 

4 exchange rate expected in homes using window ventilation. In the non-ozone season, 

5 when homes are likely to be more tightly closed to conserve heat, the ratios of indoor to 

6 outdoor concentration were 0.15 (3.2 versus 21.1 ppb) and 0.08 (2.8 versus 35.7 ppb) in 

7 Upland and the mountain communities, respectively. Avol et al. (1998a) observed a mean 

8 I/O ratio of 0.37 for 239 matched 24-h samples collected between February and 

9 December at homes in the Los Angeles area. The I/O ratio during summer was somewhat 

10 higher than the non-summer I/O ratio (0.43 versus 0.32). The authors also reported a 

11 correlation of 0.58 between the 24-h avg indoor concentration and the outdoor 

12 concentration, which was only slightly higher than the correlation between the indoor 

13 concentration and the concentration at the neighborhood fixed-site monitor (0.49). 

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

15 1995), which found summer I/O ratios of 0.30-0.43, in comparison with a winter I/O ratio 

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

17 Mexico City for 7- or 14-day cumulative samples, with the highest ratios observed in 

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

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

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

21 2004a).

22 Investigators have also measured I/O ratios for non-residential microenvironments, 

23 including schools and vehicles. Romieu et al. (1998a) reported that O3 concentrations 

24 measured during school hours (10-day cumulative sample, 5 h/day) were 30-40% of 

25 concentrations immediately outside the schools, while overall I/O ratios (14-day 

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

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

28 windows. Air exchange was also identified as an important factor in the I/O ratios 

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

30 based on simultaneous continuous measurements ranged from 0-0.45, increasing with 

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

32 with no heating or air conditioning (i.e., natural ventilation) was observed to have ratios 

33 of one-month indoor and outdoor concentrations ranging from 0.10-0.30 during a nine34 month sampling campaign, with the highest ratios reported in Nov-Dec 2009 and the 

35 lowest ratios during Jul-Aug 2009 (López-Aparicio et al., 2011). Indoor concentrations 

were relatively constant (approximately 3-7 μg/m3 36 or 2-3 ppb), while outdoor 

concentrations were lower in the winter (9-10 μg/ m3 37 or about 5 ppb) than in the summer 

(35-45 μg/ m3 38 or about 20 ppb). This seasonal variation in outdoor concentrations 

39 coupled with homogeneous indoor concentrations, together with increased wintertime air 
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1 exchange rate due to higher indoor-outdoor temperature differences, is likely responsible 

2 for the observed seasonal pattern in I/O ratios. 

3 Exposures in near-road, on-road and in-vehicle microenvironments are likely to be more 

4 variable and lower in magnitude than those in other microenvironments due to reaction of 

5 O3 with NO and other combustion emissions. Depending on wind direction, O3

6 concentrations near the roadway have been found to be 20-80% of ambient 

7 concentrations at sites 400 meters or more distant from roads (Section 3.6.2.1). A study 

8 on patrol cars during trooper work shifts reported in-vehicle 9-h concentrations that were 

9 approximately 51% of simultaneously measured roadside concentrations (mean of 11.7 

10 versus 22.4 ppb) (Riediker et al., 2003).

4.3.3 Personal-Ambient Concentration Relationships

11 Several factors influence the relationship between personal O3 exposure and ambient 

12 concentration. Due to the lack of indoor O3 sources, along with reduction of ambient O3

13 that penetrates into enclosed microenvironments, indoor and in-vehicle O3 concentrations 

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

15 microenvironments. Ambient O3 varies spatially due to reactions with other atmospheric 

16 species, especially near busy roadways where O3 concentrations are decreased by 

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

18 NOX, which show appreciably higher concentrations near the roadway than several 

19 hundred meters away (Karner et al., 2010). O3 also varies temporally over multiple 

20 scales, with generally increasing concentrations during the daytime hours, and higher O3

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

22 Figure 4-1, taken from a personal exposure study conducted by Chang et al. (2000).

23 In this figure, hourly personal exposures are seen to vary from a few ppb in some indoor 

24 microenvironments to tens of ppb in vehicle and outdoor microenvironments. The 

25 increase in ambient O3 concentration during the day is apparent from the outdoor 

26 monitoring data. In comparison, ambient PM2.5 exhibits less temporal variability over the 

27 day than O3, although personal exposure to PM2.5 also varies by microenvironment. This 

28 combined spatial and temporal variability for O3 results in varying relationships between 

29 personal exposure and ambient concentration.

30 Correlations between personal exposure to O3 and corresponding ambient concentrations, 

31 summarized in Table 4-2, exhibit a wide range (generally 0.3-0.8, although both higher 

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

33 outdoor microenvironments, high building ventilation conditions, and during the summer 

34 season. Low O3 concentrations indoors and during the winter lead to a high proportion of 
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1 personal exposures below the sampler detection limit, which may partially explain the 

2 low correlations observed in some studies under those conditions. Studies report varying 

3 correlations over a range of averaging times, with no clear trend. Ratios of personal 

4 exposure to ambient concentration, summarized in Table 4-3, are generally lower in 

5 magnitude (typically 0.1-0.3), and are also variable, with increasing time spent outdoors 

6 associated with higher ratios. The next two subsections describe studies that have 

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

8 populations.

Note: the notation below each clock hour shows the location or activity during that hour.

Source: Reprinted with permission of Air and Waste Management Association (Chang et al., 2000).

Figure 4-1 Variation in hourly personal and ambient concentrations of ozone

and PM2.5 in various microenvironments during daytime hours.
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1 Ozone concentrations near and below the passive sampler detection limit lead to 

2 uncertainty in personal-ambient correlations and ratios. Correlations are reduced in 

3 magnitude by values below the detection limit because noise obscures the underlying 

4 signal in the data, while ratios tend to fluctuate widely at low concentration since small 

5 changes in measured values cause large relative changes in resulting ratios. As with I/O 

6 ratios, this problem is particularly acute in the non-ozone season when ambient O3

7 concentrations are low. Improved characterization of the relationship between personal 

8 exposure and ambient concentration will depend on the use of recent improved 

9 monitoring techniques to accurately capture low O3 concentrations, preferably at high 

10 time resolution to facilitate evaluation of the effect of activity pattern on exposure 

11 (Section 4.3.1). While data from studies using new monitoring techniques become 

12 available, past studies summarized in the 2006 O3 AQCD remain relevant to consider 

13 along with more recent studies in evaluating personal-ambient concentration 

14 relationships.

15 Personal-Ambient Correlations. Correlations between personal exposure and 

16 ambient O3 concentrations have been evaluated in several research studies, many of 

17 which were conducted prior to 2005 and are discussed in the 2006 O3 AQCD. Some 

18 studies evaluated subject-specific, or longitudinal correlations, which describe multiple 

19 daily measurements for a single individual. These studies indicate the inter-individual 

20 variability of personal-ambient correlations. Another type of correlation is a pooled 

21 correlation, which combines data from multiple individuals over multiple monitoring 

22 periods (e.g., days), providing an overall indicator of the personal-ambient relationship 

23 for all study subjects. A third type of correlation is a community-average correlation, 

24 which correlates average exposure across all study subjects with fixed-site monitor 

25 concentrations. Community-average correlations are particularly informative for 

26 interpreting time-series epidemiologic studies, in which ambient concentrations are used 

27 as a surrogate for community-average exposure. However, few studies report this metric; 

28 this represents another opportunity for improvement of future personal exposure studies. 

29 Table 4-2 summarizes studies reporting personal-ambient correlations, and the studies in 

30 the table are discussed in the subsequent text. 

31 The results of these studies generally indicate that personal exposures are moderately 

32 well correlated with ambient concentrations, and that the ratio of personal exposure to 

33 ambient concentration is higher in outdoor microenvironments and during the summer 

34 season. In some situations, a low correlation was observed, and this may be due in part to 

35 a high proportion of personal measurements below the detection limit of the personal 

36 sampler. The effect of season is unclear, with mixed evidence on whether higher 

37 correlations are observed during the O3 season. Chang et al. (2000) measured hourly 

38 personal exposures in multiple microenvironments and found that the pooled correlation 
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1 between personal exposure and ambient concentration was highest for outdoor 

2 microenvironments (r = 0.68-0.91). In-vehicle microenvironments showed moderate to 

3 high correlations (0.57-0.72). Correlations in residential indoor microenvironments were 

4 very low (r = 0.05-0.09), with moderate correlations (0.34-0.46) in other indoor 

5 microenvironments such as restaurants and shopping malls. Liard et al. (1999) evaluated 

6 community-average correlations based on 4-day mean personal O3 exposure 

7 measurements for adults and children and found a relatively high correlation (r = 0.83) 

8 with ambient concentrations, even though 31-82% of the personal measurements were 

9 below the detection limit. Sarnat et al. (2000) studied a population of older adults in 

10 Baltimore and found that longitudinal correlations between 24-h personal exposure and 

11 ambient concentration varied by subject and season, with somewhat higher correlations 

12 observed in this study during summer (mean = 0.20) than in winter (mean = 0.06). Some 

13 evidence was presented that subjects living in well-ventilated indoor environments have 

14 higher correlations than those living in poorly ventilated indoor environments, although 

15 exceptions to this were also observed. Ramírez-Aguilar et al. (2008) measured 48- to 

16 72-h personal exposures of four groups of asthmatic children aged 6-14 in Mexico City 

17 during 1998-2000. A moderate pooled correlation (r = 0.35) was observed between these 

18 exposures and corresponding ambient concentrations.
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Table 4-2 Correlations between personal and ambient ozone concentration.

Study Location Years/Season Population Sample 

duration

Correlation Study Type Comment

Chang et al. 

(2000)

Baltimore, 

MD

Summer 1998 Older adults 1 h 0.91 Pooled Outdoor 

near Winter 1999 0.77 roadway

Summer 1998 0.68 Outdoor 

away from 

Winter 1999 0.86 road

Summer 1998 0.72 In vehicle

Winter 1999 0.57

Summer 1998 0.09 Indoorsresidence

Winter 1999 0.05

Summer 1998 0.34 Indoorsother

Winter 1999 0.46

Liard et al. 

(1999)

Paris, France Summer 1996 All age 

groups

4 day 0.83 Communityaveraged

Sarnat et al. 

(2000)

Baltimore, 

MD

Summer Older adults 24 h 0.20

SD: 0.28

95% CI: 0.06, 0.34

Longitudinal

Winter 0.06

SD: 0.34

95% CI: -0.88, 0.24

Linn et al. 

(1996)

Southern 

California

All seasons from 

1992 to 1993

Children 24 h 0.61 Communityaveraged

Brauer and 

Brook 

(1997)

Vancouver, 

Canada

Summers 1992 

and 1993

Health clinic 

workers

24 h 0.60 Pooled 0-25% of 

time 

outdoors

Camp 

counselors

24 h 0.42 Pooled 7.5-45% of

time 

outdoors

Farm 

workers

24 h 0.64 Pooled 100% of 

time 

outdoors

RamírezAguilar et 

al. (2008)

Mexico City, 

Mexico

December 1998-

April 2000

Asthmatic 

children

48 h to 72 

h

0.35 Pooled

Delfino et 

al. (1996).

San Diego, 

California

September and 

October 1993

Asthmatic 

children

12-h 0.45

Range: 0.35-0.69

Pooled

NR = not reported

1 Consistent with hourly microenvironment-specific results from the Chang et al. (2000)

2 study described above, studies have found moderate to high personal-ambient 

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

4 0.61 was reported between 24-h avg personal and central-site measurements by Linn et 

5 al. (1996) for a population of southern California schoolchildren who spent an average of 
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1 101-136 minutes per day outdoors. The authors also report a correlation of 0.70 between 

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

3 the average school outdoor concentration (34 ppb) was higher than the average central4 site concentration (23 ppb) and the average personal exposure concentration was lower 

5 (5 ppb) than the central-site value, the similarity between the correlations in this study 

6 indicate that central-site monitor concentrations can represent personal exposures in 

7 addition to representing local outdoor concentrations. A study in Vancouver, BC 

8 provided another illustration of the effect of outdoor microenvironments on personal9 ambient relationships by comparing three groups spending different amounts of time 

10 outdoors: health clinic workers (0-25% of sampling period outdoors), camp counselors 

11 (7.5-45% of sampling period outdoors), and farm workers (100% of sampling period 

12 outdoors) (Brauer and Brook, 1997). Health clinic workers and camp counselors were 

13 monitored 24 h/day, while farm workers were monitored during their work shift 

14 (6-14 hours). In this study, the pooled correlations between personal exposure and fixed15 site concentration were not substantially different among the groups (r = 0.60, 0.42, and 

16 0.64, respectively). The ratios of personal exposure to fixed-site monitor concentration 

17 increased among the groups with increasing amount of time spent outdoors (0.35, 0.53, 

18 and 0.96, respectively). This indicates that temporal variations in personal exposure to O3

19 are driven by variations in ambient concentration, even for individuals that spend little 

20 time outdoors.

21 Personal-Ambient Ratios. Studies indicate that the ratio between personal O3

22 exposure and ambient concentration varies widely, depending on activity patterns, 

23 housing characteristics, and season. Higher personal-ambient ratios are generally 

24 observed with increasing time spent outside, higher air exchange rate, and in seasons 

25 other than winter. Table 4-3 summarizes the results of several such studies discussed in 

26 the 2006 O3 AQCD together with newer studies showing the same pattern of results.

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

28 Mexico City and presented a regression model indicating a 0.56 ppb increase in 6-h 

29 personal exposure for each 1 ppb increase in ambient concentration. Regression analyses 

30 by (2005; 2001) for 24-h data from mixed populations of children and older adults in 

31 Baltimore (Sarnat et al., 2001) and Boston (Sarnat et al., 2005) found differing results 

32 between the two cities, with Baltimore subjects showing a near-zero slope (0.01) during 

33 the summertime while Boston subjects showed a positive slope of 0.27 ppb personal 

34 exposure per 1 ppb ambient concentration. In both cities, the winter slope was near zero. 

35 The low slope observed in Baltimore may have been due to differences in time spent 

36 outdoors, residential ventilation conditions, or other factors. Xue et al. (2005) measured 

37 6-day personal exposure of children in southern California and found that the average 

38 ratio of personal exposure to ambient concentration was relatively stable throughout the 
Draft – Do Not Cite or Quote 4-15 June 2012

1 year at 0.3. These authors also regressed personal exposures on ambient concentration 

2 after adjusting for time-activity patterns and housing characteristics and found a slope of 

0.54 ppb/ppb, with the regression R2 3 value of 0.58. Unadjusted regression slopes were 

4 not presented. It should also be noted that the ratio and slope would not be expected to be 

5 identified unless the intercept and other regression parameters were effectively zero.

Table 4-3 Ratios of personal to ambient ozone concentration.

Study Location Years/Season Population Sample 

duration

Ratioa Slope InterceptStudy 

Type

Comment

Sarnat et al. 

(2001)

Baltimore Summer 1998 Older 

adults and 

children 

24 h NR 0.01 1.84 Longitudinal t-value: 1.21

Winter 1999 Older 

adults, 

children, 

and 

individuals 

with COPD

NR 0.00 0.46 t-value: 0.03

Brauer and 

Brook (1997)

Vancouver, 

Canada

Summers 

1992 and 

1993

Health 

clinic 

workers

24 h 0.35 NR NR Pooled 0-25% 

of time 

outdoors

Camp 

counselors

0.53 NR NR Pooled 7.5-45% 

of time 

outdoors

Farm 

workers

0.96 NR NR Pooled 100% of 

time 

outdoors

O'Neill et al. 

(2003)

Mexico 

City, 

Mexico

April - July 

1996

Shoe 

cleaners

6 h 0.40

0.37b

SD: 

0.22

0.56

95% CI: 

0.43-0.69

NR Longitudinal

Sarnat et al. 

(2005)

Boston Summer Older 

adults and 

children

24 h NR 0.27

95% CI: 

0.18-0.37

NR Longitudinal

Winter NR 0.04

95% CI: 

0.00-0.07

NR
Draft – Do Not Cite or Quote 4-16 June 2012

Study Location Years/Season Population Sample 

duration

Ratioa Slope InterceptStudy 

Type

Comment

Xue et al. 

(2005)

Southern 

California

June 1995 -

May 1996

Children 6 day 0.3

SD: 

0.13

NR NR Longitudinal

Sarnat et al. 

(2006a)

Steubenville, 

OH

Summer Older 

adults

24 h NR 0.15

SE: 0.02

t-value: 

7.21

R2

: 0.24

NR Longitudinal All 

individuals

NR 0.18

SE: 0.03

t-value: 

7.34

R2

: 0.27

NR Highventilation

NR 0.08

SE: 0.04

t-value: 

1.89

R2

: 0.19

NR Lowventilation

Fall NR 0.27

SE: 0.03

t-value: 

8.64

R2

: 0.25

NR All 

individuals

NR 0.27

SE: 0.04

t-value: 

7.38

R2

: 0.33

NR Highventilation

NR 0.20

SE: 0.05

t-value: 

3.90

R2

: 0.12

NR Lowventilation

RamírezAguilar et al.

(2008)

Mexico City, 

Mexico

Dec.1998-

Apr. 2000

Asthmatic 

children

48 h to

72 h

0.23 0.17

SE: 0.02

95% CI : 

0.13-0.21

p-value: 

0.00

Pooled

a Mean value unless otherwise indicated

b Median

NR = not reported

SD = standard deviation

1 A few additional studies have been published since the 2006 O3 AQCD comparing 

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

3 conclusions of the 2006 O3 AQCD that ventilation conditions, activity pattern, and season 

4 may impact personal-ambient ratios. Sarnat et al. (2006a) measured 24-h personal 

5 exposures for a panel of older adults in Steubenville, OH during summer and fall 2000. 
Draft – Do Not Cite or Quote 4-17 June 2012

1 Subjects were classified as high-ventilation or low-ventilation based on whether they 

2 spent time in indoor environments with open windows. Regression of personal exposures 

3 on ambient concentration found a higher slope for high-ventilation subjects compared

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

5 0.20). Suh and Zanobetti (2010) reported an average 24-h personal exposure of 2.5 ppb as 

6 compared to 24-h ambient concentration of 29 ppb for a group of individuals with either 

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

8 the mean 24-h personal exposure across 137 participants in summer and winter was 

9 2.1 ppb, while the mean ambient concentration on sampling days was 25 ppb (Williams 

10 et al., 2009b). Although no personal exposures were measured, Mcconnell et al. (2006)

11 found that average 24-h home outdoor O3 concentrations were within 6 ppb of O3

12 concentrations measured at central-site monitors in each of three southern California 

13 communities, with a combined average home outdoor concentration of 33 ppb compared 

14 to the central-site average of 36 ppb. In Mexico City, Ramírez-Aguilar et al. (2008)

15 regressed 48- to 72-h personal exposures of four groups of asthmatic children aged 6-14 

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

17 distance to the fixed-site monitor, time spent outdoors, an interaction term combining 

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

4.3.4 Co-exposure to Other Pollutants and Environmental Stressors

19 Exposure to ambient O3 occurs in conjunction with exposure to a complex mixture of 

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

21 important consideration in evaluating health effects of O3 since these other pollutants 

22 have either known or potential health effects that may impact health outcomes due to O3. 

23 The co-occurrence of high O3 concentrations with high heat and humidity may also 

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

25 personal O3 exposure and exposure to other ambient pollutants, as well as co-exposure 

26 relationships for near-road O3 exposure.

4.3.4.1 Personal Exposure to Ozone and Copollutants

27 Personal exposure to O3 shows variable correlation with personal exposure to other 

28 pollutants, with differences in correlation depending on factors such as instrument 

29 detection limit, season, city-specific characteristics, time scale, and spatial variability of 

30 the copollutant. Suh and Zanobetti (2010) reported Spearman rank correlation 

31 coefficients during spring and fall between 24-h avg O3 measurements and co-pollutants 

32 of 0.14, 0.00, and -0.03 for PM2.5, EC, and NO2, respectively. Titration of O3 near 
Draft – Do Not Cite or Quote 4-18 June 2012

1 roadways is likely to contribute to the low or slightly negative correlations with the

2 traffic-related pollutants EC and NO2. The somewhat higher correlation with PM2.5 may 

3 reflect the influence of air exchange rate and time spent outdoors on co-exposures to 

4 ambient PM2.5 and O3. Overall, the copollutant correlations are quite small, which may be 

5 due to the very low personal exposures observed in this study (2-3 ppb), likely to be near 

6 or below the detection limit of the passive sampler over a 24-h period. Chang et al. 

7 (2000) measured hourly personal exposures to PM2.5 and O3 in summer and winter in 

8 Baltimore, Maryland. Correlations between PM2.5 and O3 were 0.05 and -0.28 in summer 

9 and winter, respectively. Results indicate personal O3 exposures were not significantly 

10 associated with personal PM2.5 exposures in either summer or winter. These non11 significant correlations may be attributed in part to the relatively low personal O3

12 exposures observed in this study; in both summer and winter, the mean personal O3

13 exposure was below the calculated limit of detection.

14 Studies conducted in Baltimore (Sarnat et al., 2001) and Boston (Sarnat et al., 2005)

15 found differing results for the correlation between 24-h avg personal O3 and personal 

16 PM2.5 exposures, particularly during the winter season. Sarnat et al. (2001) found a 

17 positive slope when regressing personal exposures of both total PM2.5 (0.21) and PM2.5 of 

18 ambient origin (0.22) against personal O3 exposures during the summer season, but 

19 negative slopes (-0.05 and -0.18, respectively) during the winter season. The summertime 

20 slope for personal PM2.5 exposure versus personal O3 exposure was much higher for 

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

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

23 between 24-h avg personal O3 and personal PM2.5 in Boston (0.72) (Sarnat et al., 2005). 

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

25 both cities during both seasons, there was a wide range of subject-specific correlations 

26 between personal O3 and personal PM2.5 exposures, with some subjects showing 

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

28 correlations (<-0.50). The median correlation in both cities was slightly positive in the 

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

30 results indicate the potential effects of city-specific characteristics, such as housing stock 

31 and building ventilation patterns, on relationships between O3 and copollutants.

32 The lack of long-term exposure studies limits evaluation of long-term correlations 

33 between O3 exposure and copollutant exposure. However, some insight may be provided 

34 by an analysis of correlations between O3 and other criteria pollutants, such as is 

35 provided in Section 3.6.4. Warm-season 8-h daily max O3 concentrations are generally 

36 positively correlated with co-located 24-h avg measurements of other criteria pollutants

37 (Figure 3-57). Median correlations range from approximately 0.15 to 0.55 for CO, SO2, 

38 NO2, PM10, and PM2.5, in that order. In contrast, year-round 8-h daily max O3 data show 
Draft – Do Not Cite or Quote 4-19 June 2012

1 negative median correlations with CO and NO2, positive correlations with PM10 and 

2 PM2.5, and essentially zero correlation with SO2. This reflects mostly negative 

3 correlations between O3 and all pollutants during wintertime, as shown in Figure 3-56. 

4 Titration of O3 near roadways also likely contributes to overall negative correlations with 

5 NO2 and CO. Positive correlations between O3 and PM2.5 during the summertime can be 

6 partly explained by meteorological conditions favoring increased formation of both 

7 secondary PM and O3. Strong positive correlations can influence the interpretation of 

8 epidemiologic results, potentially complicating the ability to identify the independent 

9 effect of a pollutant. 

4.3.4.2 Near-Road Exposure to Ozone and Copollutants

10 Beckerman et al. (2008) measured both 1-week and continuous concentrations of O3, NO, 

11 NO2, NOX, PM2.5, PM1.0, and several VOCs (the BTEX compounds, MTBE, hexane, and 

12 THC) in the vicinity of heavily traveled (annual average daily traffic [AADT] >340,000) 

13 roadways in Toronto, Canada. Passive samplers were deployed for one week in August 

14 2004. Ozone concentrations were negatively correlated with all pollutants, with the 

15 exception of VOCs at one of the monitoring sites which were suspected of being 

16 influenced by small area sources. Site specific correlations are given in Figure 4-2. 

17 Correlations were -0.77 to -0.85 for NO2, -0.48 to -0.62 for NO, and -0.55 to -0.63 for 

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

19 PM2.5 and PM1.0 correlations were -0.35 to -0.78 and -0.34 to -0.58, respectively. At the 

20 monitoring site not influenced by small area sources, O3-VOC correlations ranged from -

21 0.41 to -0.66. 

22 Beckerman et al. (2008) also made on-road measurements of multiple pollutants with a 

23 instrumented vehicle. Concentrations were not reported, but correlations between O3 and 

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

25 than the near-road correlations. SO2, CO, and BC were measured in the mobile 

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

27 O3 when the data were controlled for site. Correlations for continuous concentrations 

28 between O3 and co-pollutants were somewhat lower than the 1-week correlations, except 

29 for O3-PM2.5 correlations. Correlations were -0.90, -0.66, -0.77, and -0.89 for NO2, NO, 

30 NOX, and PM1.0 respectively. The continuous O3-PM2.5 correlation was -0.62, which is in 

31 the range of the 1-week correlation. 
Draft – Do Not Cite or Quote 4-20 June 2012

Source: Data from: Beckerman et al. (2008)

Figure 4-2 Correlations between 1-week concentrations of ozone and 

copollutants measured near roadways.

4.3.4.3 Indoor Exposure to Ozone and Copollutants

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

2 form oxidized products, as described in Section 3.2.3 as well as the 2006 O3 AQCD. It is 

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

4 was identified regarding personal exposures. The reactions are similar to those occurring 

5 in the ambient air, as summarized in Chapter 3. For example, O3 can react with terpenes 

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

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

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

9 et al., 1995b). Ozone has also been shown to react with material trapped on HVAC filters 

10 and generate airborne products (Bekö et al., 2007; Hyttinen et al., 2006). Potential 

11 oxygenated reaction products have been found to act as irritants (Anderson et al., 2007), 

12 indicating that these reaction products may have health effects separate from those of O3

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

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

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

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

17 are dependent on indoor O3 concentration, temperature, and air exchange rate, making 

18 estimation of exposures to reaction products difficult.

Copollutant

NO2

NO

NOX

PM2.5

PM1.0

VOC

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

Pearson Correlation Coefficient
Draft – Do Not Cite or Quote 4-21 June 2012

4.4 Exposure-Related Metrics

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

2 but are not themselves direct measures of exposure. Time-location-activity patterns, 

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

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

5 exposure is represented by measurements at the monitors, although factors other than 

6 distance play an important role as well. 

4.4.1 Activity Patterns

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

8 Variation in O3 concentrations among various microenvironments means that the amount 

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

10 individual’s exposure to ambient O3. The effect of activity pattern on exposure is 

11 explicitly accounted for in Equation 4-3 by the fraction of time spent in different 

12 microenvironments.

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

14 variations in exposure across a population and over time. Large-scale human activity 

15 databases, such as those developed for the National Human Activity Pattern Survey 

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

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

18 results, have been designed to characterize exposure patterns among much larger 

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

20 human activity patterns across the population (all ages) are illustrated in Figure 4-3

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

22 among the entire population as well as the proportion of time spent in each 

23 microenvironment. For example, about 25% of the individuals reported being outdoors or 

24 in a vehicle between 2:00 and 3:00 p.m., when daily O3 levels are peaking, although 

25 about half of this time was spent in or near a vehicle, where O3 concentrations are likely 

26 to be lower than ambient concentrations. 

27 Time spent in different locations has also been found to vary by age. Table 4-4

28 summarizes NHAPS data reported for four age groups, termed Very Young (0-4 years), 

29 School Age (5-17 years), Working (18-64 years), and Retired (65+ years) (Klepeis et al., 

30 1996). The working population spent the least time outdoors, while the school age 

31 population spent the most time outdoors. NHAPS respondents aged 65 and over spent 

32 somewhat more time outdoors than adults aged 18-64, with a greater fraction of time 
Draft – Do Not Cite or Quote 4-22 June 2012

1 spent outdoors at a residence. Children aged 0-4 also spent most of their outdoor time in a 

2 residential outdoor location. On average, the fraction of time spent outdoors by school 

3 age respondents was 2.62 percentage points higher than working respondents, 

4 corresponding to approximately 38 minutes more time outdoors per day. Although not 

5 accounting for activity level, this increased time spent outdoors is consistent with 

6 evidence in Chapter 8 suggesting that younger and older age groups are more at risk for 

7 O3-related health effects.

Table 4-4 Mean fraction of time spent in outdoor locations by various age 

groups in the NHAPS study

Age Group Residential-Outdoor Other Outdoor Total Outdoors

0-4 yr 5.38% 0.96% 6.34%

5-17 yr 5.05% 2.83% 7.88%

18-64 yr 2.93% 2.33% 5.26%

65+ yr 4.48% 1.27% 5.75%

Source: Data from Klepeis et al. (1996).

8 Together with location, exertion level is an important determinant of exposure. Table 4-5

9 summarizes ventilation rates for different age groups at several levels of activity as 

10 presented in Table 6-2 of the EPA’s Exposure Factors Handbook (U.S. EPA, 2011b). 

11 Most of the age-related variability is seen for moderate and high intensity activities, 

12 except for individuals under 1 year. For moderate intensity, ventilation rate increases with 

13 age through childhood and adulthood until age 61, after which a moderate decrease is 

14 observed. Ventilation rate is most variable for high intensity activities. Children aged 1 to 

15 <11 years have ventilation rates of approximately 40 L/min, while children aged 11+ and 

16 adults have ventilation rates of approximately 50 L/min. The peak is observed for the 51 

17 to <61 age group, at 53 L/min, with lower ventilation rates for older adults.
Draft – Do Not Cite or Quote 4-23 June 2012

Table 4-5 Mean ventilation rates (L/min) at different activity levels for 

different age groups.

Age Group Sleep or Nap Sedentary/Passive Light Intensity Moderate 

Intensity

High Intensity

Birth to <1 yr 3.0 3.1 7.6 14 26

1 to <2 yr 4.5 4.7 12 21 38

2 to <3 yr 4.6 4.8 12 21 39

3 to <6 yr 4.3 4.5 11 21 37

6 to <11 yr 4.5 4.8 11 22 42

11 to <16 yr 5.0 5.4 13 25 49

16 to <21 yr 4.9 5.3 12 26 49

21 to <31 yr 4.3 4.2 12 26 50

31 to <41 yr 4.6 4.3 12 27 49

41 to <51 yr 5.0 4.8 13 28 52

51 to <61 yr 5.2 5.0 13 29 53

61 to <71 yr 5.2 4.9 12 26 47

71 to <81 yr 5.3 5.0 12 25 47

81+ yr 5.2 4.9 12 25 48

Source: Data from Exposure Factors Handbook (U.S. EPA, 2011b).

1 A dramatic increase in ventilation rate occurs as exercise intensity increases. For children 

2 and adults <31 years, high intensity activities result in nearly double the ventilation rate 

3 for moderate activity, which itself is nearly double the rate for light activity. Children 

4 have other important differences in ventilation compared to adults. As discussed in 

5 Chapter 5, children tend to have a greater oral breathing contribution than adults, and 

6 they breathe at higher minute ventilations relative to their lung volumes. Both of these 

7 factors tend to increase dose normalized to lung surface area.

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

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

10 age, gender, employment, and lifestyle-dependent factors. These differences may 

11 manifest as higher mean exposures or more frequent high-exposure episodes for some 

12 individuals. The extent to which longitudinal variability in individuals contributes to the 

13 population variability in activity and location can be quantified by the ratio of between14 person variance to total variance in time spent in different locations and activities (the 

15 intraclass correlation coefficient, ICC). Xue et al. (2004) quantified ICC values in time16 activity data collected by Harvard University for 160 children aged 7–12 years in 

17 Southern California (Geyh et al., 2000). For time spent outdoors, the ICC was 

18 approximately 0.15, indicating that 15% of the variance in outdoor time was due to 
Draft – Do Not Cite or Quote 4-24 June 2012

1 between-person differences. The ICC value might be different for other population 

2 groups.

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

Figure 4-3 Distribution of time that NHAPS respondents spent in ten 

microenvironments based on smoothed 1-min diary data.

3 The EPA’s National Exposure Research Laboratory (NERL) has consolidated the most 

4 important human activity databases into one comprehensive database called the 

5 Consolidated Human Activity Database (CHAD). The current version of CHAD contains 

6 data from nineteen human activity pattern studies (including NHAPS), which were 

7 evaluated to obtain over 33,000 person-days of 24-h human activities in CHAD 

8 (McCurdy et al., 2000). The surveys include probability-based recall studies conducted 

9 by EPA and the California Air Resources Board, as well as real-time diary studies 

10 conducted in individual U.S. metropolitan areas using both probability-based and 

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

12 each subject consist of one or more days of sequential activities, in which each activity is 

13 defined by start time, duration, activity type, and microenvironment classification 

14 (i.e., location). Activities vary from one minute to one hour in duration, with longer 

15 activities being subdivided into clock-hour durations to facilitate exposure modeling. 

16 CHAD also provides information on the level of exertion associated with each activity, 
Draft – Do Not Cite or Quote 4-25 June 2012

1 which can be used by exposure models, including the APEX model (Section 4.5.3), to 

2 estimate ventilation rate and pollutant dose.

4.4.2 Ozone Averting Behavior

3 Individuals can reduce their exposure to O3 by altering their behaviors, such as by staying 

4 indoors, being active outdoors when air quality is better, and by reducing activity levels 

5 or time spent being active outdoors on high-O3 days. To assist the public in avoiding 

6 exposure to air pollution on days with high pollutant concentrations, EPA has developed 

7 an information tool known as the Air Quality Index (AQI) to provide information to the 

8 public on ambient levels of pollutants and the potential for individuals to experience 

9 health effects (U.S. EPA, 2011a). The AQI describes the potential for health effects from 

10 O3 (and other individual pollutants) in six color-coded categories of air-quality, ranging 

11 from good (green), moderate (yellow), unhealthy for sensitive groups (orange), unhealthy 

12 (red), very unhealthy (purple), and hazardous (maroon). The levels are associated with 

13 descriptors of the likelihood of health effects and the populations most likely to be 

14 affected. For example, the orange level indicates that the general population is not likely 

15 to be at risk, but susceptible groups may experience health effects. These advisories 

16 explicitly state that children, older adults, people with lung disease, and those who are 

17 active outdoors may be at greater risk from exposure to air pollution. Forecasted and 

18 actual conditions typically are reported to the public during high-O3 months through local 

19 media outlets, using various versions of this air-quality categorization scheme. People are 

20 advised to change their behavior to reduce exposure depending on predicted O3

21 concentrations and the likelihood of risk. Behavioral recommendations include being 

22 active outdoors when air quality is better, and reducing activity levels or the time spent 

23 being active outdoors on high-O3 days. Staying indoors to reduce exposure is only 

24 recommended when the AQI is at or above the very unhealthy range. 

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

26 several studies, especially for potentially susceptible populations, such as children, older 

27 adults, and asthmatics. Reduced time spent outdoors was reported in an activity diary 

28 study in 35 U.S. cities (Mansfield et al., 2006), which found that asthmatic children who 

29 spent at least some time outdoors reduced their total time spent outdoors by an average of 

30 30 min on a code red O3 day relative to a code green, yellow, or orange day; however, the 

31 authors noted that there was appreciable variation in both the overall amount of time 

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

33 children. Bresnahan et al. (1997) examined survey data collected during 1985-86 from a 

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

35 function, by an averting behavior model. A regression analysis indicated that individuals 
Draft – Do Not Cite or Quote 4-26 June 2012

1 with smog-related symptoms spent about 12 minutes less time outdoors over a two-day 

2 period for each 10 ppb increase in O3 concentration above 120 ppb. Considering that the 

3 average daily maximum O3 concentration at the time was approximately 180 ppb on days 

4 when the then-current standard (1-h max of 120 ppb) was exceeded, this implies that 

5 those individuals spent about 40 minutes less time outside per day on a typical high O3

6 day compared to days with O3 concentrations below the standard. However, the behavior 

7 was not specifically linked to exceedances or air quality alerts.

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

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

10 (1997) study, 40 percent of respondents reported staying indoors on days when air quality 

11 was poor. Individuals who reported experiencing smog-related symptoms were more 

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

13 respiratory conditions did not have a statistically significant effect on behavior. A study 

14 of parents of asthmatic children (McDermott et al., 2006) suggests that parents are aware 

15 of the hazard of outdoor air pollution and the official alerts designed to protect them and 

16 their children. It also suggests that a majority of parents (55%) comply with 

17 recommendations of the alerts to restrict children’s outdoor activity, with more parents of 

18 asthmatics reporting awareness and responsiveness to alerts. However, only 7% of all 

19 parents complied with more than one-third of the advisories issued (McDermott et al., 

20 2006). Wen et al. (2009) analyzed data from the 2005 Behavioral Risk Factor 

21 Surveillance System (BRFSS) and indicated that people with asthma are about twice as 

22 likely as people without asthma to reduce their outdoor activities based on either media 

23 alerts of poor air quality (31% vs. 16%) or individual perception of air quality (26% vs. 

24 12%). Respondents who had received advice from a health professional to reduce outdoor 

25 activity when air quality is poor were more likely to report a reduction based on media 

26 alerts, both for those with and without asthma. In a study of randomly selected 

27 individuals in Houston, TX and Portland, OR, Semenza et al. (2008) found that a 

28 relatively small fraction of survey respondents (9.7% in Houston, 10.5% in Portland) 

29 changed their behaviors during poor air quality episodes. This fraction is appreciably 

30 lower than the fraction reported for people with asthma in the Wen et al. (2009) study, 

31 although it is similar to the fraction reported in that study for those without asthma. Most 

32 of the people in the Semenza et al. (2008) study reported that their behavioral changes 

33 were motivated by self-perception of poor air quality rather than an air quality advisory. 

34 It should be noted that the McDermott et al. (2006), Wen et al. (2009), and Semenza et al. 

35 (2008) studies evaluated air quality in general and therefore are not necessarily specific to 

36 O3. 

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

38 Atlanta area showed that advisories can raise awareness among commuters but do not 
Draft – Do Not Cite or Quote 4-27 June 2012

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

2 2003). This finding is consistent with a survey for 1000 commuters in Denver, Colorado, 

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

4 advisories, but did not alter their commuting behavior (Blanken et al., 2001).

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

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

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

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

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

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

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

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

13 attendance at two outdoor facilities (i.e., a zoo and an observatory) was reduced by 

14 6-13% on days when smog alerts were announced, with greater decreases observed 

15 among children and older adults (Neidell, 2010, 2009).

16 The studies discussed in this section indicate that averting behavior is dependent on 

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

18 experiencing smog-related symptoms reduce their time spent outdoors and are more 

19 likely to change their behavior than those without respiratory conditions. Children and 

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

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

22 commuting behavior.

4.4.3 Population Proximity to Fixed-Site Ozone Monitors

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

24 and the population living near each monitor varies as well. Monitoring sites in rural areas 

25 are generally located in national or state parks and forests, and these monitors may be 

26 relevant for exposures of exercising visitors as well as those who live in similar locations. 

27 They also serve as an important source of data for evaluating ecological effects of O3

28 (Chapter 9). Rural monitors tend to be less affected than urban monitors by strong and 

29 highly variable anthropogenic sources of species participating in the formation and 

30 destruction of O3 (e.g., onroad mobile sources) and more highly influenced by regional 

31 transport of O3 or O3 precursors (Section 3.6.2.2). This may contribute to less diel 

32 variability in O3 concentration than is observed in urban areas.

33 A variety of factors determine the siting of the O3 monitors that are part of the SLAMS 

34 network reporting to AQS. As discussed in Section 3.5.6, the number and location of 
Draft – Do Not Cite or Quote 4-28 June 2012

1 required O3 monitors in an urban area depend on O3 concentration and population, among 

2 other factors. Areas classified as serious, severe, or extreme nonattainment have 

3 additional monitoring requirements. Generally, high-O3 urban areas with a population of 

4 50,000 or greater are required to have at least one monitor; in low- or moderate5 concentration areas, the minimum population for a required monitor is 350,000. Most 

6 large U.S. cities have several monitors, as shown in Figure 3-76 through Figure 3-95.

7 As an illustration of the location of O3 monitors and their concentrations with respect to 

8 population density, Figure 4-4 through Figure 4-6 present this information for Atlanta, 

9 Boston, and Los Angeles, the three cities selected for detailed analysis in Chapter 3. They 

10 represent a cross-section with respect to geographic distribution, O3 concentration, 

11 layout, geographic features, and other factors. The maps show the location of O3

12 monitors, identified by the same letters as in Chapter 3 to facilitate intercomparisons, 

13 along with the 2007-09 mean 8-h daily max O3 concentration for perspective on the 

14 variation in O3 concentration across the urban area. Population density at the census 

15 block group level is also presented on the maps.
Draft – Do Not Cite or Quote 4-29 June 2012

Figure 4-4 Map of the Atlanta CSA including ozone monitor locations and 

major roadways with respect to census block group population 

density estimates for 2009.
Draft – Do Not Cite or Quote 4-30 June 2012

Figure 4-5 Map of the Boston CSA including ozone monitor locations and 

major roadways with respect to census block group population 

density estimates for 2009.
Draft – Do Not Cite or Quote 4-31 June 2012

Figure 4-6 Map of the Los Angeles CSA including ozone monitor locations 

and major roadways with respect to census block group population 

density estimates for 2009.
Draft – Do Not Cite or Quote 4-32 June 2012

1 Similarities and differences are apparent among the cities. The spatial distribution of 

2 monitor locations in Atlanta and Boston is similar, with one site (site A) near the high 

3 population density area and other monitors in surrounding areas of lower population 

4 density. In Atlanta, the monitors near the city all have similar concentrations, while 

5 somewhat lower concentrations are observed at sites I and J, which are located >50 km 

6 from the city center. Boston shows a different spatial concentration pattern, with some 

7 low and some high concentrations in urban and less-populated areas. The differences in 

8 spatial concentration profiles between the two cities may be due to more consistent 

9 terrain in Atlanta compared with Boston, which has a coastline, along with the downwind 

10 influence of New York and other northeastern cities contributing to concentration 

11 variability.

12 Los Angeles has a much more complex spatial pattern of monitors, population, and 

13 geography. There are a large number of monitors located in multiple levels of population 

14 density across the entire CSA, which includes substantial rural areas. Most monitors are 

15 near at least moderate population density areas, but there are some high-density areas 

16 without a monitor. Concentrations increase in a somewhat radial or west-east pattern 

17 from the city, with lower concentrations near the port of Long Beach (monitors B, C, and 

18 F). The highest concentrations are located near the San Bernadino forest (e.g., monitors 

19 AG, AO,and AR), which have lower population density, but more potential for ecological 

20 impacts. Low concentrations in highly populated areas near the coast likely reflect 

21 titration by NOX and other atmospheric constituents, while high downwind 

22 concentrations reflect lack of local sources and increased photochemical processing time.

23 The location of these monitors relative to the location of dense population centers varies 

24 among urban areas. NCore sites, a subset of the overall monitoring network, are designed 

25 with population exposure as a monitoring objective, and the monitoring requirements in 

26 40 CFR Part 58, Appendix D include population density as one of several factors that 

27 would be considered in designing the O3 monitoring program for an area. At least one site 

28 for each MSA is designed to be a maximum concentration site, which could presumably 

29 represent the location with the maximum exposure potential in the city. Sites may also be 

30 required upwind and downwind of high-concentration urban areas.

31 All three cities have some high population density areas without an O3 monitor. The 

32 siting considerations for NCore monitors generally target the neighborhood (0.5-4 km) or 

33 urban (4-50 km) scale to provide representative concentrations throughout the 

34 metropolitan area; however, a middle-scale (0.1-0.5 km) site may be acceptable in cases 

35 where the site can represent many such locations throughout a metropolitan area. In other 

36 words, a monitor could potentially represent exposures in other similar areas of the city if 

37 land use and atmospheric chemistry conditions are similar. This is supported by the 
Draft – Do Not Cite or Quote 4-33 June 2012

1 correlation analyses in Chapter 3. For example, in Los Angeles, monitors H and L are 

2 located in medium-density areas and show moderately high correlation (0.78), although 

3 they are some 50 km apart. 

4 Although proximity to a monitor does not determine the degree to which that monitor 

5 represents an individual’s ambient exposure, it is one indicator. One way to calculate 

6 monitor representativeness is to calculate the fraction of the urban population living 

7 within a certain radius of a monitor. Table 4-6 presents the fraction of the population in 

8 selected cities living within 1, 5, 10, and 20 km of an O3 monitor. Values are presented 

9 for both total population and for those under 18 years of age, a potentially susceptible 

10 population to the effects of O3. The data indicate that relatively few people live within 

11 1 km of an O3 monitor, while nearly all of the population in most cities lives within 

12 20 km of a monitor. Looking at the results for a 5-km radius, corresponding roughly to 

13 the neighborhood scale (Section 3.5.6.1), generally 20-30% of the population lives within 

14 this distance from an O3 monitor. Some cities have a greater population in this buffer, 

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

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

17 clear trend.

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

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

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

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

22 represented by each of the 11 monitors in the city ranged from 2.9-22%. The two 

23 monitors closest to the city center (sites A and B on Figure 4-4) accounted for 16% and 

24 8% of the population, respectively. Site B has two listed monitoring objectives, highest 

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

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

27 population (22%). The eight monitors with a primary monitoring objective of population 

28 exposure account for 2.9-17% of the population per monitor. 
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Table 4-6 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 Atlanta population fractions for children (<18 years of age) are similar to those for the 

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

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

4 with respect to the monitors, with most monitors showing a difference of more than a 

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

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

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

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

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

10 potentially susceptible populations.
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4.5 Exposure Modeling

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

2 estimate exposures, particularly for large populations for which individual-level 

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

4 epidemiologic studies or as stand-alone assessments of the level of exposure likely to be 

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

6 approaches used to improve exposure estimates, including concentration surface 

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

8 exchange rate modeling, which estimates building ventilation based on housing 

9 characteristics and meteorological parameters; and microenvironment-based exposure 

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

11 pattern simulations to estimate time-weighted exposures based on concentrations in 

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

13 summarized in Table 4-7. The remainder of this section provides more detail on specific 

14 modeling approaches, as well as results of applying the models.
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Table 4-7 Characteristics of exposure modeling approaches.

Model Type Model Description Strengths Limitations

Concentration 

Surface

Spatial 

Interpolation 

(e.g., Inverse 

Distance 

Weighting, Kriging)

Measured concentrations 

are interpolated across an 

area to yield local outdoor 

concentration estimates

High concentration 

resolution; uses available 

data; requires low to 

moderate resources for 

implementation

Spatial heterogeneity not 

fully captured; a single 

high-concentration monitor 

can skew results; no 

location-activity 

information

Chemistrytransport 

(e.g., CMAQ)

Grid-based O3

concentrations are 

calculated from precursor 

emissions, meteorology, and 

atmospheric chemistry and 

physics

First-principles 

characterization of 

physical and chemical 

processes influencing O3

formation

Grid cell resolution; 

resource-intensive; no 

location-activity 

information 

Land-use 

regression (LUR)

Merges concentration data 

with local-scale variables 

such as land use factors to 

yield local concentration 

surface

High concentration 

resolution

Reactivity and small-scale 

spatial variability of O3; 

location-specific, limiting 

generalizability; no 

location-activity 

information

Air Exchange Rate Mechanistic 

(LBL, LBLX)

Uses database on building 

leakage tests to predict AER 

based on building 

characteristics and 

meteorological variables 

(including natural ventilation 

in LBLX)

Physical characterization 

of driving forces for air 

exchange

Moderate resource 

requirement; no locationactivity information

Empirical Predicts AER based on 

factors such as building age 

and floor area

Low input data 

requirements

Cannot account for 

meteorology; no locationactivity information

Integrated 

Microenvironmental 

Exposure and Dose

Population 

(APEX, SHEDS)

Stochastic treatment of air 

quality data, demographic 

variables, and activity 

pattern to generate 

estimates of 

microenvironmental 

concentrations, exposures, 

and doses

Probabilistic estimates of 

exposure and dose 

distributions for specific 

populations; consideration 

of nonambient sources; 

small to moderate 

uncertainty for exercising 

asthmatic children (APEX)

Resource-intensive; 

evaluation with measured 

exposures; 

underestimation of 

multiple high-exposure 

events in an individual 

(APEX)

4.5.1 Concentration Surface Modeling

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

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

3 monitors estimated using a model to compensate for missing data. The calculated O3

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

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

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

7 different microenvironments. This is an important consideration in the utility of these 

8 methods for exposure assessment; while improved local-scale estimates of outdoor 

9 concentrations may contribute to better assignment of exposures, information on activity 
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1 patterns is needed to produce estimates of personal exposure. There are three main types 

2 of approaches: spatial interpolation of measured concentrations; statistical models using 

3 meteorological variables, pollutant concentrations, and other predictors to estimate 

4 concentrations at receptors in the domain; and rigorous first-principle models, such as 

5 chemistry-transport models or dispersion models incorporating O3 chemistry. Some 

6 researchers have developed models that combine these techniques. The models may be 

7 applied over urban, regional, or national spatial scales, and can be used to estimate daily 

8 concentrations or longer-term averages. This discussion will focus on short-term 

9 concentrations estimated across urban areas. 

10 The 2006 O3 AQCD discussed concentration surface models, focusing on chemistry11 transport models as well as geospatial and spatiotemporal interpolation techniques (e.g., 

12 Christakos and Vyas, 1998a, b; Georgopoulos et al., 1997). Recent research has 

13 continued to refine and extend the modeling approaches. A few recent papers have 

14 compared different approaches for the same urban area.

15 Marshall et al. (2008) compared four spatial interpolation techniques for estimation of O3

16 concentrations in Vancouver, BC. The investigators assigned a daily average O3

17 concentration to each of the 51,560 postal-code centroids using one of the following 

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

19 of all monitors within 10 km; (3) the inverse-distance-weighted (IDW) average of all 

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

21 Method 1 (the nearest-monitor approach) and Method 4 (IDW-50 km) had similar mean 

22 and median estimated annual- and monthly-average concentrations, although the 10th23 90th percentile range was smaller for IDW-50. This is consistent with the averaging of 

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

25 the two methods was 0.93 for monthly-average concentrations and 0.78 for annual26 average concentrations. Methods 2 and 3 were considered sub-optimal and were excluded 

27 from further analysis. In the case of Method 2, a single downtown high-concentration 

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

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

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

31 immediately adjacent to a monitoring site. CMAQ concentration estimates using a 

32 4 km×4 km grid were also compared to the interpolation techniques in this study. Mean 

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

34 and Method 4 estimates for both annual and monthly average concentrations. This may 

35 be due in part to the CMAQ grid size, which was too coarse to reveal near-roadway 

36 decrements in O3 concentration due to titration by NO. The IQR for the annual average 

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

38 CMAQ IQR was approximately twice as large, indicating a seasonal effect.
Draft – Do Not Cite or Quote 4-38 June 2012

1 Bell (2006) compared CMAQ estimates for northern Georgia with nearest-monitor and 

2 spatial interpolation techniques, including IDW and kriging. The area-weighted 

3 concentration estimates from CMAQ indicated areas of spatial heterogeneity that were 

4 not captured by approaches based on the monitoring network. The author concluded that 

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

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

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

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

9 pollutants, predominantly NO2, as a function of several land use factors, such as land use 

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

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

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

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

14 factors. The regression is then used for predicting concentrations at multiple locations 

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

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

17 associations between pollutants and covariates from those of correlated copollutants, 

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

19 changing temporal conditions, and introduction of confounding factors when LUR is used 

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

21 that have been developed for O3 at the urban scale. Brauer et al. (2008) evaluated the use 

22 of LUR and IDW-based spatial-interpolation models in epidemiologic analyses for 

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

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

25 is appropriate for secondary pollutants such as NO2 and PM2.5 with less spatial variability. 

26 Although O3 is also a secondary pollutant, its reactivity and high small-scale spatial 

27 variability near high-traffic roadways indicates this conclusion may not apply for O3. 

28 At a much larger spatial scale, EU-wide, Beelen et al. (2009) compared a LUR model for 

29 O3 with ordinary kriging and universal kriging, which incorporated meteorological, 

30 topographical, and land use variables to characterize the underlying trend. The LUR 

31 model performed reasonably well at rural locations (5-km resolution), explaining a higher 

percentage of the variability (R2 32 = 0.62) than for other pollutants. However, at the urban 

33 scale (1-km resolution), only one variable was selected into the O3 LUR model 

(high-density residential land use), and the R2 34 value was very low (0.06). Universal 

35 kriging was the best method for the large-scale composite EU concentration map, for O3

as well as for NO2 and PM10, with an R2 36 value for O3 of 0.70. The authors noted that 

37 these methods were not designed to capture spatial variation in concentrations that are 
Draft – Do Not Cite or Quote 4-39 June 2012

1 known to occur within tens of meters of roadways (Section 3.6.2.1), which could partially 

2 explain poor model performance at the urban scale.

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

4 roadways. Mcconnell et al. (2006) developed a regression model to predict residential O3

5 concentrations in southern California using estimates of residential NOX calculated from 

6 traffic data with the CALINE4 line source dispersion model. The annual average model 

results were well-correlated (R2 7 = 0.97) with multi-year average monitoring data. The 

8 authors estimated that local traffic contributes 18% of NOX concentrations measured in 

9 the study communities, with the remainder coming from regional background. Their 

10 regression model indicates that residential NOX reduces residential O3 concentrations by 

11 0.51 ppb (SE 0.11 ppb) O3 per 1 ppb NOX, and that a 10th-90th percentile increase in 

12 local NOX results in a 7.5 ppb decrease in local O3 concentrations. This intra-urban 

13 traffic-related variability in O3 concentrations suggests that traffic patterns are an 

14 important factor in the relationship between central site monitor and residential O3, and

15 that differences in traffic density between the central site monitor and individual homes 

16 could result in either an overestimate or underestimate of residential O3.

17 A substantial number of researchers have used geostatistical methods and chemistry18 transport models to estimate O3 concentrations at urban, regional, national, and 

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

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

21 for long-term exposure assessment, O3 forecasts, or evaluating emission control 

22 strategies. However, as discussed at the beginning of this section, caveats regarding the 

23 importance of activity pattern information in estimating personal and population exposure 

24 should be kept in mind. 

4.5.2 Residential Air Exchange Rate Modeling

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

26 an important mechanism for entry of ambient O3. As described in Section 4.3.2, the 

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

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

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

30 such as APEX, SHEDS, and EMI (discussed in Section 4.5.3) (U.S. EPA, 2011c, 2009b; 

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

32 exposure models, mechanistic and empirical (i.e., regression-based) AER models can be 

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

34 characteristics (e.g., age, number of stories, wind sheltering), occupant behavior 
Draft – Do Not Cite or Quote 4-40 June 2012

1 (e.g., window opening), climatic region, and meteorology (e.g., local temperature and 

2 wind speed). Mechanistic AER models use these meteorological parameters to account 

3 for the physical driving forces of the airflows due to pressure differences across the 

4 building envelope from wind and indoor-outdoor temperature differences (ASHRAE, 

5 2009). Empirical AER models do not consider the driving forces from the wind and 

6 indoor-outdoor temperature differences. Instead, a scaling constant can be used based on 

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

8 Single-zone mechanistic models represent a whole-building as a single, well-mixed 

9 compartment. These AER models, such as the Lawrence Berkeley Laboratory (LBL) 

10 model, can predict residential AER using input data from whole-building pressurization 

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

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

13 leakage area model using population-level census and residential survey data (Sherman 

14 and McWilliams, 2007) and individual-level questionnaire data (Breen et al., 2010). The 

15 LBL model, which predicts the AER from air infiltration (i.e., small uncontrollable 

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

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

18 AER predictions from the LBL and LBLX models were compared to daily AER 

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

20 central North Carolina (Breen et al., 2010). For the individual model-predicted and 

measured AER, the median absolute difference was 43% (0.17 h-1) and 40% (0.17 h-1 21 ) for 

22 the LBL and LBLX models, respectively. Given the uncertainty of the AER 

23 measurements (accuracy of 20-25% for occupied homes), these results demonstrate the 

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

25 openings) and natural ventilation (e.g., window opening) to help reduce the AER 

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

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

28 predict indoor concentrations of ambient O3 that may be substantial for conditions of high 

29 AER such as open windows.

4.5.3 Microenvironment-Based Models

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

31 Human Exposure and Dose Simulation (SHEDS) integrated microenvironmental 

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

33 2009b; Burke et al., 2001). These are described in detail in the 2008 NOX ISA (U.S. EPA, 

34 2008b), in AX3.6.1. Stochastic models utilize distributions of pollutant-related and 

35 individual-level variables, such as ambient and local O3 concentration contributions and 
Draft – Do Not Cite or Quote 4-41 June 2012

1 breathing rate respectively, to compute the distribution of individual exposures across the 

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

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

4 framework rather than point estimates allows the models to incorporate uncertainty and 

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

6 time-weighted exposure for modeled individuals by summing exposure in each 

7 microenvironment visited during the exposure period. 

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

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

10 concentrations in a set of microenvironments are generated either by mass balance 

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

12 factors. Microenvironments modeled include indoor residences; other indoor locations, 

13 such as schools, offices, and public buildings; and vehicles. The sequence of 

14 microenvironments and exertion levels during the exposure period is determined from 

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

16 profile for each simulated individual by sampling from distributions of demographic 

17 variables such as age, gender, and employment; physiological variables such as height 

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

19 conditioning. Activity and location (microenvironmental) patterns from a database such 

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

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

22 rates for each individual are calculated for each activity based on predicted energy 

23 expenditures, and the corresponding dose may then be computed. APEX has an algorithm 

24 to estimate O3 dose and changes in FEV1 resulting from O3 exposure. Summaries of 

25 individual- and population-level metrics are produced, such as maximum exposure or 

26 dose, number of individuals exceeding a specified exposure/dose, and number of 

27 person-days at or above benchmark exposure levels. The models also consider the 

28 nonambient contribution to total exposure. Nonambient source terms are added to the 

29 infiltration of ambient pollutants to calculate the total concentration in the 

30 microenvironment. Output from model runs with and without nonambient sources can be 

31 compared to estimate the ambient contribution to total exposure and dose.

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

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

34 Total Risk Studies) in a simulation of O3 exposure in Philadelphia over a 2-week period 

35 in July 1999. 500 individuals were sampled from CHAD in each of 482 census tracts to 

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

37 over the modeling domain were calculated from interpolation of photochemical modeling 

38 results and fixed-site monitor concentrations. These concentrations were then used as 
Draft – Do Not Cite or Quote 4-42 June 2012

1 input data for SHEDS. Median microenvironmental concentrations predicted by SHEDS 

2 for nine simulated microenvironments were strongly correlated with outdoor 

3 concentrations, a result consistent with the lack of indoor O3 sources in the model. A 

4 regression of median microenvironmental concentrations against outdoor concentrations 

5 indicated that the microenvironmental concentrations were appreciably lower than 

6 outdoor concentrations (regression slope = 0.26). 95th percentile microenvironmental 

7 concentrations were also well correlated with outdoor concentrations and showed a 

8 regression slope of 1.02, although some microenvironmental concentrations were well 

9 below the outdoor values. This suggests that in most cases the high-end concentrations 

10 were associated with outdoor microenvironments. Although the authors did not report 

11 exposure statistics for the population, their dose calculations also indicated that O3 dose 

12 due to time spent outdoors dominated the upper percentiles of the population dose 

13 distribution. They found that both the 50th and 95th percentile O3 concentrations were 

14 correlated with census-tract level outdoor concentrations estimated by photochemical 

15 modeling combined with spatiotemporal interpolation, and attributed this correlation to 

16 the lack of indoor sources of O3. Relationships between exposure and concentrations at 

17 fixed-site monitors were not reported.

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

19 uncertainty in input parameters and databases to the uncertainty in model outputs 

20 (Langstaff, 2007). The Monte Carlo analysis indicates that the uncertainty in model 

21 exposure estimates for asthmatic children during moderate exercise is small to moderate, 

22 with 95% confidence intervals of at most ± 6 percentage points at exposures above 60, 

23 70, and 80 ppb (8-h avg) However, APEX appears to substantially underestimate the 

24 frequency of multiple high-exposure events for a single individual. The two main sources 

25 of uncertainty identified were related to the activity pattern database and the spatial 

26 interpolation of fixed-site monitor concentrations to other locations. Additional areas 

27 identified in the uncertainty analysis for potential improvement include: further 

28 information on children’s activities, including longitudinal patterns in the activity pattern 

29 database; improved information on spatial variation of O3 concentrations, including in 

30 near-roadway and indoor microenvironments; and data from personal exposure monitors 

31 with shorter averaging times to capture peak exposures and lower detection limits to 

32 capture low indoor concentrations. A similar modeling approach has been developed for 

33 panel epidemiologic studies or for controlled human exposure studies, in which activity 

34 pattern data specific to the individuals in the study can be collected. Time-activity data is 

35 combined with questionnaire data on housing characteristics, presence of indoor or 

36 personal sources, and other information to develop a personalized set of model input 

37 parameters for each individual. This model, the Exposure Model for Individuals, has been 

38 developed by EPA’s National Exposure Research Laboratory (U.S. EPA, 2011c; 

39 Zartarian and Schultz, 2010).
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4.6 Implications for Epidemiologic Studies

1 Exposure measurement error, which refers to the uncertainty associated with using 

2 exposure metrics to represent the actual exposure of an individual or population, can be 

3 an important contributor to variability in epidemiologic study results. Time-series studies 

4 assess the daily health status of a population of thousands or millions of people over the 

5 course of multiple years (i.e., thousands of days) across an urban area by estimating their 

6 daily exposure using a short monitoring interval (hours to days). In these studies, the 

7 community-averaged concentration of an air pollutant measured at central-site monitors 

8 is typically used as a surrogate for individual or population ambient exposure. In 

9 addition, panel studies, which consist of a relatively small sample (typically tens) of 

10 study participants followed over a period of days to months, have been used to examine 

11 the health effects associated with short-term exposure to ambient concentrations of air 

12 pollutants (Delfino et al., 1996). Panel studies may also apply a microenvironmental 

13 model to represent exposure to an air pollutant. A longitudinal cohort epidemiologic 

14 study, such as the ACS cohort study, typically involves hundreds or thousands of subjects 

15 followed over several years or decades (Jerrett et al., 2009). Concentrations are generally 

16 aggregated over time and by community to estimate exposures. 

17 Exposure error can under- or over-estimate epidemiologic associations between ambient 

18 pollutant concentrations and health outcomes by biasing effect estimates toward or away 

19 from the null, and tends to widen confidence intervals around those estimates (Sheppard 

20 et al., 2005; Zeger et al., 2000). Exposure misclassification can also tend to obscure the 

21 presence of potential thresholds for health effects, as demonstrated by a simulation study 

22 of nondifferential exposure misclassification (Brauer et al., 2002). The importance of 

23 exposure misclassification varies with study design and is dependent on the spatial and 

24 temporal aspects of the design. For example, the use of a community-averaged O3

25 concentration in a time-series epidemiologic study may be adequate to represent the day26 to-day temporal concentration variability used to evaluate health effects, but may not 

27 capture differences in the magnitude of exposure due to spatial variability. Other factors 

28 that could influence exposure estimates include nonambient exposure, topography of the 

29 natural and built environment, meteorology, measurement errors, use of ambient O3

30 concentration as a surrogate for ambient O3 exposure, and the presence of O3 in a mixture 

31 of pollutants. The following sections will consider various sources of error and how they 

32 affect the interpretation of results from epidemiologic studies of different designs.
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4.6.1 Non-Ambient Ozone Exposure

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

2 personal exposure. There are relatively few indoor sources of O3; as a result, personal O3

3 exposure is expected to be dominated by ambient O3 in outdoor microenvironments and 

4 in indoor microenvironments with high air exchange rates (e.g., with open windows). 

5 Even in microenvironments where nonambient exposure is substantial, such as in a room 

6 with an O3 generator, this nonambient exposure is unlikely to be temporally correlated 

7 with ambient O3 exposure (Wilson and Suh, 1997), and therefore would not affect 

8 epidemiologic associations between O3 and a health effect (Sheppard et al., 2005). In 

9 simulations of a nonreactive pollutant, Sheppard et al. (2005) concluded that nonambient 

10 exposure does not influence the health outcome effect estimate if ambient and 

11 nonambient concentrations are independent. Since personal exposure to ambient O3 is 

12 some fraction of the ambient concentration, it should be noted that effect estimates 

13 calculated based on personal exposure rather than ambient concentration will be 

14 increased in proportion to the ratio of ambient concentration to ambient exposure, and 

15 daily fluctuations in this ratio can widen the confidence intervals in the ambient 

16 concentration effect estimate, but uncorrelated nonambient exposure will not bias the 

17 effect estimate (Sheppard et al., 2005; Wilson and Suh, 1997). 

4.6.2 Spatial and Temporal Variability

18 Spatial and temporal variability in O3 concentrations can contribute to exposure error in 

19 epidemiologic studies, whether they rely on central-site monitor data or concentration 

20 modeling for exposure assessment. Spatial variability in the magnitude of concentrations 

21 may affect cross-sectional and large-scale cohort studies by undermining the assumption 

22 that intra-urban concentration and exposure differences are less important than inter23 urban differences. This issue may be less important for time-series studies, which rely on 

24 day-to-day temporal variability in concentrations to evaluate health effects. Low inter25 monitor correlations contribute to exposure error in time-series studies, including bias 

26 toward the null and increased confidence intervals.

4.6.2.1 Spatial Variability

27 Spatial variability of O3 concentrations is highly dependent on spatial scale; in effect, O3

28 is a regional pollutant subject to varying degrees of local variability. In the immediate 

29 vicinity of roadways, O3 concentrations are reduced due to reaction with NO and other 

30 species (Section 4.3.4.2); over spatial scales of a few kilometers, O3 may be more 
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1 homogeneous due to its formation as a secondary pollutant; over scales of tens of 

2 kilometers, atmospheric processing can result in higher concentrations downwind of an 

3 urban area than in the urban core. Local-scale variations have a large impact on the 

4 relative magnitude of concentrations among urban monitors, while conditions favoring 

5 high or low rates of O3 formation (e.g., temperature) vary over large spatial scales. This 

6 suggests that neighborhood monitors are likely to track one another temporally, but miss 

7 small-scale spatial variability in magnitude. This is supported by an analysis in Atlanta 

8 that found correlations greater than 0.8 for daily O3 concentration metrics (1-h max, 

9 8-h max, and 24-h avg) measured at monitors 10-60 km apart (Darrow et al., 2011a). In 

10 rural areas, a lower degree of fluctuation in O3 precursors such as NO and VOCs is likely 

11 to make the diel concentration profile less variable than in urban areas, resulting in more 

12 sustained ambient levels. Spatial variability contributes to exposure error if the ambient 

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

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

15 residence and/or worksite (in the absence of indoor O3 sources). Averaging data from a 

16 large number of samplers will dampen intersampler variability, and use of multiple 

17 monitors over smaller land areas may allow for more variability to be incorporated into 

18 an epidemiologic analysis. 

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

20 several subcommunities having different sources and topographies, such as the 

21 Los Angeles CSA (Section 3.6.2.1 and Section 4.4.3). Ozone monitors in Los Angeles 

22 had a much wider range of intermonitor correlations (-0.06 to 0.97) than Atlanta (0.61 to 

23 0.96) or Boston (0.56 to 0.97) using 2007-2009 data. Although the negative and near-zero 

24 correlations in Los Angeles were observed for monitors located some distance apart 

25 (>150 km), some closer monitor pairs had low positive correlations, likely due to changes 

26 in land use, topography, and airflow patterns over short distances. Lower COD values, 

27 which indicate less variability among monitors in the magnitude of O3 concentrations, 

28 were observed in Atlanta (0.05-0.13) and Boston (0.05-0.19) than Los Angeles 

29 (0.05-0.56), although a single monitor (AM) was responsible for all Los Angeles COD 

30 values above 0.40. The spatial and temporal variability in O3 concentration in 24 MSAs 

31 across the U.S. was also examined in the 2006 O3 AQCD by using Pearson correlation 

32 coefficients, values of the 90th percentile of the absolute difference in O3 concentrations, 

33 and CODs. No clear discernible regional differences across the U.S. were found in the 

34 ranges of parameters analyzed. 

35 An analysis of the impact of exposure error due to spatial variability and instrument 

36 imprecision on time-series epidemiologic study results indicated that O3 has relatively 

37 low exposure error compared to other routinely monitored pollutants, and that the 

38 simulated impact on effect estimates is minor. Goldman et al. (2011) computed 
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1 population-weighted scaled semivariances and Pearson correlation coefficients for daily 

2 concentration metrics of twelve pollutants measured at multiple central-site monitors in 

3 Atlanta. 8-h daily max O3 exhibited the lowest semivariance and highest correlation of 

4 any of the pollutants. Although this indicates some degree of urban-scale homogeneity 

5 for O3, the analysis did not account for near-road effects on O3 concentrations. 

6 Studies evaluating the influence of monitor selection on epidemiologic study results have 

7 found that O3 effect estimates are similar across different spatial averaging scales and 

8 monitoring sites. A study in Italy compared approaches for using fixed-site monitoring 

9 data in a case-crossover epidemiologic study of daily O3 and mortality (Zauli Sajani et 

10 al., 2011). O3 effect estimates were found to be similar whether the nearest monitor was 

11 used, or whether single-city, three-city, or six-city regional averages were used for 

12 exposure assessment. In contrast, effect estimates for PM10 and NO2 increased with 

13 increasing scale of spatial averaging. Confidence intervals increased with increasing 

14 spatial scale for all pollutants. The authors attributed the consistency of O3 effect 

15 estimates to the relative spatial homogeneity of O3 over multi-km spatial scales, and 

16 pointed to the high (0.85-0.95) inter-monitor correlations to support this. The use of 

17 background monitors rather than monitors influenced by local sources in this study 

18 suggests that local-scale spatial variation in O3, such as that due to titration by traffic 

19 emissions, was not captured in the analyses. A multi-city U.S. study of asthmatic children 

20 found comparable respiratory effect estimates when restricting the analysis to the 

21 monitors closest to the child’s zip code centroid as when using the average of all 

22 monitors in the urban area (Mortimer et al., 2002), suggesting little impact of monitor 

23 selection. Sarnat et al. (2010) studied the spatial variability of O3, along with PM2.5, NO2, 

24 and CO, in the Atlanta, GA, metropolitan area and evaluated how spatial variability 

25 affects interpretation of epidemiologic results, using time-series data for circulatory 

26 disease ED visits. The authors found that associations with ambient 8-h daily maximum 

27 O3 concentration were similar among all sites tested, including multiple urban sites and a 

28 rural site some 38 miles from the city center. This result was also observed for 24-h PM2.5

29 concentrations. In contrast, hourly CO and NO2 showed different associations for the 

30 rural site than the urban sites, although the urban site associations were similar to one 

31 another for CO. This suggests that the choice of monitor may have little impact on the 

32 results of O3 time-series studies, consistent with the moderate to high inter-monitor 

33 correlations observed in Atlanta (Chapter 3).

34 One potential explanation for this finding from the study by Sarnat et al. (2010) is that 

35 although spatial variability at different scales contributes to a complicated pattern of 

36 variations in the magnitude of O3 concentrations between near-road, urban core, and 

37 urban downwind sites, day-to-day fluctuations in concentrations may be reflected across 

38 multiple urban microenvironments. In addition, time-averaging of O3 and PM2.5
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1 concentrations may smooth out some of the intra-day spatial variability observed with the 

2 hourly CO and NO2 concentrations. However, some uncertainty in observed effect 

3 estimates due to spatial variability and associated exposure error is expected to remain, 

4 including a potential bias towards the null. 

4.6.2.2 Seasonality

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

6 vary by season, with at least three factors potentially contributing to this variation: 

7 differences in building ventilation (e.g., air conditioning or heater use versus open 

8 window ventilation), higher O3 concentrations during the O3 season contributing to 

9 increased exposure and improved detection by personal monitors; and changes in activity 

10 pattern resulting in more time spent outside. Evidence has been presented in studies 

11 conducted in several cities regarding the effect of ventilation on personal-ambient and 

12 indoor-outdoor O3 relationships (see Section 4.3.2 and Section 4.3.3). More limited 

13 evidence is available regarding the specific effects of O3 detection limits and activity 

14 pattern changes on O3 relationships.

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

16 exposure and ambient concentration (Sarnat et al., 2005; Sarnat et al., 2000) or between 

17 indoor and outdoor O3 concentrations (Geyh et al., 2000; Avol et al., 1998a). However, 

18 others have found higher ratios in fall than in summer (Sarnat et al., 2006a) or equivalent, 

19 near-zero ratios in winter and summer (Sarnat et al., 2001), possibly because summertime 

20 use of air conditioners decreases building air exchange rates. It should be noted that O3

21 concentrations during winter are generally much lower than summertime concentrations, 

22 possibly obscuring wintertime relationships due to detection limit issues. Studies 

23 specifically evaluating the effect of ventilation conditions on O3 relationships have found 

24 increased correlations or ratios for individuals or buildings experiencing higher air 

25 exchange rates (Sarnat et al., 2006a; Geyh et al., 2000; Sarnat et al., 2000; Romieu et al., 

26 1998a). 

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

28 between indoor and outdoor concentration, are likely to reduce error in exposure 

29 estimates used in epidemiologic studies. This suggests that studies conducted during the 

30 O3 season or in periods when communities are likely to have high air exchange rates 

31 (e.g., during mild weather) may be less prone to exposure error than studies conducted 

32 only during winter. Year-round studies that include both the O3 and non-O3 seasons may 

33 have an intermediate level of exposure error.
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4.6.3 Exposure Duration

1 Epidemiologic studies of health effects associated with short-term and long-term 

2 exposures use different air pollution metrics and thus have different sources of exposure 

3 error. The following subsections discuss the impact of using different short-term and 

4 long-term exposure metrics on epidemiologic results.

4.6.3.1 Short-Term Exposure

5 The averaging time of the daily exposure metrics used to evaluate daily aggregated health 

6 data (e.g., 1-h or 8-h daily maximum vs. 24-h avg concentration) may also impact 

7 epidemiologic results, since different studies report different daily metrics. Correlations 

8 between 1-h daily max, 8-h daily max, and 24-h avg concentrations for U.S. monitoring 

9 sites are presented in Section 3.6.1 (Figure 3-23 and accompanying text). The two daily 

10 peak values (1-h max and 8-h max) are well correlated, with a median (IQR) correlation 

11 of 0.97 (0.96-0.98). The correlation between the 8-h max and 24-h avg are somewhat less 

12 well correlated with a median (IQR) correlation of 0.89 (0.86-0.92). While this may 

13 complicate quantitative comparisons between epidemiologic studies using different daily 

14 metrics, as well as the interpretation of studies using metrics other than the current 8-h 

15 standard, the high inter-metric correlations suggest it is a relatively small source of 

16 uncertainty in comparing the results of studies using different metrics. This is supported 

17 by a study comparing each of these metrics in a time-series study of respiratory ED visits 

18 (Darrow et al., 2011a), which found positive associations for all metrics, with the 

19 strongest association for the 8-h daily max exposure metric (Section 6.2.7.3).

20 The ratios of 1-h daily max, 8-h daily max, and 24-h avg concentrations to one another 

21 have been found to differ across communities and across time within individual 

22 communities (Anderson and Bell, 2010). For example, 8:24 hour ratios ranged from 

23 1.23-1.83, with a median of 1.53. Lower ratios were generally observed in the spring and 

24 summer compared to fall and winter. O3 concentration was identified as the most 

25 important predictor of ozone metric ratios, with higher overall O3 concentrations 

26 associated with lower ratios. In communities with higher long-term ozone concentrations, 

27 the lower 8:24 hour ratio is attributed to high baseline O3, which results in elevated 24-h 

28 average values. Differences in the representativeness of O3 metrics introduces uncertainty 

29 into the interpretation of epidemiologic results and complicates comparison of studies 

30 using different metrics. Preferably, studies will report results using multiple metrics. In 

31 cases where this does not occur, the results of the study by Anderson and Bell (2010) can 

32 inform the uncertainty associated with using a standard increment to adjust effect 

33 estimates based on different metrics so that they are comparable (Chapter 6).
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1 A study compared measures of spatial and temporal variability for 1-h daily max and 

2 24-h daily avg O3 concentrations in Brazil (Bravo and Bell, 2011). The 1-h daily max 

3 value was found to have higher correlation between monitors (i.e., lower temporal 

4 variability) and lower COD (a measure of spatiotemporal variability which incorporates 

5 differences in concentration magnitude, with lower values indicating lower variability; 

6 see Chapter 3) than the 24-h avg value. The range of correlation coefficients and COD 

7 values was similar between the two metrics, although the variation was lower for the 1-h 

daily max, as indicated by the R2 8 value for the regression of correlation coefficient on 

9 inter-monitor distance.

4.6.3.2 Long-Term Exposure

10 Long-term O3 exposure studies are not available that permit evaluation of the relationship 

11 between long-term O3 concentrations and personal or population exposure. The value of 

12 short-term exposure data for evaluating long-term concentration-exposure relationships is 

13 uncertain. If the longer averaging time (annual vs. daily or hourly) smooths out short14 term fluctuations, long-term concentrations may be well-correlated with long-term 

15 exposures. However, lower correlation between long-term exposures and ambient 

16 concentration could occur if important exposure determinants change over a period of 

17 several years, including activity pattern and residential air exchange rate.

18 A study in Canada suggests that an exposure metric based on a single year can represent 

19 exposure over a multi-decade period. The authors compared exposure assessment 

20 methods for long-term O3 exposure and found that the annual average concentration in 

21 the census tract of a subject’s residence during 1980 and 1994 was well-correlated (0.76 

22 and 0.82, respectively) with a concentration metric accounting for movement among 

23 census subdivisions during 1980-2002 (Guay et al., 2011). This may have been due in 

24 part to a relatively low rate of movement, with subjects residing on average for 71% of 

25 the 22-year period in the same census subdivision they were in during 1980. 

26 Analysis of the exposure assessment methodology in a recent study of mortality 

27 associated with long-term O3 exposure (Jerrett et al., 2009) is illustrative. In this study, 

28 the authors computed quarterly averages of the daily 1-h max O3 concentration, averaged 

29 the two summer quarters together to produce an annual value, then calculated a 23-year 

30 average value for each city in the study. Producing a single value for each city enables a 

31 comparison of relatively cleaner cities with relatively more polluted cities. In this case, 

32 the average was calculated using the 1-h daily max value; if the 24-h avg value had been 

33 used, concentrations would have been lower and potentially more variable, based on 

34 analyses in Chapter 3. According to 
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1 Table 3-7, the 2007-2009, 3-year average 1-h daily max value during the warm season 

2 was approximately 50% higher than the corresponding 24-avg value on a nationwide 

3 basis. Correlation between the two metrics varies by site, indicating the differential 

4 influence of the overnight period on 24-h avg concentrations. The median correlation 

5 between 1-h daily max and 24-h avg is 0.83, with an IQR of 0.78-0.88. It is not clear, 

6 however, that a different exposure assignment method would yield different results.

7 Long-term O3 trends, as discussed in Chapter 3, show gradually decreasing 

8 concentrations. Figure 3-48 shows that concentrations have decreased most for the 90th 

9 percentile, with relatively little change among the 10th percentile monitors. The decrease 

10 has been greater in the eastern U.S. than in the western part of the country (excluding 

11 California). For the most part, the rank order of regions in terms of O3 concentration has 

12 remained the same, as shown in Figure 3-50, with the Northeast, Southeast, and 

13 California exhibiting the highest concentrations. The decreasing trend is consistent across 

14 nearly all monitors in the U.S., with only 1-2% of monitors reporting an increase of more 

15 than 5 ppb between the 2001-2003 and 2008-2010 time periods (Figure 3-52 and 

16 Figure 3-53). This provides some evidence that epidemiologic studies of long-term 

17 exposure are not affected by drastic changes in O3 concentration, such as a relatively 

18 clean city becoming highly polluted or the reverse. 

19 A few epidemiologic studies have evaluated the impact of distance to monitor on 

20 associations between long-term O3 concentration and reproductive outcomes, as 

21 discussed in Chapter 7. It is not clear from this evidence whether using a local monitor 

22 for these multi-month concentration averages improves exposure assessment. Jalaludin et 

23 al. (2007) found somewhat higher effect estimates for women living within 5 km of a 

24 fixed-site O3 monitor than for all women in the Sydney metropolitan area, suggesting that 

25 increased monitor proximity reduced exposure misclassification. In contrast, Darrow et 

26 al. (2011b) found no substantial difference between effect estimates for those living 

27 within 4 mi of a fixed-site monitor and those living in the five-county area around 

28 Atlanta. This result could be due to spatial variability over smaller scales than the 4-mi 

29 radius evaluated, time spent away from the residence impacting O3 exposure, or 

30 similarity in monitor location and representativeness across the urban area (see 

31 Figure 4-4). At this time, the effect of exposure error on long-term exposure 

32 epidemiologic studies is unclear.

4.6.4 Exposure to Copollutants and Ozone Reaction Products

33 Although indoor O3 concentrations are usually well below ambient concentrations, the 

34 same reactions that reduce O3 indoors form particulate and gaseous species, including 
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1 other oxidants, as summarized in Section 4.3.4.3. Exposures to these reaction products 

2 would therefore be expected to be correlated with ambient O3 concentrations, although no 

3 evidence was identified regarding personal exposures. Such exposure could potentially 

4 contribute to health effects observed in epidemiologic studies.

4.6.5 Averting Behavior

5 As described in Section 4.4.2, several recent studies indicate that some populations alter 

6 their behavior on high ozone days to avoid exposure. Such behavioral responses to 

7 information about forecasted air quality may introduce systematic measurement error in 

8 air pollution exposure, leading to biased estimates of the impact of air pollution on health. 

9 For example, studies have hypothesized that variation in time spent outdoors may be a 

10 driving factor behind the considerable heterogeneity in ozone mortality impacts across 

11 communities (Bell et al., 2004). If averting behavior reduces outdoor O3 exposure, then 

12 studies that do not account for averting behavior may produce effect estimates that are 

13 biased towards the null (Section 6.2.7.2).

14 This is supported by an epidemiologic study that examined the association between 

15 exposure to ambient ozone concentrations and asthma hospitalizations in Southern 

16 California during 1989-1997, which indicates that controlling for avoidance behavior 

17 increases the effect estimate for both children and older adults, but not for adults aged 

18 20-64 (Neidell and Kinney, 2010; Neidell, 2009). Figure 4-7 and Figure 4-8, reproduced 

19 from Neidell (2009), show covariate-adjusted asthma hospital admissions as a function of 

20 daily maximum 1-h O3 concentration for all days (gray line) and days when no O3 alert 

21 was issued (black line). Stage 1 smog alerts were issued by the State of California for 

22 days when ambient O3 concentrations were forecast to be above 0.20 ppm; however, the 

23 concentration-response functions are based on measured O3 concentrations. For children 

24 aged 5-19 (Figure 4-7), hospital admissions were higher on high-O3 days when no alert 

25 was issued, especially on days with O3 concentrations above 0.15 ppm (150 ppb). The 

26 concentration-response curves for all days and days with no alert diverge at measured O3

27 concentrations between 0.10 and 0.15 ppm because smog alerts begin to be issued more 

28 frequently in this range. This suggests that in the absence of information that would 

29 enable averting behavior, children experience higher ozone exposure and subsequently a 

30 greater number of asthma hospital admissions than on alert days with similar O3

31 concentrations. The lower rate of admissions observed when alert days were included in 

32 the analysis suggests that averting behavior reduced O3 exposure and asthma hospital 

33 admissions. In both cases, O3 was found to be associated with asthma hospital 

34 admissions, although the strength of the association is underestimated when not 

35 accounting for averting behavior. A different result was observed when examining 
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1 associations for adults aged 20-64 (Figure 4-8), who had similar rates of hospital 

2 admissions on non-alert days as on all days. The lack of change for adults aged 20-64, 

3 which is primary employment age, may reflect lower response to air quality alerts due to 

4 the increased opportunity cost of behavior change. The finding that air quality 

5 information reduces the daily asthma hospitalization rate in these populations provides 

6 additional support for a link between ozone and health effects.

Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Press 

(Neidell, 2009).

Figure 4-7 Adjusted asthma hospital admissions by age on lagged ozone by 

alert status, ages 5-19.
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Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Press; 

Neidell (2009).

Figure 4-8 Adjusted asthma hospital admissions by age on lagged ozone by 

alert status, ages 20-64.

4.6.6 Exposure Estimation Methods in Epidemiologic Studies

1 Epidemiologic studies use a variety of methods to assign exposure. Study design, data 

2 availability, and research objectives are all important factors for epidemiologists when 

3 selecting an exposure assessment method. Common methods for assigning exposure 

4 using monitoring data include using a single fixed-site monitor to represent population 

5 exposure, averaging concentrations from multiple monitors, and selecting the closest 

6 monitor. Investigators may also use statistical adjustment methods, such as trimming 

7 extreme values, to prepare the concentration data set. Panel or small-scale cohort studies 

8 involving dozens of individuals may use more individualized concentration 

9 measurements, such as personal exposures, residential indoor or outdoor measurements, 

10 or concentration data from local study-specific monitors. For long-term epidemiologic 

11 studies, the lack of personal exposure data or dedicated measurements means that 

12 investigators must rely on fixed-site monitoring data. These data may be used directly, 

13 averaged across counties or other geographic areas, or used to construct geospatial or 

14 regression models to assign concentrations to unmonitored locations. Longer-term 

15 averages (months to years) are typically used (e.g., in studies discussed in 

16 Section 7.3.1.1). Chapters 6 and 7 describe the exposure assessment methods used in the 
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1 epidemiologic studies described therein, providing additional detail on studies with 

2 innovative or expanded techniques designed to improve exposure assessment and reduce 

3 exposure error.

4 The use of O3 measurements from central ambient monitoring sites is the most common 

5 method for assigning exposure in epidemiologic studies. However, fixed-site 

6 measurements do not account for the effects of spatial variation in O3 concentration, 

7 ambient and non-ambient concentration differences, and varying activity patterns on 

8 personal exposures (Brown et al., 2009; Chang et al., 2000; Zeger et al., 2000). The use 

9 of fixed-site concentrations results in minimal exposure error when: (1) O3 concentrations 

10 are uniform across the region; (2) personal activity patterns are similar across the 

11 population; and (3) housing characteristics, such as air exchange rate and indoor reaction 

12 rate, are constant over the study area. Since these factors vary by location and population, 

13 there will be errors in the magnitude of total exposure based solely on ambient 

14 monitoring data.

15 Modeled concentrations can also be used as exposure surrogates in epidemiologic studies, 

16 as discussed in Section 4.5. Geostatistical spatial interpolation techniques, such as IDW 

17 and kriging, can provide finer-scale estimates of local concentration over urban areas. A 

18 microenvironmental modeling approach simulates exposure using empirical distributions 

19 of concentrations in specific microenvironments together with human activity pattern 

20 data. The main advantage of the modeling approach is that it can be used to estimate 

21 exposures over a wide range of population and scenarios. A main disadvantage of the 

22 modeling approach is that the results of modeling exposure assessment must be compared 

23 to an independent set of measured exposure levels (Klepeis, 1999). In addition, 

24 resource-intensive development of validated and representative model inputs is required, 

25 such as human activity patterns, distributions of air exchange rate, and deposition rate. 

26 Therefore, modeled exposures are used much less frequently in epidemiologic studies.

4.7 Summary and Conclusions

27 This section will briefly summarize and synthesize the main points of the chapter, with 

28 particular attention to the relevance of the material for the interpretation of epidemiologic 

29 studies. 

30 Passive badge samplers are the most widely used technique for measuring personal O3

31 exposure (Section 4.3.1). The detection limit of the badges for a 24-h sampling period is 

32 approximately 5-10 ppb, with lower detection limits at longer sampling durations. In low33 concentration conditions this may result in an appreciable fraction of 24-h samples being 

34 below the detection limit. The use of more sensitive portable active monitors, including 
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1 some that have recently become available, may help overcome this issue and improve 

2 personal monitoring in the future.

3 Since there are relatively few indoor sources of O3, indoor O3 concentrations are often 

4 substantially lower than outdoor concentrations due to reactions of O3 with indoor 

5 surfaces and airborne constituents (Section 4.3.2). Air exchange rate is a key determinant 

6 of the I/O ratio, which is generally in the range of 0.1-0.4 (Table 4-1), with some 

7 evidence for higher ratios during the O3 season when concentrations are higher.

8 Personal exposure is moderately correlated with ambient O3 concentration, as indicated 

9 by studies reporting correlations generally in the range of 0.3-0.8 (Table 4-2). Hourly 

10 concentration correlations are more variable than those averaged over 24 hours or longer, 

11 with correlations in outdoor microenvironments (0.7-0.9) much higher than those in 

12 residential indoor (0.1) or other indoor (0.3-0.4) microenvironments. Some studies report 

13 substantially lower personal-ambient correlations, a result attributable in part to low air 

14 exchange rate and O3 concentrations below the sampler detection limit, conditions often 

15 encountered during wintertime. Low correlations may also occur for individuals or 

16 populations spending substantial time indoors. 

17 The ratio between personal exposure and ambient concentration varies widely depending 

18 on activity patterns, housing characteristics, and season, with higher personal-ambient

19 ratios generally observed with increasing time spent outside, higher air exchange rate, 

20 and in seasons other than winter (Table 4-3). Personal-ambient ratios are typically 

21 0.1-0.3, although individuals spending substantial time outdoors (e.g., outdoor workers) 

22 may have much higher ratios (0.5-0.9). 

23 Personal exposure to other pollutants shows variable association with personal exposure 

24 to O3, with differences in copollutant relationships depending on factors such as season, 

25 city-specific characteristics, activity pattern, and spatial variability of the copollutant 

26 (Section 4.3.4). In near-road and on-road microenvironments, correlations between O3

27 and traffic-related pollutants are moderately to strongly negative, with the most strongly 

28 negative correlations observed for NO2 (-0.8 to -0.9). This is consistent with the 

29 chemistry of NO oxidation, in which O3 is consumed to form NO2. The more moderate 

30 negative correlations observed for PM2.5, PM1.0, and VOC may reflect reduced 

31 concentrations of O3 in polluted environments due to other scavenging reactions. A 

32 similar process occurs indoors, where infiltrated O3 reacts with airborne or surface33 associated materials to form secondary compounds, such as formaldehyde. Although such 

34 reactions decrease indoor O3 exposure, they result in increasing exposure to other species 

35 which may themselves have health effects.
Draft – Do Not Cite or Quote 4-56 June 2012

1 Variations in ambient O3 concentrations occur over multiple spatial and temporal scales. 

2 Near roadways, O3 concentrations are reduced due to reaction with NO and other species 

3 (Section 4.3.4.2). Over spatial scales of a few kilometers and away from roads, O3 may 

4 be somewhat more homogeneous due to its formation as a secondary pollutant, while 

5 over scales of tens of kilometers, additional atmospheric processing can result in higher 

6 concentrations downwind of an urban area. Although local-scale variability impacts the 

7 magnitude of O3 concentrations, O3 formation rates are influenced by factors that vary 

8 over larger spatial scales, such as temperature (Section 3.2), suggesting that urban 

9 monitors may track one another temporally but miss small-scale variability in magnitude. 

10 The resulting uncertainty in exposure contributes to exposure measurement error in 

11 epidemiologic studies. 

12 Another factor that may influence epidemiologic results is the tendency for people to 

13 avoid O3 exposure by altering their behavior (e.g., reducing time spent outdoors) on high14 O3 days. Activity pattern has a substantial effect on ambient O3 exposure, with time spent 

15 outdoors contributing to increased exposure (Section 4.4.2). Averting behavior has been 

16 predominantly observed among children, older adults, and people with respiratory 

17 problems. Such effects are less pronounced in the general population, possibly due to the 

18 opportunity cost of behavior modification. Evidence from one recent epidemiologic study 

19 indicates increased asthma hospital admissions among children and older adults when O3

20 alert days were excluded from the analysis (presumably thereby eliminating averting 

21 behavior based on high O3 forecasts). The lower rate of admissions observed when alert 

22 days were included in the analysis suggests that estimates of health effects based on 

23 concentration-response functions which do not account for averting behavior may be 

24 biased towards the null.

25 The range of personal-ambient correlations reported by most studies (0.3-0.8) is similar 

26 to that for NO2 (U.S. EPA, 2008b) and somewhat lower than that for PM2.5 (U.S. EPA, 

27 2009d). To the extent that relative changes in central-site monitor concentration are 

28 associated with relative changes in exposure concentration, this indicates that ambient 

29 monitor concentrations are representative of day-to-day changes in average total personal 

30 exposure and in personal exposure to ambient O3. The lack of indoor sources of O3, in 

31 contrast to NO2 and PM2.5, is partly responsible for low indoor-outdoor ratios (generally 

32 0.1-0.4) and low personal-ambient ratios (generally 0.1-0.3), although it contributes to 

33 increased personal-ambient correlations. The lack of indoor sources also suggests that 

34 fluctuations in ambient O3 may be primarily responsible for changes in personal 

35 exposure, even under low-ventilation, low-concentration conditions. Nevertheless, low 

36 personal-ambient correlations are a source of exposure error for epidemiologic studies, 

37 tending to obscure the presence of potential thresholds, bias effect estimates toward the 

38 null, and widen confidence intervals, and this impact may be more pronounced among 
Draft – Do Not Cite or Quote 4-57 June 2012

1 populations spending substantial time indoors. The impact of this exposure error may 

2 tend more toward widening confidence intervals than biasing effect estimates, since 

3 epidemiologic studies evaluating the influence of monitor selection indicate that effect 

4 estimates are similar across different spatial averaging scales and monitoring sites. 
Draft – Do Not Cite or Quote 4-58 June 2012

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