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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 in the context of existing relevant 

7 information summarized in the 2006 O3 AQCD, which in many areas remains definitive. 

8 A brief summary of findings from the 2006 O3 AQCD is presented at the beginning of 

9 each section as appropriate.

10 Section 4.2 presents general exposure concepts describing the relationship between 

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

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

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

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

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

16 modeling local O3 concentrations, air exchange rates, microenvironmental 

17 concentrations, and personal and population exposure. Section 4.6 discusses the 

18 implications of using ambient O3 concentrations to estimate exposure in epidemiologic 

19 studies, including several factors that contribute to exposure error.

4.2 General Exposure Concepts

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

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

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

23 activities throughout a given time period: 

Equation 4-1

24 where ET = total (T) exposure over a time-period of interest, Cj = airborne O3

25 concentration at microenvironment j, and dt = portion of the time-period spent in 
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1 microenvironment j. Equation 4-1 can be decomposed into a model that accounts for 

2 exposure to O3, of 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 

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

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

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

9 parameters 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 

microenvironment (i). Equation 4-3 is subject to the constraint Σfo + Σf 18 i = 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 (movies, restaurants, bars). “Outdoor” exposure can occur in parks or yards, on 

sidewalks, and on bicycles or motorcycles. Finf 24 is a function of the building air exchange 

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

26 function of the penetration (P) of O3 into the microenvironment, the air exchange rate (a) 

27 of the microenvironment, and the rate of O3 loss (k) in the microenvironment; Finf = 

28 Pa/(a+k).

29 In epidemiologic studies, the central-site ambient concentration, Ca, is often used in lieu 

30 of outdoor microenvironmental data to represent these exposures based on the availability 
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1 of data. Thus it is often assumed that Co = Ca and that the fraction of time spent outdoors 

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

3 infiltration differs among different microenvironments. If an epidemiologic study 

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

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

Equation 4-4

6 The spatial variability of outdoor O3 concentrations due to meteorology, varying 

7 precursor emissions and O3 formation rates; design of the epidemiologic study; and other 

8 factors determine whether or not Equation 4-4 is a reasonable approximation for 

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

10 Equation 4-3 are described in Section 4.6 with respect to implications for interpreting 

11 epidemiologic studies. Epidemiologic studies often use concentration measured at a 

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

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

Equation 4-5

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

Equation 4-6

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

16 microenvironment, the ambient component of a microenvironmental O3 concentration 

can be represented as the product of the ambient concentration and Finf 17 . Wallace et al. 

18 (2006) note that time-activity data and corresponding estimates of Finf for each 

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

20 epidemiologic studies, α is assumed to be constant in lieu of time-activity data and 

estimates of Finf 21 , which can vary with building and meteorology-related air exchange 

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

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

24 concentration may be estimated using dispersion models, land use regression models, 

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

26 techniques are described in Section 4.5.
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4.3 Exposure Measurement

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

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

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

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

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

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

7 study results.

4.3.1 Personal Monitoring Techniques

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

23 multiple microenvironments with relatively low participant burden.

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

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

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

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

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

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

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

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

32 humidity changes in different microenvironments (Wilson and Birks, 2006). Its size and 
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1 weight (approximately 10×20×30 cm; 2 kg) make it suitable for use in a backpack 

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

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

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

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

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

4.3.2 Indoor-Outdoor Concentration Relationships

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

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

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

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

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

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

13 indoor surfaces where reactions produce secondary pollutants such as formaldehyde 

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

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

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

17 Table 4-1 summarizes indoor-outdoor (I/O) ratios and correlations reported by older and 

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

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

20 during the O3 season when concentrations are higher.

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

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

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

24 O3 concentrations are low. Further improvements in characterization of 

25 microenvironmental O3 concentrations and I/O ratios will rely on improved monitoring. 

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

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

28 studies in evaluating the relationship between indoor and outdoor O3 concentrations.
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Table 4-1 Relationships of Indoor and Outdoor Ozone Concentration 

Study Location Years/Season Population Sample 

duration Ratioa Correlation Micro- environment Others

Geyh et al. 

(2000)

Upland, Southern 

California

June - September 

1995 and May 1996

Children 6 day 0.24 NR Home Air-conditioned

October 1995-April 

1996

0.15

Mountain 

Communities, 

Southern California

June - September 

1995 and May 1996

0.36 Opening 

windows

October 1995-April 

1996

0.08

Avol et al. 

(1998b)

Southern California FebruaryDecember, 1994NR 24 h 0.37

SD: 0.25

0.58 Home

Summer 0.43

SD: 0.29

NR

Non-summer 0.32

SD: 0.21

NR

Romieu et 

al. (1998b)

Mexico City, Mexico September 1993 -

July 1994

Children 7 or 14 day 0.20

0.15b

Range: 

0.01-

1.00

NR Home

Lee et al. 

(2004a)

Nashville, TN Summer 1994 Children 1 week 0.1 NR Home

Héroux et 

al. (2010)

Regina, 

Saskatchewan, 

Canada

Summer 2007 All age groups 5 day 0.13 NR Home

López- Aparicio et 

al. (2011)

Prague, Czech 

Republic

July 2009 – March 

2010

NR 1 month 0.10- 0.30NR Home No heating or 

air conditioning

Liu et al. 

(1995)

Toronto, Canada Winter, 1992 All age groups 1 week 0.07

SD: 0.10

NR Home

Summer, 1992 0.40

SD: 0.29

Summer, 1992 12 h 0.30

SD: 0.32

Daytime

Summer, 1992 0.43

SD: 0.54

Nighttime

Romieu et 

al. (1998b)

Mexico City, Mexico September 1993 - July 1994Children 24 h/day, 

14 days

0.15 NR School

Children (during 

school hours)

5 h/day, 10 

days

0.30-

0.40

Immediately 

outside the 

schools

Blondeau et 

al. (2005)

La Rochelle, France Spring, 2000 Children NR Range: 

0.00-

0.45

NR School

Riediker et 

al. (2003)

North Carolina August - October 

2001

Adults 9 h 0.51 NR Vehicle

a Mean value unless otherwise indicated b Median

NR = not reported 

SD = standard deviation

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 

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

8 (11.8 versus 48.2 ppb), while in the mountain communities, the indoor concentration was 

9 36% of the outdoor concentration (21.4 versus 60.1 ppb). This is consistent with the 

10 increased air exchange rate expected in homes using window ventilation. In the non11 ozone season, when homes are likely to be more tightly closed to conserve heat, the ratios 
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1 of indoor to outdoor concentration were 0.15 (3.2 versus 21.1 ppb) and 0.08 (2.8 versus 

2 35.7 ppb) in Upland and the mountain communities, respectively. Avol et al. (1998b)

3 observed a mean I/O ratio of 0.37 for 239 matched 24-h samples collected between 

4 February and December at homes in the Los Angeles area. The I/O ratio during summer 

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

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

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

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

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

10 1995), which found summer I/O ratios of 0.30-0.43, in comparison with a winter I/O ratio 

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

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

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

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

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

16 2004a).

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

18 including schools and vehicles. Romieu et al. (1998b) reported that O3 concentrations 

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

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

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

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

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

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

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

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

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

28 of one-month indoor and outdoor concentrations ranging from 0.10-0.30 during a nine29 month sampling campaign, with the highest ratios reported in Nov-Dec 2009 and the 

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

were relatively constant (approximately 3-7 ug/m3 31 or 2-3 ppb), while outdoor 

concentrations were lower in the winter (9-10 ug/ m3 32 or about 5 ppb) than in the summer 

(35-45 ug/ m3 33 or about 20 ppb). This seasonal variation in outdoor concentrations 

34 coupled with homogeneous indoor concentrations, together with increased wintertime air 

35 exchange rate due to higher indoor-outdoor temperature differences, is likely responsible 

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

37 Exposures in near-road, on-road and in-vehicle microenvironments are likely to be highly 

38 variable and lower than those in other microenvironments due to reaction of O3 with NO 
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1 and other combustion emissions. Depending on wind direction, O3 concentrations near 

2 the roadway have been found to be 20-80% of ambient concentrations at sites 400 m or 

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

4 shifts reported in-vehicle 9-h concentrations that were approximately 51% of 

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

6 (Riediker et al., 2003).

4.3.3 Personal-Ambient Concentration Relationships

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

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

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

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

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

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

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

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

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

16 scales, with a generally increasing trend during the daytime hours, and higher O3

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

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

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

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

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

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

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

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

25 personal exposure and ambient concentration.

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

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

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

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

30 season. Low O3 concentrations indoors and during the winter lead to a high proportion of 

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

32 low correlations observed in some studies under those conditions. Ratios of personal 

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

34 magnitude (typically 0.1-0.3), and are also variable, with increasing time spent outdoors 
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1 associated with higher ratios. The next two subsections describe studies that have 

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

3 populations.

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

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

Figure 4-1 Variation in hourly personal and ambient concentrations of O3 and 

PM2.5 in various microenvironments during daytime hours. 

4 O3 concentrations near and below the passive sampler detection limit lead to uncertainty 

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

6 values below the detection limit because noise obscures the underlying signal in the data, 

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

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

9 problem is particularly acute in the non-ozone season when ambient O3 concentrations 

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

11 ambient concentration will depend on improved monitoring techniques to accurately 

12 capture low O3 concentrations, preferably at high time resolution to facilitate evaluation 

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

14 available, past studies summarized in the 2006 O3 AQCD remain relevant to consider 
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1 along with more recent studies in evaluating personal-ambient concentration 

2 relationships.

3 Personal-Ambient Correlations. Correlations between personal exposure and ambient 

4 O3 concentrations have been evaluated in several research studies, many of which were 

5 conducted prior to 2005 and are discussed in the 2006 O3 AQCD. Some studies evaluated 

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

7 for a single individual. These studies indicate the inter-individual variability of personal8 ambient correlations. Another type of correlation is a pooled correlation, which combines 

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

10 overall indicator of the personal-ambient relationship for all study subjects. A third type 

11 of correlation is a community-average correlation, which correlates average exposure 

12 across all study subjects with fixed-site monitor concentrations. Community-average 

13 correlations are particularly informative for interpreting time-series epidemiologic 

14 studies, in which ambient concentrations are used as a surrogate for community-average 

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

16 improvement of future personal exposure studies. Table 4-2 summarizes studies reporting 

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

18 text. 

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

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

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

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

23 proportion of personal measurements below the detection limit. The effect of season is 

24 unclear, with mixed evidence on whether higher correlations are observed during the O3

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

26 microenvironments and found that the pooled correlation between personal exposure and 

27 ambient concentration was highest for outdoor microenvironments (r = 0.68-0.91). In28 vehicle microenvironments showed moderate to high correlations (0.57-0.72). 

29 Correlations in residential indoor microenvironments were very low (r = 0.05-0.09), with 

30 moderate correlations (0.34-0.46) in other indoor microenvironments such as restaurants 

31 and shopping malls. Liard et al. (1999) evaluated community-average correlations based 

32 on 4-day mean personal O3 exposure measurements for adults and children and found a 

33 relatively high correlation (r = 0.83) with ambient concentrations, even though 31-82% of 

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

35 population of older adults in Baltimore and found that longitudinal correlations between 

36 24-h personal exposure and ambient concentration varied by subject and season, with 

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

38 in winter (mean = 0.06). Some evidence was presented that subjects living in well-
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1 ventilated indoor environments have higher correlations than those living in poorly 

2 ventilated indoor environments, although exceptions to this were also observed. Ramirez3 Aguilar et al. (2008) measured 48- to 72-h personal exposures of four groups of asthmatic 

4 children aged 6-14 in Mexico City during 1998-2000. A moderate pooled correlation (r = 

5 0.35) was observed between these exposures and corresponding ambient concentrations.

Table 4-2 Correlations between Personal and Ambient Ozone Concentration 

Study Location Years/Season Population Sample 

duration Correlation Study Type Others

Chang et al. 

(2000)

Baltimore, MD Summer 1998 Older adults 1 h 0.91 Pooled Outdoor near 

roadway

Winter 1999 0.77

Summer 1998 0.68 Outdoor away 

from road

Winter 1999 0.86

Summer 1998 0.72 In vehicle

Winter 1999 0.57

Summer 1998 0.09 Indoors- residence

Winter 1999 0.05

Summer 1998 0.34 Indoors-other

Winter 1999 0.46

Liard et al. 

(1999)

Paris, France Summer 1996 All age groups 4 day 0.83 Community- averaged

Sarnat et al. 

(2000)

Baltimore, MD Summer Older adults 24 h 0.20 Longitudinal

Winter 0.06

Linn et al. 

(1996)

Southern 

California

All seasons from 

1992 to 1993

Children 24 h 0.61 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

NR = not reported

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

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

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

9 0.61 was reported between 24-h avg personal and central-site measurements by Linn et 

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

11 101-136 minutes per day outdoors. The authors also report a correlation of 0.70 between 

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

13 the average school outdoor concentration (34 ppb) was higher than the average central14 site concentration (23 ppb) and the average personal exposure concentration was lower 
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1 (5 ppb) than the central-site value, the similarity between the correlations indicate that 

2 central-site monitor concentrations can represent personal exposures in addition to 

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

4 illustration of the effect of outdoor microenvironments on personal-ambient relationships 

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

6 workers (0-25% of time outdoors), camp counselors (7.5-45% of time outdoors), and 

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

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

9 during their work shift (6-14 hours). In this study, the pooled correlations between 

10 personal exposure and fixed-site concentration not substantially different among the 

11 groups (r = 0.60, 0.42, and 0.64, respectively). The ratios of personal exposure to fixed12 site monitor concentration increased among the groups with increasing amount of time 

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

14 in personal exposure to O3 are driven by variations in ambient concentration, even for 

15 individuals that spend little time outdoors.

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

17 and ambient concentration varies widely, depending on activity patterns, housing 

18 characteristics, and season. Higher personal-ambient ratios are generally observed with 

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

20 Table 4-3 summarizes the results of several such studies discussed in the 2006 O3 AQCD

21 together with newer studies showing the same pattern of results.

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

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

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

25 by Sarnat et al. for 24-h data from mixed populations of children and older adults in 

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

27 Baltimore subjects showing a near-zero slope (0.01) during the summertime while Boston 

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

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

30 Baltimore may have been due to differences in time spent outdoors, residential 

31 ventilation conditions, or other factors. Xue et al. (2005) measured 6-day personal 

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

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

34 authors also regressed personal exposures on ambient concentration after adjusting for 

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

the regression R2 36 value of 0.58. Unadjusted regression slopes were not presented.
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Table 4-3 Ratios of Personal to Ambient Ozone Concentration 

Study Location Years/Season Population Sample 

duration Ratio Study Type Others

Sarnat et al. 

(2001)

Baltimore Summer 1998 Older adults, children, and 

individuals with COPD

24 h 0.01 Longitudinal

Winter 1999 0.00

Brauer and Brook

(1997)

Vancouver, 

Canada

Summers 1992 

and 1993

Health clinic workers 24 h 0.35 Pooled 0-25% of time 

outdoors

Camp counselors 0.53 Pooled 7.5-45% of time 

outdoors

Farm workers 0.96 Pooled 100% of time 

outdoors

O’Neill et al. 

(2003)

Mexico City, 

Mexico

April - July 1996 Shoe cleaners 6 h 0.56

95% CI: 

0.43-0.69

Longitudinal

Sarnat et al. 

(2005)

Boston Summer Older adults and children 24 h 0.27

95% CI: 

0.18-0.37

Longitudinal

Winter 0.04

95% CI: 

0.00-0.07

Xue et al. (2005) Southern 

California

June 1995 - May 

1996

Children 6 day 0.3 Longitudinal Ratio

0.54 Regression slope 

(R2 = 0.58)

Sarnat et al. 

(2006b)

Steubenville, 

OH

Summer Older adults 24 h 0.15

SE: 0.02

Longitudinal High-ventilation

0.08

SE: 0.04

Low-ventilation

Fall 0.27

SE: 0.03

High-ventilation

0.20

SE: 0.05

Low-ventilation

Ramírez-Aguilar 

et al. (2008)

Mexico City, 

Mexico

Dec.1998-Apr. 

2000

Asthmatic children 48 h to

72 h

0.17 Pooled

NR = not reported 

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 

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

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

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

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

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

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

10 0.20). Suh and Zanobetti (2010) reported an average 24-h personal exposure of 2.5 ppb as 

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

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

13 the mean 24-h personal exposure across 137 participants in summer and winter was 2.1 

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

15 al., 2009b). Although no personal exposures were measured, McConnell et al. (2006)

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

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

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

19 to the central-site average of 36 ppb. In Mexico City, Ramirez-Aguilar et al. (2008)
Draft – Do Not Cite or Quote 4-14 September 2011

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

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

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

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

4.3.4 Co-Exposure to Other Pollutants and Environmental Stressors

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

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

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

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

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

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

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

12 relationships for near-road O3 exposure.

4.3.4.1 Personal Exposure to Ozone and Co-pollutants

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

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

15 detection limit, season, city-specific characteristics, and spatial variability of the 

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

17 during spring and fall between 24-h avg O3 measurements and co-pollutants of 0.14, 

18 0.00, and -0.03 for PM2.5, EC, and NO2, respectively. Titration of O3 near roadways is 

19 likely to contribute to the low or slightly negative correlations with the traffic-related 

20 pollutants EC and NO2. The somewhat higher correlation with PM2.5 may reflect the 

21 influence of air exchange rate and time spent outdoors on co-exposures to ambient PM2.5

22 and O3. Overall, the copollutant correlations are quite small, which may be due to the 

23 very low personal exposures observed in this study (2-3 ppb), likely to be near or below 

24 the detection limit of the passive sampler over a 24-h period. Chang et al. (2000)

25 measured hourly personal exposures to PM2.5 and O3 in summer and winter in Baltimore, 

26 Maryland. Correlations between PM2.5 and O3 were 0.05 and -0.28 in summer and 

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

28 associated with personal PM2.5 exposures in either summer or winter. These non29 significant correlations may be attributed in part to the relatively low personal O3

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

31 exposure was below the calculated limit of detection.
Draft – Do Not Cite or Quote 4-15 September 2011

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

18 and building ventilation patterns, on relationships between O3 and co-pollutants.

4.3.4.2 Near-Road Exposure to Ozone and Co-pollutants

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

20 NO, NO2, NOX, PM2.5, PM1.0, and several VOCs (the BTEX compounds, MTBE, 

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

22 [AADT] >340,000) roadways in Toronto, Canada. Passive samplers were deployed for 

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

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

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

26 in Figure 4-2. Correlations were -0.77 to -0.85 for NO2, -0.48 to -0.62 for NO, and -0.55 

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

28 magnitude. PM2.5 and PM1.0 correlations were -0.35 to -0.78 and -0.34 to -0.58, 

29 respectively. At the monitoring site not influenced by small area sources, O3-VOC 

30 correlations ranged from -0.41 to -0.66. 

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

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

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

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

35 laboratory, although not at the roadside, and they all showed negative correlations with 
Draft – Do Not Cite or Quote 4-16 September 2011

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

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

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

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

5 in the range of the 1-week correlation.

Source data from: Beckerman et al. (2008)

Figure 4-2 Correlations between 1-week concentrations of O3 and 

copollutants measured near roadways.

4.3.4.3 Indoor Exposure to Ozone and Co-pollutants

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

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

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

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

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

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

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

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

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

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

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

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 4-17 September 2011

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

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

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

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

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

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

7 estimation of exposures to reaction products difficult.

4.4 Exposure-Related Metrics

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

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

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

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

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

13 distance play an important role as well. 

4.4.1 Activity Patterns

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

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

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

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

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

19 microenvironments.

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

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

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

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

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

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

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

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

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

29 among the entire population as well as the proportion of time spent in each microen30 vironment. For example, about 25% of the individuals reported being outdoors or in a 

31 vehicle between 2:00 and 3:00 pm, when daily O3 levels are peaking, although about half 
Draft – Do Not Cite or Quote 4-18 September 2011

1 of this time was spent in or near a vehicle, where O3 concentrations are likely to be lower 

2 than ambient concentrations. Different patterns would be anticipated when breaking 

3 down activity patterns only for subgroups such as children or the elderly. Population 

4 exposures can be estimated using O3 concentration data in each microenvironment.

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

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

7 age, gender, employment, and lifestyle-dependent factors. These differences may mani8 fest as higher mean exposures or more frequent high-exposure episodes some individuals. 

9 The extent to which longitudinal variability in individuals contributes to the population 

10 variability in activity and location can be quantified by the ratio of between-person 

11 variance to total variance in time spent in different locations and activities (the intraclass 

12 correlation coefficient, ICC). Xue et al. (2004) quantified ICC values in time-activity data 

13 collected by Harvard University for 160 children aged 7–12 years in Southern California 

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

15 that 15% of the variance in outdoor time was due to between-person differences. The ICC 

16 value might be different for other population 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. 
Draft – Do Not Cite or Quote 4-19 September 2011

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

2 majority of the most significant human activity databases into one comprehensive 

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

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

5 which were evaluated to obtain over 33,000 person-days of 24-h human activities in 

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

7 conducted by EPA and the California Air Resources Board, as well as real-time diary 

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

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

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

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

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

13 being subdivided into clock-hour durations to facilitate exposure modeling. CHAD also 

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

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

4.4.2 Ozone Averting Behavior

16 Individuals can reduce their exposure to O3 by altering their behaviors, such as reducing 

17 their time outdoors. To protect the public from O3-related health effects, EPA and 

18 organizations such as the American Lung Association recommend that people spend 

19 more time indoors and engage in less strenuous activities on days with relatively high O3

20 concentrations. To assist individuals concerned about O3 conditions, EPA developed the 

21 Air Quality Index (AQI). This index combines information about O3 (and other pollutant) 

22 concentrations to produce five categories of air-quality, ranging from good to very 

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

24 high-O3 months through local media outlets, using various versions of this air-quality 

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

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

27 advised to curtail children’s outdoor exertion to varying degrees depending on the 

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

29 medical conditions. 

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

31 several studies, especially for susceptible populations, such as children, older adults, and 

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

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

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

35 on a code red O3 day relative to a code green, yellow, or orange day; however, the 
Draft – Do Not Cite or Quote 4-20 September 2011

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

24 more than one-third of the advisories issued (McDermott et al., 2006). Wen et al. (2009)

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

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

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

28 (31% vs. 16%) or individual perception of air quality (26% vs. 12%). Respondents who 

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

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

31 and without asthma. In a study of randomly selected individuals in Houston, TX and 

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

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

34 air quality episodes. This fraction is appreciably lower than the fraction reported for 

35 people with asthma in the Wen et al. (2009) study, although it is similar to the fraction 

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

37 (2008) study reported that their behavioral changes were motivated by self-perception of 

38 poor air quality rather than an air quality advisory. It should be noted that the McDermott 
Draft – Do Not Cite or Quote 4-21 September 2011

1 et al. (2006), Wen et al. (2009), and Semenza et al. (2008) studies evaluated air quality in 

2 general and therefore are not necessarily specific to O3. 

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

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

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

6 2003). This finding is consistent with a survey for 1000 commuters in Denver, Colorado, 

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

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

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

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

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

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

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

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

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

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

17 attendance at two outdoor facilities (i.e. a zoo and an observatory) was reduced by 6-13% 

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

19 children and older adults (Neidell, 2010, 2009).

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

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

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

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

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

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

26 commuting behavior.

4.4.3 Population Proximity to Fixed-Site Ozone Monitors

27 The distribution of O3 monitors across urban areas varies between cities (Section 

28 3.6.2.1), and the population living near each monitor varies as well. Monitoring sites in 

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

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

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

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

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

34 transport of O3 or O3 precursors (Section 3.6.2.2). This may contribute to less diel 
Draft – Do Not Cite or Quote 4-22 September 2011

1 variability in O3 concentration than is observed in urban areas. It is not necessarily true 

2 that proximity to a monitor determines the degree to which that monitor represents an 

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

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

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

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

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

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

9 1 km of an O3 monitor, while nearly all of the population in most cities lives within 20 

10 km of a monitor. Many O3 monitors are sited at “neighborhood scale,” intended to 

11 represent an area of the city with dimensions in the 0.5-4 km range (Section 3.5.6.1). 

12 Looking at the results for a 5-km radius, generally 20-30% of the population lives within 

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

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

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

16 clear trend.

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

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

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

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

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

22 monitors closest to the city center (sites A and B on Figure 3-24) accounted for 16% and 

23 8% of the population, respectively. Site B has two listed monitoring objectives, highest 

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

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

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

27 exposure account for 2.9-17% of the population per monitor. 
Draft – Do Not Cite or Quote 4-23 September 2011

Table 4-4 Fraction of the 2009 population living within a specified distance of 

an ozone monitor in selected U.S. cities

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.
Draft – Do Not Cite or Quote 4-24 September 2011

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 4-5. The remainder of this section provides more detail on specific 

14 modeling approaches, as well as results of applying the models.

Table 4-5 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

Chemistry-transport 

(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 locationactivity 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)
Draft – Do Not Cite or Quote 4-25 September 2011

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. There are three main types of approaches: spatial 

8 interpolation of measured concentrations; statistical models using meteorological 

9 variables, pollutant concentrations, and other predictors to estimate concentrations at 

10 receptors in the domain; and rigorous first-principle models, such as chemistry-transport 

11 models or dispersion models incorporating O3 chemistry. Some researchers have 

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

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

14 longer-term averages. This discussion will focus on short-term concentrations estimated 

15 across urban areas. 

16 The 2006 O3 AQCD discussed concentration surface models, focusing on chemistry17 transport models as well as geospatial and spatiotemporal interpolation techniques (e.g., 

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

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

20 compared different approaches for the same urban area.

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

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

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

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

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

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

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

28 and median estimated annual- and monthly-average concentrations, although the 10th29 90th percentile range was smaller for IDW-50. This is consistent with the averaging of 

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

31 the two methods was 0.93 for monthly-average concentrations and 0.78 for annual32 average concentrations. Methods 2 and 3 were considered sub-optimal and were excluded 

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

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

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

36 assigned most locations a concentration near the regional average, except for locations 
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1 immediately adjacent to a monitoring site. CMAQ concentration estimates using a 4 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

25 associations between pollutants and covariates from those of correlated co-pollutants, 

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

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

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

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

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

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

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

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

34 variability. Although O3 is also a secondary pollutant, its reactivity and high small-scale 

35 spatial variability near high-traffic roadways indicates this conclusion may not apply for 

36 O3. 
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1 At a much larger spatial scale, EU-wide, Beelen et al. (2009) compared a LUR model for 

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

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

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

percentage of the variability (R2 5 = 0.62) than for other pollutants. However, at the urban 

6 scale (1-km resolution), only one variable was selected into the O3 LUR model (highdensity residential land use), and the R2 7 value was very low (0.06). Universal kriging was 

8 the best method for the large-scale composite EU concentration map, for O3 as well as 

for NO2 and PM10, with an R2 9 value for O3 of 0.70. The authors noted that these methods 

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

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

12 model performance at the urban scale.

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

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

15 residential O3 concentrations in southern California using estimates of residential NOX

16 calculated from traffic data with the CALINE4 line source dispersion model. The authors 

17 estimated that local traffic contributes 18% of NOX concentrations measured in the study 

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

19 model indicates that residential NOX reduces residential O3 concentrations by 0.51 ppb 

20 O3 per 1 ppb NOX, and that a 10th-90th percentile increase in local NOX results in a 

21 7.5 ppb decrease in local O3 concentrations. This intra-urban traffic-related variability in 

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

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

24 between the central site monitor and individual homes could result in either an 

25 overestimate or underestimate of residential O3.

26 A substantial number of researchers have used geostatistical methods and chemistry27 transport models to estimate O3 concentrations at urban, regional, national, and 

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

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

30 for long-term exposure assessment, O3 forecasts, or evaluating emission control 

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

32 while improved local-scale estimates of outdoor concentrations may contribute to better 

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

34 of personal exposure.
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4.5.2 Residential Air Exchange Rate Modeling

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

2 an important mechanism for entry of ambient O3. As described in Section 4.3.2, the 

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

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

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

6 such as APEX, SHEDS, and EMI (discussed in Section 4.5.3) (U.S. EPA, 2011b, 2009b; 

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

8 exposure models, mechanistic and empirical (i.e., regression-based) AER models can be 

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

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

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

12 speed). Mechanistic AER models use these meteorological parameters to account for the 

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

14 envelope from wind and indoor-outdoor temperature differences (ASHRAE, 2009). 

15 Empirical AER models do not consider the driving forces from the wind and indoor16 outdoor temperature differences. Instead, a scaling constant can be used based on factors 

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

18 Single-zone mechanistic models represent a whole-building as a single, well-mixed 

19 compartment. These AER models, such as the Lawrence Berkeley Laboratory (LBL) 

20 model, can predict residential AER using input data from whole-building pressurization 

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

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

23 leakage area model using population-level census and residential survey data (Sherman 

24 and McWilliams, 2007) and individual-level questionnaire data (Breen et al., 2010). The 

25 LBL model, which predicts the AER from air infiltration (i.e., small uncontrollable 

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

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

28 AER predictions from the LBL and LBLX models were compared to daily AER 

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

30 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 31 ) for 

32 the LBL and LBLX models, respectively. Given the uncertainty of the AER 

33 measurements (accuracy of 20-25% for occupied homes), these results demonstrate the 

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

35 openings) and natural ventilation (e.g., window opening) to help reduce the AER 

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

37 exposure modeling needs, as described in Section 4.5.3, which includes the ability to 
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1 predict indoor concentrations of ambient O3 that may be substantial for conditions of 

2 high AER such as open windows.

4.5.3 Microenvironment-Based Models

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

4 Human Exposure and Dose Simulation (SHEDS) integrated microenvironmental 

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

6 2009b; Burke et al., 2001). These are described in detail in the 2008 NOX ISA (U.S. 

7 EPA, 2008b), in AX3.6.1. Stochastic models utilize distributions of pollutant-related and 

8 individual-level variables, such as ambient and local O3 concentration contributions and 

9 breathing rate respectively, to compute the distribution of individual exposures across the 

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

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

12 framework rather than point estimates allows the models to incorporate uncertainty and 

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

14 time-weighted exposure for modeled individuals by summing exposure in each 

15 microenvironment visited during the exposure period. 

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

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

18 concentrations in a set of microenvironments are generated either by mass balance 

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

20 factors. Microenvironments modeled include indoor residences; other indoor locations, 

21 such as schools, offices, and public buildings; and vehicles. The sequence of 

22 microenvironments and exertion levels during the exposure period is determined from 

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

24 profile for each simulated individual by sampling from distributions of demographic 

25 variables such as age, gender, and employment; physiological variables such as height 

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

27 conditioning. Activity and location (microenvironmental) patterns from a database such 

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

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

30 rates for each individual are calculated for each activity based on predicted energy 

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

32 computed. APEX has an algorithm to estimate O3 dose and changes in FEV1 resulting 

33 from O3 exposure. Summaries of individual- and population-level metrics are produced, 

34 such as maximum exposure or dose, number of individuals exceeding a specified 

35 exposure/dose, and number of person-days at or above benchmark exposure levels. The 
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1 models also consider the nonambient contribution to total exposure. Nonambient source 

2 terms are added to the infiltration of ambient pollutants to calculate the total 

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

4 nonambient sources can be compared to estimate the ambient contribution to total 

5 exposure and dose.

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

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

8 Total Risk Studies) in a simulation of O3 exposure in Philadelphia over a 2-week period 

9 in July 1999. 500 individuals were sampled from CHAD in each of 482 census tracts to 

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

11 over the modeling domain were calculated from interpolation of photochemical modeling 

12 results and fixed-site monitor concentrations. These concentrations were then used as 

13 input data for SHEDS. Median microenvironmental concentrations predicted by SHEDS 

14 for nine simulated microenvironments were strongly correlated with outdoor 

15 concentrations, a result consistent with the lack of indoor O3 sources in the model. A 

16 regression of median microenvironmental concentrations against outdoor concentrations 

17 indicated that the microenvironmental concentrations were appreciably lower than 

18 outdoor concentrations (regression slope = 0.26). 95th percentile microenvironmental 

19 concentrations were also well correlated with outdoor concentrations and showed a 

20 regression slope of 1.02, although some microenvironmental concentrations were well 

21 below the outdoor values. This suggests that in most cases the high-end concentrations 

22 were associated with outdoor microenvironments. Although the authors did not report 

23 exposure statistics for the population, their dose calculations also indicated that O3 dose 

24 due to time spent outdoors dominated the upper percentiles of the population dose 

25 distribution. They found that both the 50th and 95th percentile O3 concentrations were 

26 correlated with census-tract level outdoor concentrations estimated by photochemical 

27 modeling combined with spatiotemporal interpolation, and attributed this correlation to 

28 the lack of indoor sources of O3. Relationships between exposure and concentrations at 

29 fixed-site monitors were not reported.

30 As part of the previous NAAQS review completed in 2008, EPA’s Office of Air Quality 

31 Planning and Standards used APEX-O3 to estimate O3 exposures in 12 cities during the 

32 O3 monitoring seasons of 2002-04 and reported the results in the 2007 O3 Staff Paper 

33 (U.S. EPA, 2007b). Exposures were modeled for the general population, school-age 

34 children (ages 5-18), and asthmatic school-age children. Hourly air quality input data 

35 from monitors in each city were adjusted to simulate just meeting various alternative 

36 standards, ranging from 65 to 85 ppb (8-h average), to demonstrate the effect of different 

37 standards on O3 exposure metrics. O3 decay (i.e., reaction) in indoor microenvironments 

38 was modeled, but no indoor O3 sources were included.
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1 Results of the model runs indicated that children and asthmatic children had similar 

2 exposures, with the general population experiencing lower exposure. For example, in 

3 Boston using 2002 air quality data adjusted to meet the then-current 8-h standard of 0.08 

4 ppm (fourth-highest maximum averaged over 3 yr), approximately 28% of children or 

5 asthmatic children were estimated to experience one or more 8-h avg exposures of 70 ppb 

6 or greater during an 8-h period in which they engaged in moderate exercise. In 

7 comparison, about 10% of the general population (including children) would experience a 

8 70 ppb-8h or greater exposure under the same conditions (Exhibit 2 and Figure 4-7 of the 

9 Staff Paper). A similar pattern was observed in other cities, although the magnitude of 

10 exposure was different. In most cases, exposures were substantially higher in 2002 than 

11 2004, with 2003 exposures in between the estimates for the other two years (Figure 4-8 of 

12 the Staff Paper).

13 Exposures were quite variable across cities due primarily to differing air quality 

14 distributions that resulted in a differential result from the air quality adjustment 

15 procedure. For example, the same 74 ppb-8h (fourth maximum) alternative standard 

16 scenario for 2002 estimated that 10% of Boston children but very few (<0.5%) of Los 

17 Angeles children experience exposures above 70 ppb-8h while engaged in moderate 

18 exertion. The relationship between the fourth-highest concentrations (the basis for the air 

19 quality adjustment) and the remainder of the air quality distribution is quite different 

20 between the two cities, with the result that more of the upper range of the air quality data 

21 was rolled back in Los Angeles than in Boston. This substantially reduced the occurrence 

22 of modeled high-end exposures.

23 Simulations indicate that meeting O3 air quality standards would reduce the fraction of 

24 individuals experiencing high-end exposures, as expected. Using unadjusted 2004 air 

25 quality data (the lowest of the three years simulated), the estimate of the fraction of 

26 children experiencing a 60 ppb-8h exposure while engaging in moderate exertion ranged 

27 from 12% (Chicago) to 69% (Los Angeles). Adjusting air quality data to meet fourth28 maximum alternative standards of 85, 75, and 65 ppb reduced that range to 1-26%, 0-

29 11%, and 0-1%, respectively (Exhibit 9 of the Staff Paper).

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

31 uncertainty in input parameters and databases to the uncertainty in model outputs 

32 (Langstaff, 2007). The Monte Carlo analysis indicates that the uncertainty in model 

33 exposure estimates for asthmatic children during moderate exercise is small to moderate, 

34 with 95% confidence intervals of at most ± 6 percentage points at exposures above 60, 

35 70, and 80 ppb (8-h avg) However, APEX appears to substantially underestimate the 

36 frequency of multiple high-exposure events for a single individual. The two main sources 

37 of uncertainty identified were related to the activity pattern database and the spatial 
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1 interpolation of fixed-site monitor concentrations to other locations. One area of potential 

2 improvement in the activity pattern database is additional information on children’s 

3 activities, including longitudinal patterns. Improved information on spatial variation of 

4 O3 concentrations, including in near-roadway and indoor microenvironments, would also 

5 contribute to reduced uncertainty. Another area of need is for improved personal 

6 exposure monitors with shorter averaging times to capture peak exposures and lower 

7 detection limits to capture low indoor concentrations. A similar modeling approach is 

8 currently being developed which is suitable for panel epidemiologic studies or for 

9 controlled human exposure studies, in which activity pattern data specific to the 

10 individuals in the study can be collected. Time-activity data is combined with 

11 questionnaire data on housing characteristics, presence of indoor or personal sources, and 

12 other information to develop a personalized set of model input parameters for each 

13 individual. This model, the Exposure Model for Individuals, is under development by 

14 EPA’s National Exposure Research Laboratory (U.S. EPA, 2011b; Zartarian and Schultz, 

15 2010).

4.6 Implications for Epidemiologic Studies

16 Exposure measurement error, which refers to the uncertainty associated with using 

17 exposure metrics to represent the actual exposure of an individual or population, can be 

18 an important contributor to variability in epidemiologic study results. Time-series studies 

19 assess the daily health status of a population of thousands or millions of people over the 

20 course of multiple years (i.e., thousands of days) across an urban area by estimating their 

21 daily exposure using a short monitoring interval (hours to days). In these studies, the 

22 community-averaged concentration of an air pollutant measured at central-site monitors 

23 is typically used as a surrogate for individual or population ambient exposure. In 

24 addition, panel studies, which consist of a relatively small sample (typically tens) of 

25 study participants followed over a period of days to months, have been used to examine 

26 the health effects associated with short-term exposure to ambient concentrations of air 

27 pollutants (Delfino et al., 1996). Panel studies may also apply a microenvironmental 

28 model to represent exposure to an air pollutant. A longitudinal cohort epidemiologic 

29 study, such as the ACS cohort study, typically involves hundreds or thousands of subjects 

30 followed over several years or decades (Jerrett et al., 2009). Concentrations are generally 

31 aggregated over time and by community to estimate exposures. 

32 Exposure error can under- or over-estimate epidemiologic associations between ambient 

33 pollutant concentrations and health outcomes by biasing effect estimates toward or away 

34 from the null. Exposure misclassification can also tend to obscure the presence of 

35 thresholds for health effects, as demonstrated by a simulation study of nondifferential 
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1 exposure misclassification (Brauer et al., 2002). The importance of exposure 

2 misclassification varies with study design and is dependent on the spatial and temporal 

3 aspects of the design. For example, the use of a community-averaged O3 concentration in 

4 a time-series epidemiologic study may be adequate to represent the day-to-day temporal 

5 concentration variability used to evaluate health effects, but may not capture differences 

6 in the magnitude of exposure due to spatial variability. Other factors that could influence 

7 exposure estimates include nonambient exposure, topography of the natural and built 

8 environment, meteorology, measurement errors, use of ambient O3 concentration as a 

9 surrogate for ambient O3 exposure, and the presence of O3 in a mixture of pollutants. The 

10 following sections will consider various sources of error and how they affect the 

11 interpretation of results from epidemiologic studies of different designs.

4.6.1 Nonambient Ozone Exposure

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

13 personal exposure. There are relatively few indoor sources of O3; as a result, personal O3

14 exposure is expected to be dominated by ambient O3 in outdoor microenvironments and 

15 in indoor microenvironments with high air exchange rates (e.g., with open windows). 

16 Even in microenvironments where nonambient exposure is substantial, such as in a room 

17 with an O3 generator, this nonambient exposure is unlikely to be temporally correlated 

18 with ambient O3 exposure (Wilson and Suh, 1997), and therefore would not affect 

19 epidemiologic associations between O3 and a health effect (Sheppard et al., 2005). In 

20 simulations of a nonreactive pollutant, Sheppard et al. (2005) concluded that nonambient 

21 exposure does not influence the health outcome effect estimate if ambient and 

22 nonambient concentrations are independent. Since personal exposure to ambient O3 is 

23 some fraction of the ambient concentration, it should be noted that effect estimates 

24 calculated based on personal exposure rather than ambient concentration will be 

25 increased in proportion to the ratio of ambient concentration to ambient exposure, and 

26 daily fluctuations in this ratio can widen the confidence intervals in the ambient 

27 concentration effect estimate, but uncorrelated nonambient exposure will not bias the 

28 effect estimate. 

4.6.2 Spatiotemporal Variability

29 Spatial and temporal variability in O3 concentrations can contribute to exposure error in 

30 epidemiologic studies, whether they rely on central-site monitor data or concentration 

31 modeling for exposure assessment. Spatial variability in the magnitude of concentrations 

32 may affect cross-sectional and large-scale cohort studies by undermining the assumption 
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1 that intra-urban concentration and exposure differences are less important than inter2 urban differences. This issue may be less important for time-series studies, which rely on 

3 day-to-day temporal variability in concentrations to evaluate health effects. Low inter4 monitor correlations contribute to exposure error in time-series studies, including bias 

5 toward the null and increased confidence intervals.

6 The averaging time of the daily exposure metrics used to evaluate daily aggregated health 

7 data (e.g., 1-h or 8-h daily maximum vs. 24-h avg concentration) may also impact 

8 epidemiologic results, since different studies report different daily metrics. Correlations 

9 between 1-h daily max, 8-h daily max, and 24-h avg concentrations for U.S. monitoring 

10 sites are presented in Section 3.6.1 (Figure 3-18 and accompanying text). The two daily 

11 peak values (1-h max and 8-h max) are well correlated, with a median (IQR) correlation 

12 of 0.97 (0.96-0.98). The correlation between the 8-h max and 24-h avg are somewhat less 

13 well correlated with a median (IQR) correlation of 0.89 (0.86-0.92). While this may 

14 complicate quantitative comparisons between epidemiologic studies using different daily 

15 metrics, as well as the interpretation of studies using metrics other than the current 8-h 

16 standard, the high inter-metric correlations suggest it is a relatively small source of 

17 uncertainty in comparing the results of studies using different metrics. This is supported 

18 by a study comparing each of these metrics in a time-series study of respiratory ED visits 

19 (Darrow et al., 2011b), which found positive associations for all metrics, with the 

20 strongest association for the 8-h daily max exposure metric (Section 6.7.3.2).

21 The ratios of 1-h daily max, 8-h daily max, and 24-h avg concentrations to one another 

22 have been found to differ across communities and across time within individual 

23 communities (Anderson and Bell, 2010). For example, 8:24 hour ratios ranged from 1.23-

24 1.83, with a median of 1.53. Lower ratios were generally observed in the spring and 

25 summer compared to fall and winter. O3 concentration was identified as the most 

26 important predictor of ozone metric ratios, with higher overall O3 concentrations 

27 associated with lower ratios. In communities with higher long-term ozone concentrations, 

28 the low 8:24 hour ratio is attributed to high baseline O3, which results in elevated 24-h 

29 average values. Differences in the representativeness of O3 metrics introduces uncertainty 

30 into epidemiologic results and complicates comparison of studies using different metrics. 

31 Preferably, studies will report results using multiple metrics. In cases where this does not 

32 occur, the results of this study can inform the uncertainty associated with using a standard 

33 increment to adjust effect estimates based on different metrics so that they are 

34 comparable (Chapter 6).

35 A study compared measures of spatial and temporal variability for 1-h daily max and 24-

36 h daily avg O3 concentrations in Brazil (Bravo and Bell, 2011). The 1-h daily max value 

37 was found to have higher correlation between monitors (i.e., lower temporal variability) 
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1 and lower COD (a measure of spatiotemporal variability which incorporates differences 

2 in concentration magnitude, with lower values indicating lower variability; see Chapter 3) 

3 than the 24-h avg value. The range of correlation coefficients and COD values was 

4 similar between the two metrics, although the variation was lower for the 1-h daily max, 

as indicated by the R2 5 value for the regression of correlation coefficient on inter-monitor 

6 distance.

7 Long-term exposure epidemiologic studies use concentrations averaged over months, 

8 years, or decades to evaluate health effects of extended O3 exposure. A study in Canada 

9 comparing exposure assessment methods for long-term O3 exposure found that the 

10 annual average concentration in the census tract of a subject’s residence during 1980 and 

11 1994 was well-correlated (0.76 and 0.83, respectively) with a concentration metric 

12 accounting for movement among census subdivisions during 1980-2002 (Guay et al., 

13 2011). This may have been due in part to a relatively low rate of movement, with subjects 

14 residing on average for 71% of the 22-year period in the same census subdivision they 

15 were in during 1980. This suggests that an exposure metric based on a single year can 

16 represent exposure over a multi-decade period.

4.6.2.1 Spatial Variability

17 Spatial variability of O3 concentrations is highly dependent on spatial scale; in effect, O3

18 is a regional pollutant subject to varying degrees of local variability. In the immediate 

19 vicinity of roadways, O3 concentrations are reduced due to reaction with NO and other 

20 species (Section 4.3.4.2); over spatial scales of a few kilometers, O3 may be more 

21 homogeneous due to its formation as a secondary pollutant; over scales of tens of 

22 kilometers, atmospheric processing can result in higher concentrations downwind of an 

23 urban area than in the urban core. Local-scale variations have a large impact on the

24 relative magnitude of concentrations among urban monitors, while conditions favoring 

25 high or low rates of O3 formation (e.g., temperature) vary over large spatial scales. This 

26 suggests that neighborhood monitors are likely to track one another temporally, but miss 

27 small-scale spatial variability in magnitude. In rural areas, a lower degree of fluctuation 

28 in O3 precursors such as NO and VOCs is likely to make the diel concentration profile 

29 less variable than in urban areas, resulting in more sustained ambient levels. Spatial 

30 variability contributes to exposure error if the ambient O3 concentration measured at the 

31 central site monitor is used as an ambient exposure surrogate and differs from the actual 

32 ambient O3 concentration outside a subject’s residence and/or worksite (in the absence of 

33 indoor O3 sources). Averaging data from a large number of samplers will dampen 

34 intersampler variability, and use of multiple monitors over smaller land areas may allow 

35 for more variability to be incorporated into an epidemiologic analysis. 
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1 Community exposure may not be well represented when monitors cover large areas with 

2 several subcommunities having different sources and topographies, such as the 

3 Los Angeles CSA (Section 3.6.2.1). Ozone monitors in Los Angeles had a much wider 

4 range of intermonitor correlations (-0.06 to 0.97) than Atlanta (0.61 to 0.96) or Boston 

5 (0.56 to 0.97) using 2007-2009 data. Although the negative and near-zero correlations in 

6 Los Angeles were observed for monitors located some distance apart (>150 km), some 

7 closer monitor pairs had low positive correlations, likely due to changes in topography 

8 and airflow patterns over short distances. Lower COD values, which indicate less 

9 variability among monitors in the magnitude of O3 concentrations, were observed in 

10 Atlanta (0.05-0.13) and Boston (0.05-0.19) than Los Angeles (0.05-0.56), although a 

11 single monitor (AM) was responsible for all Los Angeles COD values above 0.40. The 

12 spatial and temporal variability in O3 concentration in 24 MSAs across the U.S. was also 

13 examined in the 2006 O3 AQCD by using Pearson correlation coefficients, values of the 

14 90th percentile of the absolute difference in O3 concentrations, and CODs. No clear 

15 discernible regional differences across the U.S. were found in the ranges of parameters 

16 analyzed. 

17 An analysis of the impact of exposure error due to spatial variability and instrument 

18 imprecision on time-series epidemiologic study results indicated that O3 has relatively 

19 low exposure error compared to other routinely monitored pollutants, and that the 

20 simulated impact on effect estimates is minor. Goldman et al. (2011) computed 

21 population-weighted scaled semivariances and Pearson correlation coefficients for daily 

22 concentration metrics of twelve pollutants measured at multiple central-site monitors in 

23 Atlanta. 8-h daily max O3 exhibited the lowest semivariance and highest correlation of 

24 any of the pollutants. Although this indicates some degree of urban-scale homogeneity 

25 for O3, the analysis did not account for near-road effects on O3 concentrations. 

26 Studies evaluating the influence of monitor selection on epidemiologic study results have 

27 found that O3 effect estimates are similar across different spatial averaging scales and 

28 monitoring sites. A study in Italy compared approaches for using fixed-site monitoring 

29 data in a case-crossover epidemiologic study of daily O3 and mortality (Zauli Sajani et 

30 al., 2011). O3 effect estimates were found to be similar whether the nearest monitor was 

31 used, or whether single-city, three-city, or six-city regional averages were used for 

32 exposure assessment. In contrast, effect estimates for PM10 and NO2 increased with 

33 increasing scale of spatial averaging. Confidence intervals increased with increasing 

34 spatial scale for all pollutants. The authors attributed the consistency of O3 effect 

35 estimates to the relative spatial homogeneity of O3 over multi-km spatial scales, and 

36 pointed to the high (0.85-0.95) inter-monitor correlations to support this. The use of 

37 background monitors rather than monitors influenced by local sources in this study 

38 suggests that local-scale spatial variation in O3, such as that due to titration by traffic 
Draft – Do Not Cite or Quote 4-37 September 2011

1 emissions, was not captured in the analyses. Sarnat et al. (2010) studied the spatial 

2 variability of O3, along with PM2.5, NO2, and CO, in the Atlanta, GA, metropolitan area 

3 and evaluated how spatial variability affects interpretation of epidemiologic results, using 

4 time-series data for circulatory disease ED visits. The authors found that associations 

5 with ambient 8-h daily maximum O3 concentration were similar among all sites tested, 

6 including multiple urban sites and a rural site some 38 miles from the city center. This 

7 result was also observed for 24-h PM2.5 concentrations. In contrast, hourly CO and NO2

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

9 site associations were similar to one another for CO. This suggests that the choice of 

10 monitor may have little impact on the results of O3 time-series studies, consistent with 

11 the moderate to high inter-monitor correlations observed in Atlanta (Chapter 3).

12 One potential explanation for this finding from the study by Sarnat et al. (2010) is that 

13 although spatial variability at different scales contributes to a complicated pattern of 

14 variations in the magnitude of O3 concentrations between near-road, urban core, and 

15 urban downwind sites, day-to-day fluctuations in concentrations may be reflected across 

16 multiple urban microenvironments. In addition, time-averaging of O3 and PM2.5

17 concentrations may smooth out some of the intra-day spatial variability observed with the 

18 hourly CO and NO2 concentrations. However, some uncertainty in observed effect 

19 estimates due to spatial variability and associated exposure error is expected to remain, 

20 including a potential bias towards the null. 

4.6.2.2 Seasonality

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

22 vary by season, with at least three factors potentially contributing to this variation: 

23 differences in building ventilation (e.g., air conditioning or heater use versus open 

24 window ventilation), higher O3 concentrations during the O3 season contributing to 

25 increased exposure and improved detection by personal monitors; and changes in activity 

26 pattern resulting in more time spent outside. Evidence has been presented in studies 

27 conducted in several cities regarding the effect of ventilation on personal-ambient and 

28 indoor-outdoor O3 relationships (see Sections 4.3.2 and 4.3.3). More limited evidence is

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

30 on O3 relationships.

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

32 exposure and ambient concentration (Sarnat et al., 2005; Sarnat et al., 2000) or between 

33 indoor and outdoor O3 concentrations (Geyh et al., 2000; Avol et al., 1998b). However, 

34 others have found higher ratios in fall than in summer (Sarnat et al., 2006b) or equivalent, 
Draft – Do Not Cite or Quote 4-38 September 2011

1 near-zero ratios in winter and summer (Sarnat et al., 2001), possibly because summertime 

2 use of air conditioners decreases building air exchange rates. It should be noted that O3

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

4 possibly obscuring wintertime relationships due to detection limit issues. Studies 

5 specifically evaluating the effect of ventilation conditions on O3 relationships have found 

6 increased correlations or ratios for individuals or buildings experiencing higher air 

7 exchange rates (Sarnat et al., 2006b; Geyh et al., 2000; Sarnat et al., 2000; Romieu et al., 

8 1998b). 

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

10 between indoor and outdoor concentration, are likely to reduce error in exposure 

11 estimates used in epidemiologic studies. This suggests that studies conducted during the 

12 O3 season or in periods when communities are likely to have high air exchange rates 

13 (e.g., during mild weather) may be less prone to exposure error than studies conducted 

14 only during winter. Year-round studies that include both the O3 and non-O3 seasons may 

15 have an intermediate level of exposure error.

4.6.3 Exposure to Co-pollutants and Ozone Reaction Products

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

17 same reactions that reduce O3 indoors form particulate and gaseous species, including 

18 other oxidants, as summarized in Section 4.3.4.3. Exposures to these reaction products 

19 would therefore be expected to be correlated with ambient O3 concentrations, although 

20 no evidence was identified regarding personal exposures. Such exposure could 

21 potentially contribute to health effects observed in epidemiologic studies.

4.6.4 Averting Behavior

22 As described in Section 4.4.2, several recent studies indicate that some populations alter 

23 their behavior on high ozone days to avoid exposure. Such behavioral responses to 

24 information about forecasted air quality may introduce systematic measurement error in 

25 air pollution exposure, leading to biased estimates of the impact of air pollution on health. 

26 For example, studies have hypothesized that variation in time spent outdoors may be a 

27 driving factor behind the considerable heterogeneity in ozone mortality impacts across 

28 communities (Bell et al., 2004). If averting behavior in fact results in smaller, in 

29 magnitude, effect estimates, then studies that do not account for averting behavior may 

30 produce effect estimates that are biased towards the null (Section 6.2.7.5).
Draft – Do Not Cite or Quote 4-39 September 2011

1 This is supported by an epidemiologic study that examined the association between 

2 exposure to ambient ozone concentrations and asthma hospitalizations in Southern 

3 California during 1989-1997, which indicates that controlling for avoidance behavior 

4 increases the effect estimate for both children and older adults, but not for adults aged 20-

5 64 (Neidell and Kinney, 2010; Neidell, 2009). Figure 4-4 and Figure 4-5, reproduced 

6 from Neidell (2009), show covariate-adjusted asthma hospital admissions as a function of 

7 daily maximum 1-h O3 concentration for all days (gray line) and days when no O3 alert 

8 was issued (black line). Stage 1 smog alerts were issued by the State of California for

9 days when ambient O3 concentrations were forecast to be above 0.20 ppm; however, the 

10 concentration-response functions are based on measured O3 concentrations. For children 

11 aged 5-19 (Figure 4-4), hospital admissions were higher on high-O3 days when no alert 

12 was issued, especially on days with O3 concentrations above 0.15 ppm (150 ppb). The 

13 concentration-response curves for all days and days with no alert diverge at measured O3

14 concentrations between 0.10 and 0.15 ppm because smog alerts begin to be issued more 

15 frequently in this range. This suggests that in the absence of information that would 

16 enable averting behavior, children experience higher ozone exposure and subsequently a 

17 greater number of asthma hospital admissions than on alert days with similar O3

18 concentrations. The lower rate of admissions observed when alert days were included in 

19 the analysis suggests that averting behavior reduced O3 exposure and asthma hospital 

20 admissions. In both cases, O3 was found to be associated with asthma hospital 

21 admissions, although the strength of the association is underestimated when not 

22 accounting for averting behavior. A similar result was not observed when examining 

23 associations for adults aged 20-64 (Figure 4-5), who had similar rates of hospital 

24 admissions on non-alert days as on all days. The lack of change for adults aged 20-64, 

25 which is primary employment age, may reflect lower response to air quality alerts due to 

26 the increased opportunity cost of behavior change. The finding that air quality 

27 information reduces the daily asthma hospitalization rate in these populations provides 

28 additional support for a link between ozone and health effects.
Draft – Do Not Cite or Quote 4-40 September 2011

Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Press

(Neidell, 2009)

Figure 4-4 Adjusted asthma hospital admissions by age on lagged ozone by 

alert status, ages 5-19. 

Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Press; 

Neidell, (2009)

Figure 4-5 Adjusted asthma hospital admissions by age on lagged ozone by 

alert status, ages 20-64 
Draft – Do Not Cite or Quote 4-41 September 2011

4.6.5 Exposure Estimation Methods in Epidemiologic Studies

1 The use of O3 measurements from central ambient monitoring sites is the most common 

2 method for assigning exposure in epidemiologic studies. However, fixed-site 

3 measurements do not account for the effects of spatial variation in O3 concentration, 

4 ambient and non-ambient concentration differences, and varying activity patterns on 

5 personal exposures (Brown et al., 2009; Chang et al., 2000; Zeger et al., 2000). The use 

6 of fixed-site concentrations results in minimal exposure error when: (1) O3

7 concentrations are uniform across the region; (2) personal activity patterns are similar 

8 across the population; and (3) housing characteristics, such as air exchange rate and 

9 indoor reaction rate, are constant over the study area. Since these factors vary by location 

10 and population, there will be errors in the magnitude of total exposure based solely on 

11 ambient monitoring data.

12 Modeling approaches can also be used to estimate exposures for epidemiologic studies, 

13 as discussed in Section 4.5. Geostatistical spatial interpolation techniques can provide 

14 finer-scale estimates of local concentration over urban areas. A microenvironmental 

15 modeling approach simulates exposure using empirical distributions of concentrations in 

16 specific microenvironments together with human activity pattern data. The main 

17 advantage of the modeling approach is that it can be used to estimate exposures over a

18 wide range of population and scenarios. A main disadvantage of the modeling approach 

19 is that the results of modeling exposure assessment must be compared to an independent 

20 set of measured exposure levels (Klepeis, 1999). In addition, resource-intensive 

21 development of validated and representative model inputs is required, such as human 

22 activity patterns, distributions of air exchange rate, and deposition rate. Therefore, 

23 modeled exposures are used much less frequently in epidemiologic studies.

4.7 Summary and Conclusions

24 This section will briefly summarize and synthesize the main points of the chapter, with 

25 particular attention to the relevance of the material for the interpretation of epidemiologic 

26 studies. 

27 Passive badge samplers are the most widely used technique for measuring personal O3

28 exposure (Section 4.3.1). The detection limit of the badges for a 24-h sampling period is 

29 approximately 5-10 ppb, with lower detection limits at longer sampling durations. In low30 concentration conditions this may result in an appreciable fraction of 24-h samples being 

31 below the detection limit. The use of more sensitive portable active monitors, including 
Draft – Do Not Cite or Quote 4-42 September 2011

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 increased 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 0.1-

21 0.3, although individuals spending substantial time outdoors (e.g., outdoor workers) may 

22 have much higher ratios (0.5-0.9). Thus, applying personal-ambient ratios for outdoor 

23 workers to the general population or susceptible populations spending substantial time 

24 indoors can result in overestimates of the magnitude of personal exposure for these 

25 groups.

26 Personal exposure to other pollutants shows variable association with personal exposure 

27 to O3, with differences in copollutant relationships depending on factors such as season, 

28 city-specific characteristics, activity pattern, and spatial variability of the copollutant

29 (Section 4.3.4). In near-road and on-road microenvironments, correlations between O3

30 and traffic-related pollutants are moderately to strongly negative, with the most strongly 

31 negative correlations observed for NO2 (-0.8 to -0.9). This is consistent with the 

32 chemistry of NO oxidation, in which O3 is consumed to form NO2. The more moderate 

33 negative correlations observed for PM2.5, PM1.0, and VOC may reflect reduced 

34 concentrations of O3 in polluted environments due to other scavenging reactions. A 

35 similar process occurs indoors, where infiltrated O3 reacts with airborne or surface36 associated materials to form secondary compounds, such as formaldehyde. Although such 
Draft – Do Not Cite or Quote 4-43 September 2011

1 reactions decrease indoor O3 exposure, they result in increasing exposure to other species 

2 which may themselves have health effects.

3 Variations in ambient O3 concentrations occur over multiple spatial and temporal scales. 

4 Near roadways, O3 concentrations are reduced due to reaction with NO and other species 

5 (Section 4.3.4.2). Over spatial scales of a few kilometers and away from roads, O3 may 

6 be somewhat more homogeneous due to its formation as a secondary pollutant, while 

7 over scales of tens of kilometers, additional atmospheric processing can result in higher 

8 concentrations downwind of an urban area. Although local-scale variability impacts the 

9 magnitude of O3 concentrations, O3 formation rates are influenced by factors that vary 

10 over larger spatial scales, such as temperature (Section 3.2), suggesting that urban 

11 monitors may track one another temporally but miss small-scale variability in magnitude. 

12 The resulting uncertainty in exposure contributes to exposure measurement error in 

13 epidemiologic studies. 

14 Another factor that may influence epidemiologic results is the tendency for people to 

15 avoid O3 exposure by altering their behavior (e.g., reducing time spent outdoors) on high16 O3 days. Activity pattern has a substantial effect on ambient O3 exposure, with time 

17 spent outdoors contributing to increased exposure (Section 4.4.2). Averting behavior has 

18 been predominantly observed among children, older adults, and people with respiratory 

19 problems. Such effects are less pronounced in the general population, possibly due to the 

20 opportunity cost of behavior modification. Preliminary epidemiologic evidence reports 

21 increased asthma hospital admissions among children and older adults when O3 alert 

22 days were excluded from the analysis (presumably thereby eliminating averting behavior 

23 based on high O3 forecasts). The lower rate of admissions observed when alert days were 

24 included in the analysis suggests that estimates of health effects based on dose-response 

25 functions which do not account for averting behavior may be biased towards the null.

26 The range of personal-ambient correlations reported by most studies (0.3-0.8) is similar 

27 to that for NO2 (U.S. EPA, 2008b) and somewhat lower than that for PM2.5 (U.S. EPA, 

28 2009d). To the extent that relative changes in central-site monitor concentration are 

29 associated with relative changes in exposure concentration, this indicates that ambient 

30 monitor concentrations are representative of day-to-day changes in average total personal 

31 exposure and in personal exposure to ambient O3. The lack of indoor sources of O3, in 

32 contrast to NO2 and PM2.5, is partly responsible for low indoor-outdoor ratios (generally 

33 0.1-0.4) and low personal-ambient ratios (generally 0.1-0.3), although it contributes to 

34 increased personal-ambient correlations. The lack of indoor sources also suggests that 

35 fluctuations in ambient O3 may be primarily responsible for changes in personal 

36 exposure, even under low-ventilation, low-concentration conditions. Nevertheless, low 

37 personal-ambient correlations are a source of exposure error for epidemiologic studies, 
Draft – Do Not Cite or Quote 4-44 September 2011

1 tending to obscure the presence of thresholds, bias effect estimates toward the null, and 

2 widen confidence intervals, and this impact may be more pronounced among populations 

3 spending substantial time indoors. The impact of this exposure error may tend more 

4 toward widening confidence intervals than biasing effect estimates, since epidemiologic 

5 studies evaluating the influence of monitor selection indicate that effect estimates are 

6 similar across different spatial averaging scales and monitoring sites.

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