### Draft

– Do Not Cite or Quote 4

- 1 June 2012 

## 4 EXPOSURE TO AMBIENT OZONE

4.1 Introduction

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

### 1
microenvironments, discussed methods for estimati ng personal and population exposure 

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

### 3 ambient concentrations, and discussed the implications of using ambient O 3 

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

### 5 new information regarding exposure to ambient O 3 which builds upon the body of 

### 6 evidence presented in the 2006 O 3 AQCD. A brief summary of findings from the 2006 O 3 

### 7 AQCD is presented at the beginning of each section as appropriate

. 

### 8

Section 4.2 presents general exposure concepts describing the relationship between 

### 9 ambient pollutant concentrations and personal exposure. Section 4.3 describ es exposure 

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

### 11

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

### 12 parameters relevant to exposure estimation, including activity patterns, averting behavior, 

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

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

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

### 16

ambient O 3 concentrations to estimate exposure in epidemiologic studies, including 

### 17 several factors that contribute to exposure error. 

### 18



## 4.2 General Exposure Concepts

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

### 19

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

- integrated total 

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

### 21 activities throughout a given time period: 

### 22

Equation 4

- 1

where E T = total exposure over a time

- period of interest, C j = airborne O 3 concentration at 

### 23

microenvironment j , and dt = portion of the time

- period spent in microenvironment j. 

### 24

### Draft

– Do Not Cite or Quote 4

- 2 June 2012 Equation 4

- 1 can be decomposed into a model that accounts for exposure to O 3 , of 

### 1
ambient ( E a ) and nonambient ( E na ) origin of the form: 

### 2

Equation 4

- 2

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

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

### 4

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

### 5 import ant sources of population exposure. Ozone concentrations generated by ambient 

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

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

### 8

aff ecting interpretation of epidemiologic studies are discussed in Section 4.5

. 

### 9

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

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

- state outdoor conditions, E a can be 

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

### 12

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

### 13

Equation 4

- 3

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

- 1 ), subscript 

### 14

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

### 15 microenvironments, subscript o,i = index of outdoor micro environments adjacent to a 

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

### 17

microenvironment i

. Equation 4

- 3 is subject to the constraint Σ f

o + Σ f

i = 1 to reflect the 

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

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

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

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

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

### 23 yards, on sidewalks, and on bicy cles or motorcycles. Assuming steady state ventilation 

### 24 conditions, the infiltration factor is a function of the penetration ( P ) of O 3 into the 

### 25 microenvironment, the air exchange rate ( a ) of the microenvironment, and the rate of O 3 

### 26
loss ( k ) in the microenvironment; F inf = Pa /( a + k ). 

### 27

### Draft

– Do Not Cite or Quote 4

- 3 June 2012 In epidemiologic studies, the central

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

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

### 2

of data. Thus it is often as sumed that C
o = C
a and that the fraction of time spent outdoors 

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

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

### 5 employs only C
a , then t he assumed model of an individual’s exposure to ambient O 3 , first 

### 6 given in Equation 4

- 3 , is re

- expressed solely as a function of C
a : 

### 7

Equation 4

- 4

The spatial variability of outdoor O 3 concentrations due to meteorology, topography, 

### 8 varying precursor emissions and O 3 formation rates; the design of the epidemiologic 

### 9 study; and other factors determine whether or not Equation 4

- 4 is a reasonable 

### 10 approximation for Equation 4

- 3 . These equations also assume steady

- state 

### 11

microenvironmental concentrations. Errors and uncertainties inherent in use of Equation 

### 12

4

- 4 in lieu of Equation 4

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

### 13 interpreting epidemiologic studies. Epidemiologic studies often use concentration 

### 14

measured at a central site mon itor to represent ambient concentration; thus α , the ratio 

### 15 between personal exposure to ambient O 3 and the ambient concentration of O 3 , is defined 

### 16

as: 

### 17

Equation 4

- 5

Combination of Equation 4

- 4 and Equation 4

- 5 yields: 

### 18

Equatio n 4

- 6

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

### 19 microenvironment, the ambient component of a microenvironmental O 3 concentration 

### 20 can be represented as the product of the ambi ent concentration and F inf . Wallace et al. 

### 21
( 2006 ) note that time

- activity data and corresponding estimates of F inf for each 

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

### 23

### Draft

– Do Not Cite or Quote 4

- 4 June 2012 epidemiologic studies, α is assumed to be constant in lieu of time

- activity data and 

### 1

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

- related air exchange 

### 2

characteristics. If important local out door sources and sinks exist that are not captured by 

### 3

central site moni tors, then the ambient component of the local outdoor concentration may 

### 4 be estimated using dispersion models, land use regression models, receptor models, fine 

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

### 6

Section 4.5

. 

### 7



## 4.3 Exposure Measurement

This section describes techniques that have been used to measure microenvironmental 

### 8

concentrations of O 3 and personal O 3 ex posures as well as results of studies using those 

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

### 10

studies that evaluate indoor

- outdoor concentration relationships, associations between 

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

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

### 13 study results. 

### 14

4.3.1 Personal Monitoring Techniques

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

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

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

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

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

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

### 20

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

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

- term daily fluctuations in 

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

### 23

ba dges for a 6

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

- hour 

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

- 10 ppb. This can result in a 

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

### 26
Sarnat et al., 2005 ) , which is a limitation of past and current exposure studies. 

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

- duration monitoring with 

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

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

### 30

### Draft

– Do Not Cite or Quote 4

- 5 June 2012 The nitrite

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

### 1 sampler consisting of a nitrite

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

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

### 3

limit is 10 pp b

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

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

. 

### 5

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

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

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

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

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

### 10 configurati on. The monitors are currently used by the U.S. National Park service as 

### 11 stationary monitors to measure O 3 in several national parks ( Chapter 3 ). F uture 

### 12 improvements and continued miniaturization of real

- time O 3 monitors can yield highly 

### 13

time

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

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

### 15

4.3.2 Indoor

- Outdoor Concentration Relationships

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

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

### 17 immediately outside the indoor microenvironment. These studies show that t he indoor 

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

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

### 20

with surfaces and airborne constituents. Studies have shown that O 3 is d eposited onto 

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

### 22
( Reiss et al., 1995b ; Reiss et al., 1995a ) . However, the indoor

- outdoor relationship is 

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

### 24 as open windows, the indoor O 3 concentration may ap proach the outdoor concentration. 

### 25 Thus, in rooms with open windows, the indoor

- outdoor (I/O) ratio may approach 1.0. 

### 26

Table 4

- 1 summarizes I/O ratios a nd correlations reported by older and more recent 

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

### 28 range from about 0.1 to 0.4, with some evidence for higher ratios during the O 3 season 

### 29

when concentrations are h igher. 

### 30

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

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

### 32 resulting ratios. This problem is particularly acute in the non

- ozone seas on when ambient 

### 33

O 3 concentrations are low. Further improvements in characterization of 

### 34

### Draft

– Do Not Cite or Quote 4

- 6 June 2012 microenvironmental O 3 concentrations and I/O ratios will rely on improved monitoring. 

### 1 Until new monitoring techniques are available and can be used in the field, past st udies 

### 2

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

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

### 4

Table 4

- 1 Relationships between indo or and outdoor ozone concentration

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

- environment Comment

Geyh et al. (20 00 ) Upland, Southern California June

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

conditioned

Ratio: Indoor mean/outdoor

mean October 1995

- April 1996 0.15

Mountain Communities, Southern California June

- September 1995 and May 1996 0.36 Opening windows

Ratio: indoor mean/outdoor

mean October 1995

- April 1996 0.08

Avol et al. (1998a ) Southern California February

December, 1994 NR 24 h 0.37
SD: 0.25 0.58 Home Ratio: each pair of measurements

Summer 0.43
SD: 0.29 NR

Non

- summer 0.32
SD: 0.21 NR

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

- July 1994 Children 7 or 14 days 0.20
SD: 0.18

0.15 b
Range: 0.01

- 1.00 NR Home Ratio: each pair of measurements

Lee et al. (2004a ) Nashville, TN Summer 1994 Children 1 week 0.1
SD: 0.18 NR Home Ratio: Indoor mean/outdoor

mean

### Draft

– Do Not Cite or Quote 4

- 7 June 2012 Study Location Years/Season Population Sample duration Ratio a Correlation Micro

- environment Comment

Héroux et al. (2010 ) Regina, Saskatchewa, Canada Summer 2007 All age groups 5 days 0.13 NR Home Ratio: Indoor mean/outdoor

mean

Liu et al. (1995 ) Toronto, Canada Winter, 1992 All age groups 1 week 0.07
SD: 0.10 NR Home Ratio: each pair of measurements

Summer, 1992 0.40
SD: 0.29

Summer, 1992 12 h 0.30
SD: 0.32 Daytime

Ratio: each pair of measurements

Summer, 1992 0.43
SD: 0.54 Nighttime

Ratio: each pair of measurements

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

- July 1994 Children 24 h/day, 14 days 0.15 NR School Ratio: each pair of measurements

Children (during school hours) 5 h/day, 5 days, 10 days 0.30

- 0.40 Immediately outside the schools

Blondeau et al. (2005 ) La Rochelle, France Spring, 2000 Children 2 weeks Range: 0.00

- 0.45 NR School No air conditioning

Ratio: Indoor mean/outdoor

mean
López

Aparicio et al. (2011 ) Prague, Czech Republic July 2009 All age groups 1 month 0.10 NR Historic Library No heating or air conditioning

Ratio: Indoor mean/outdoor

mean Dec 2009 0.30

Riediker et al. (2003 ) North Carolina August

- Octob er 2001 Adults 9 h 0.51

p

value: 0.000 NR Vehicle Ratio: Indoor mean/outdoor

mean

a Mean value unless otherwise indicated

b Median

NR = not reported
SD = standard deviation.

Geyh et al. (2000 ) measured 6

- day indoor and outdoor concentrations at 116 homes in 

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

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

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

### 4

all air

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

### 5 windows. During the O 3 season, the indoor O 3 concentration a veraged over all homes was 

### 6

### Draft

– Do Not Cite or Quote 4

- 8 June 2012 approximately 24% of the overall mean outdoor concentration in Upland (11.8 versus 

### 1

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

### 2
outdoor concentration (21.4 versus 60.1 ppb). This is consis tent with the increased air 

### 3 exchange rate expected in homes using window ventilation. In the non

- ozone season, 

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

### 5
outdoor concentration were 0.15 (3.2 versus 21.1 ppb) a nd 0.08 (2.8 versus 35.7 ppb) in 

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

### 7

I/O ratio of 0.37 for 239 matched 24

- h samples collected between February and 

### 8

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

### 9 higher than the non

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

### 10

correlation of 0.58 between the 24

- h avg indoor concentration and the outdoor 

### 11 concentration, which was only slightly higher than the correlation between t he indoor 

### 12 concentration and the concentration at the neighborhood fixed

- site monitor (0.49). 

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

### 14

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

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

### 15

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

### 16

Mexico City for 7

- or 14

- day cumulative samples, with the highest ratios observed in 

### 17

homes where windo ws were usually open during the day and where there was no 

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

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

### 20

2004a )

. 

### 21

Investigators have also measured I/O ratios for non

- residen tial microenvironments, 

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

### 23 measured during school hours (10

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

- 40% of 

### 24 concentrations immediately outside the schools, while overall I/O ratios (14

- day 

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

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

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

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

### 29

based on simultaneous continuous measurements ranged from 0

- 0.45, increasing with 

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

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

### 32

of one

- month indo or and outdoor concentrations ranging from 0.10

- 0.30 during a nine



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

- Dec 2009 and the 

### 34 lowest ratios during Jul

- Aug 2009 ( López

- Aparicio et al., 2011 ) . Indoor concentrations 

### 35 were relatively constant (approximately 3

- 7 μg/m 3 or 2

- 3 ppb), while outdoor 

### 36
concentrations were lower in the winter (9

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

### 37
(35

- 45 μg/ m 3 or about 20 ppb). This seasonal variation in outdoor concentra tions 

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

### 39

### Draft

– Do Not Cite or Quote 4

- 9 June 2012 exchange rate due to higher indoor

- outdoor temperature differences, is likely responsible 

### 1 for the observed seasonal pattern in I/O ratios. 

### 2

Exposures in near

- road, on

- road and in

- vehicle microenvironments are likely to be more 

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

### 4

O 3 with NO and other combustion emissions. Depending on wind direction, O 3 

### 5

concentrations near the road way have been found to be 20

- 80% of ambient 

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

### 7 on patrol cars during trooper work shifts reported in

- vehicle 9

- h concentrations that were 

### 8 approximately 51% of simultaneously measured roadside concentrations (mean of 11.7 

### 9

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

. 

### 10

4.3.3 Personal

- Ambient Concentration Relationships

Several factors influen ce the relationship between personal O 3 exposure and ambient 

### 11

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

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

- vehicle O 3 concentrations 

### 13 are highly depende nt on air exchange rate and therefore vary widely in different 

### 14

microenvironments. Ambient O 3 varies spatially due to reactions with other atmospheric 

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

### 16
reaction with NO (Section 3.6.2.1 ). This is in contrast with pollutants such as CO and 

### 17

NO X , which show appreciably higher concentrations near the roadway than several 

### 18

hundre d meters away ( Karner et al., 2010 ) . O 3 also varies temporally over multiple 

### 19 scales, with generally increasing concentrations during the daytime hours, and higher O 3 

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

### 21 Figure 4

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

. 

### 22

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

### 23

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

### 24

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

### 25

monitor ing data. In comparison, ambient PM 2.5 exhibits less temporal variability over the 

### 26 day than O 3 , although personal exposure to PM 2.5 also varies by microenvironment. This 

### 27 combined spatial and temporal variability for O 3 results in varying relationships betw een 

### 28 personal exposure and ambient concentration. 

### 29

Correlations between personal exposure to O 3 and corresponding ambient concentrations, 

### 30

summarized in Table 4

- 2 , exhibit a wide range (generally 0.3

- 0.8, although both higher 

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

### 32 outdoor microenvironments, high building ventilation conditions, and during the summer 

### 33

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

### 34

### Draft

– Do Not Cite or Quote 4

- 10 June 2012 personal exposures below the sampler detection limit, which may partially explain the 

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

### 2

corr elations over a range of averaging times, with no clear trend. Ratios of personal 

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

- 3 , are gene rally lower in 

### 4 magnitude (typically 0.1

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

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

### 6 reported personal

- ambient correlations and slopes for a variety of s easons, locations, and 

### 7 populations. 

### 8

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

.


### Figure 4

- 1 Variation in hourly personal and ambient concentrations of ozone and PM 2.5 

### in various microenvironments during daytime hours.

### Draft

– Do Not Cite or Quote 4

- 11 June 2012 Ozone c oncentrations near and below the passive sampler detection limit lead to 

### 1 uncertainty in personal

- ambient correlations and ratios. Correlations are reduced in 

### 2 magnitude by values below the detection limit because noise obscures the underlying 

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

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

### 5 ratios, this problem is particularly acute in the non

- ozone season when ambient O 3 

### 6

concent rations are low. Improved characterization of the relationship between personal 

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

### 8 monitoring techniques to accurately capture low O 3 concentrations, preferably at high 

### 9

time resoluti on to facilitate evaluation of the effect of activity pattern on exposure 

### 10
(Section 4.3.1 ). While data from studies using new monitoring techniques b ecome 

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

### 12 along with more recent studies in evaluating personal

- ambient concentration 

### 13 relationships. 

### 14

Personal

- Ambient Correlations. Correlations between personal exposure and 

### 15

ambient O 3 concentrations have been evaluated in several research studies, many of 

### 16 which were conducted prior to 2005 and are discussed in the 2006 O 3 AQCD. Some 

### 17 studies evaluated subject

- specific, or longitudina l correlations, which describe multiple 

### 18 daily measurements for a single individual. These studies indicate the inter

- individual 

### 19 variability of personal

- ambient correlations. Another type of correlation is a pooled 

### 20
correlation, which combines data from mult iple individuals over multiple monitoring 

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

- ambient relationship 

### 22 for all study subjects. A third type of correlation is a community

- average correlation, 

### 23 which correlates average exposure ac ross all study subjects with fixed

- site monitor 

### 24 concentrations. Community

- average correlations are particularly informative for 

### 25 interpreting time

- series epidemiologic studies, in which ambient concentrations are used 

### 26 as a surrogate for community

- average ex posure. However, few studies report this metric; 

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

### 28

Table 4

- 2 sum marizes studies reporting personal

- ambient correlations, and the studies in 

### 29 the table are discussed in the subsequent text. 

### 30

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

### 31
well correlated with ambient concentrations, and that the ratio of personal exposure to 

### 32 ambient concentration is higher in outdoor microenvironments and during the summer 

### 33

season. In some situations

, a low correlation was observed, and this may be due in part to 

### 34 a high proportion of personal measurem ents below the detection limit of the personal 

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

### 36 correlations are observed during the O 3 season. Chang et al. (2000 ) measured hourly 

### 37 personal exposures in multiple microenvironments and found that the pooled correlation 

### 38

### Draft

– Do Not Cite or Quote 4

- 12 June 2012 between personal exposure and ambient concentration was highest for outdoor 

### 1
microenvironments (r = 0.68

- 0 .91). In

- vehicle microenvironments showed moderate to 

### 2 high correlations (0.57

- 0.72). Correlations in residential indoor microenvironments were 

### 3 very low (r = 0.05

- 0.09), with moderate correlations (0.34

- 0.46) in other indoor 

### 4

microenvironments such as restau rants and shopping malls. Liard et al. (1999 ) evaluated 

### 5 community

- average correlations based on 4

- day mean personal O 3 exposure 

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

### 7 with ambient concentrations, even though 31

- 82% of the personal measurements were 

### 8

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

### 9 Baltimore and found that longitudinal correlations between 24

- h personal exposure and 

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

### 11 observed in this study during summer (mean = 0.20) than in winte r (mean = 0.06). Some 

### 12 evidence was presented that subjects living in well

- ventilated indoor environments have 

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

### 14 exceptions to this were also observed. Ramírez

- Aguilar et al. (2008 ) measured 48

- to 

### 15

72

- h personal exposures of fo ur groups of asthmatic children aged 6

- 14 in Mexico City 

### 16 during 1998

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

### 17 exposures and corresponding ambient concentrations. 

### 18

### Draft

– Do Not Cite or Quote 4

- 13 June 2012 Table 4

- 2 Correlations between personal and ambient ozone concentration.

Study Location Years/Season Population Sample duration Correlation Study Type Comment

Chang et al. (2000 ) Baltimore, MD Summer 1998 Older adults 1 h 0.91 Pooled Outdoor

near 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 Commun ity

averaged

Sarnat et al. (2000 ) Baltimore, MD Summer Older adults 24 h 0.20
SD: 0.28

95% CI: 0.06, 0.34 Longitudinal

Winter 0.06
SD: 0.34

95% CI:

- 0.88, 0.24

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

averaged

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

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

- 45% of time outdoors

Farm workers 24 h 0.64 Pooled 100% of time outdoors

Ramír ez

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

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

Delfino et al. (1996 )

. San Diego, California September and October 1993 Asthmatic children 12

- h 0.45 Range: 0.35

- 0.69 Pooled

NR = not reported

Consistent with hourly microenvironment

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

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

- ambient 

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

### 3 0.61 was reported between 24

- h avg personal and central

- site measurements by Linn et 

### 4
al. (1996 ) for a population of southern California schoolchildren who sp ent an average of 

### 5

### Draft

– Do Not Cite or Quote 4

- 14 June 2012 101

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

### 1

central

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

### 2 the average school outdoor concentration (34 ppb) was higher tha n the average central



### 3
site concentration (23 ppb) and the average personal exposure concentration was lower 

### 4
(5 ppb) than the central

- site value, the similarity between the correlations in this study 

### 5

indicate that central

- site monitor concentrations can rep resent personal exposures in 

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

### 7 provided another illustration of the effect of outdoor microenvironments on personal



### 8 ambient relationships by comparing three groups spending diffe rent amounts of time 

### 9 outdoors: health clinic workers (0

- 25% of sampling period outdoors), camp counselors 

### 10
(7.5

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

### 11
outdoors) ( Brauer and Brook, 1997 ) . Health clinic workers and camp counselors were 

### 12

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

### 13
(6

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



### 14 site concentration were not substantially different among the groups (r = 0.60, 0.42, and 

### 15 0.64, respectively). The ratios of personal exposure to fixed

- site mon itor concentration 

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

### 17 and 0.96, respectively). This indicates that temporal variations in personal exposure to O 3 

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

### 19

time outdoors. 

### 20

Personal

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

### 21 exposure and ambient concentration varies widely, depending on activity p atterns, 

### 22 housing characteristics, and season. Higher personal

- ambient ratios are generally 

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

### 24

other than winter. Table 4

- 3 summarizes the results of several such studies discussed in 

### 25

the 2006 O 3 AQCD together with newer studies showing the same pattern of results. 

### 26

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

### 27

Mexico City and presented a regression mod el indicating a 0.56 ppb increase in 6

- h 

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

### 29 by ( 2005 ; 2001 ) for 24

- h data from mi xed populations of children and older adults in 

### 30

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

### 31 between the two cities, with Baltimore subjects showing a near

- zero slope (0.01) during 

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

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

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

### 35
outdoors, residential ventilation conditions, or other factors. Xue et al. (2005 ) measured 

### 36

6

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

### 37 ratio of personal exposure to ambient concentration was relatively stable throug hout the 

### 38

### Draft

– Do Not Cite or Quote 4

- 15 June 2012 year at 0.3. These authors also regressed personal exposures on ambient concentration 

### 1 after adjusting for time

- activity patterns and housing characteristics and found a slope of 

### 2

0.54 ppb/ppb, with the regression R 2 value of 0.58. Unadjusted regres sion slopes were 

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

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

### 5

Table 4

- 3 Rat ios of personal to ambient ozone concentration.

Study Location Years/Season Population Sample duration Ratio a Slope Inter

cept Study Type Comment

Sarnat et al. (2001 ) Baltimore Summer 19 98 Older adults and children 24 h NR 0.01 1.84 Longitudinal t

- value: 1.21

Winter 1999 Older adults, children, and individuals with COPD NR 0.00 0.46 t

- value: 0.03

Brauer and Brook (1997 ) Vancouver, Canada Summers 1992 and 1993 Health clinic workers 24 h 0.35 NR NR Pooled 0

- 25% of time outdoors
Camp counselors 0.53 NR NR Pooled 7.5

- 45% of time outdoors

Farm workers 0.96 NR NR Pooled 100% of time outdoors

O'Neill et al. (2003 ) Mexico City, Mexico April

- July 1996 Shoe cleaners 6 h 0.40

0.37 b

SD: 0.22 0.56

95% CI: 0.43

- 0.69 NR Longitudinal

Sarnat et al. (2005 ) Boston Summer Older adults a nd children 24 h NR 0.27

95% CI: 0.18

- 0.37 NR Longitudinal

Winter NR 0.04

95% CI: 0.00

- 0.07 NR

### Draft

– Do Not Cite or Quote 4

- 16 June 2012 Study Location Years/Season Population Sample duration Ratio a Slope Inter

cept Study Type Comment

Xue et al. (2005 ) Southern California June 1995

- May 1996 Children 6 day 0.3
SD: 0.13 NR NR Longitudinal

Sarnat et al. (2006a ) Steubenville, OH Summer Older adults 24 h NR 0.15
SE: 0.02

t

- value:

7.21

R 2 : 0.24 NR Longitudinal All individuals

NR 0.18
SE: 0.03

t

- value:

7.34

R 2 : 0.27 NR High

ventilation

NR 0.08
SE: 0.04

t

- value:

1.89

R 2 : 0.19 NR Low

ventilation

Fall NR 0.27
SE: 0.03

t

- value:

8.64

R 2 : 0.25 NR All individuals

NR 0.27
SE: 0.04

t

- value:

7.38

R 2 : 0.33 NR High

ventilation

NR 0.20
SE: 0.05

t

- value:

3.90

R 2 : 0.12 NR Low

ventilation

Ramírez

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

- Apr. 2000 Asthmatic children 48 h to

72 h 0.23 0.17
SE: 0.02

95% CI : 0.13

- 0.21

p

- value:

0.00 Pooled

a Mean value unless otherwise indicated
b M e dian NR = not reported SD = standard deviation

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

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

### 2

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

### 3 may impact perso nal

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

- h personal 

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

### 5

### Draft

– Do Not Cite or Quote 4

- 17 June 2012 Subjects were classified as high

- ventilation or low

- ventilation based on whether they 

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

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

- ventilation subjects compared 

### 3

with low

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

### 4
0.20). Suh and Zanobetti (2010 ) reported an average 24

- h personal exposure of 2.5 ppb as 

### 5 compared to 24

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

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

### 7

the mean 24

- h personal exposure across 137 participants in summer and winter was 

### 8

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

### 9
et al., 2009b ) . Although no personal exposures were measured, Mcconnell et al. (2006 ) 

### 10 found that average 24

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

### 11

concentrations measured at central

- site monitors in each of three southern California 

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

### 13

to the central

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

- Aguilar et al. (2008 ) 

### 14 regressed 48

- to 72

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

- 14 

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

### 16

distance to the fixed

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

### 17
these two variables, and an interaction term representing neighborhood and study group. 

### 18

4.3.4 Co

- exposure to Other Pollutants and Environmental Stressors

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

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

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

### 21 have either known or pote ntial health effects that may impact health outcomes due to O 3 . 

### 22

The co

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

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

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

- exposure 

### 25 relationships for near

- road O 3 exposure. 

### 26

4.3.4.1 Personal Exposure to Ozone and Copollutants

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

### 27 pollutants, with differ ences in correlation depending on factors such as instrument 

### 28 detection limit, season, city

- specific characteristics, time scale, and spatial variability of 

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

### 30

c oefficients during spring and fall between 24

- h avg O 3 measurements and co

- pollutants 

### 31
of 0.14, 0.00, and

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

### 32

### Draft

– Do Not Cite or Quote 4

- 18 June 2012 roadways is likely to contribute to the low or slightly negative correlations with the 

### 1

traffic

- related pollutants EC and NO 2 . The somewhat higher correlation with PM 2.5 may 

### 2 reflect the influence of air exchange rate and time spent outdoors on co

- exposures to 

### 3

ambient PM 2.5 and O 3 . Overall, the copollutant correlations are quite small, which may be 

### 4 due to the very low personal exposures observed in this study (2

- 3 ppb), likely to be near 

### 5 or below the detection limit of the passive sampler over a 24

- h period. Chang et al. 

### 6 (2000 ) measured hourly personal exposures to PM 2.5 and O 3 in summer and winter in 

### 7 Baltimore, Maryland. Correlations between PM 2.5 and O 3 were 0.05 and

- 0.28 in summer 

### 8 and winter, respectively. Results indicate personal O 3 exposures were not significa ntly 

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



### 10 significa nt correlations may be attributed in part to the relatively low personal O 3 

### 11 exposures observed in this study; in both summer and wint er, the mean personal O 3 

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

### 13

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

### 14 found differing results for the c orrelation between 24

- h avg personal O 3 and personal 

### 15

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

### 16 positive slope when regressing personal exposures of both total PM 2.5 (0.21) and PM 2.5 of 

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

### 18 negative slopes (

- 0.05 and

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

### 19 slope for personal PM 2.5 exposure versus person al O 3 exposure was much higher for 

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

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

### 22

between 24

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

. 

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

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

- specific correlations 

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

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

### 27
correlations (<

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

### 28
summer and near zero (Bos ton) or slightly negative (Baltimore) in the winter. These 

### 29 results indicate the potential effects of city

- specific characteristics, such as housing stock 

### 30 and building ventilation patterns, on relationships between O 3 and copollutants. 

### 31

The lack of long

- term exposure studies limits evaluation of long

- term correlations 

### 32

between O 3 exposure and copollutant exposure. However, some insight may be provided 

### 33 by an analysis of correlations between O 3 and other criteria pollutants, such as is 

### 34 provided in Section 3.6.4 . Warm

- season 8

- h daily max O 3 concentrations are generally 

### 35 positively correlated with co

- located 24

- h avg measurements of other criteria pollutants 

### 36
( Figure 3

- 57 ). Median correlations range from approximately 0.15 to 0.55 for CO, SO 2 , 

### 37

NO 2 , PM 10 , and PM 2.5 , in that order. In contrast, year

- round 8

- h daily max O 3 data show 

### 38

### Draft

– Do Not Cite or Quote 4

- 19 June 2012 negative median correlations with CO and NO 2 , positive correlations with PM 10 and 

### 1

PM 2.5 , and essentially zero correlation with SO 2 . This reflects mostly negative 

### 2

correlations between O 3 and all pollutants during wintertime, as shown in Figure 3

- 56

. 

### 3

Titration of O 3 near roadways also likely contributes to overall negative correlations with 

### 4

NO 2 and CO. Positive correlations between O 3 and PM 2.5 during the summertime can be 

### 5 partly explained by meteorological conditions favoring increased formation of both 

### 6 secondary PM and O 3 . Strong positive correlations can influence the interpretation of 

### 7 epidemiologic results, potentially complicati ng the ability to identify the independent 

### 8 effect of a pollutant. 

### 9

4.3.4.2 Near

- Road Exposure to Ozone and Copollutants

Beckerman et al. (2008 ) measured both 1

- week and continuous concentrations of O 3 , NO, 

### 10

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

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

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

### 13

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

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

### 15 influenced by small area sources. Site specific correlations are given in Figur e 4

- 2

. 

### 16

Correlations were

- 0.77 to

- 0.85 for NO 2 , - 0.48 to

- 0.62 for NO, and

- 0.55 to

- 0.63 for 

### 17

NO X . Pooled correlations using data from both sites were somewhat lower in magnitude. 

### 18

PM 2.5 and PM 1.0 correlations were

- 0.35 to

- 0.78 and

- 0.34 to

- 0.58, respectively. At the 

### 19 monitoring site not influenced by small area sources, O 3 - VOC correlations ranged from

- 

### 20

0.41 to

- 0.66. 

### 21

Beckerman et al. (2008 ) also made on

- road measurements of multiple pollutants with a 

### 22

instrume nted vehicle. Concentrations were not reported, but correlations between O 3 and 

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

### 24

than the near

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

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

### 26

O 3 when the data were controlled for site. Correlations for continuous concentrations 

### 27

between O 3 and co

- pollutants were somewhat lower than the 1

- week correlations, except 

### 28

for O 3 - PM 2.5 correlations. Correlations were

- 0.90,

- 0.66,

- 0.77, and

- 0.89 for NO 2 , NO, 

### 29

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

- 0.62, which is in 

### 30 the range of the 1

- week correlation. 

### 31

### Draft

– Do Not Cite or Quote 4

- 20 June 2012 Source: Data from: Beckerman et al. (2008 )



### Figure 4

- 2 Correlations between 1

- week concentrations of ozone and copollutants measured near roadways.

4.3.4.3 Indoor Exposure to Ozone and Copollutants

Ambient O 3 that infiltrates indoors reacts with organic compounds and other chemica ls to 

### 1 form oxidized products, as described in Section 3.2.3 as well as the 2006 O 3 AQCD. It is 

### 2 anticipated that individuals are exposed to these re action products, although no evidence 

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

### 4
in the ambient air, as summarized in Chapter 3 . For example, O 3 can react with terpenes 

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

### 6 the gas phase and on surfaces to form particulate and gaseous sp ecies, such as 

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

### 8
et al., 1995b ) . Ozone has also been shown to react with material trapped on HVAC filters 

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

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

, 

### 11 indicating tha t these reaction products may have health effects separate from those of O 3 

### 12

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

### 13 then go on to participate in additional reactions. White et al. (2 010 ) found evidence that 

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

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

### 16 are dependent on indoor O 3 concentration, temperature, a nd air exchange rate, making 

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

### 18 Copollutant

NO 2

NO

NO X

PM 2.5

PM 1.0

VOC

- 0.9

- 0.8

- 0.7

- 0.6

- 0.5

- 0.4

- 0.3

- 0.2

- 0.1 0

Pearson Correlation Coefficient

### Draft

– Do Not Cite or Quote 4

- 21 June 2012 

## 4.4 Exposure

- Related Metrics

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

### 1 but are not themselves direct measures of exposure. Tim e

- location

- activity patterns, 

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

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

### 4 exposure is represented by measurements at the monito rs, although factors other than 

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

### 6

4.4.1 Activity Patterns

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

### 7

Variation in O 3 concentrations among various microenvironments means that the amo unt 

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

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

### 10 explicitly accounted for in Equation 4

- 3 by the fraction of time spent in different 

### 11

microenvironments. 

### 12

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

### 13 variations in exposure across a population and o ver time. Large

- scale human activity 

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

### 15
(NHAPS) ( Klepeis et al., 2001 ) or the Consolidated Human Activity Database (CHAD) 

### 16
( McCurdy et al., 2000 ) , which includes NHAPS data together with other activity study 

### 17
results, have been de signed to characterize exposure patterns among much larger 

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

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

- 3 

### 20
( Klepeis et al., 2001 ) , which is presented to illustrate the diversity of daily activities 

### 21 among the entire populati on as well as the proportion of time spent in each 

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

### 23 in a vehicle between 2:00 and 3:00 p.m., when daily O 3 levels are peaking, although 

### 24 about half of this time was spent in or near a vehicle, where O 3 concentrations are likely 

### 25

to be lower than ambient concentrations. 

### 26

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

- 4 

### 27 summarizes NHAPS data reported for four age groups, termed Very Young (0

- 4 years), 

### 28 School Age (5

- 17 years), Working (18

- 64 years), and Retired (65+ years) ( Klepeis et al., 

### 29

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

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

### 31 somewhat more time outdoors than adults aged 18

- 64, with a greater fraction of time 

### 32

### Draft

– Do Not Cite or Quote 4

- 22 June 2012 spent outdoors at a residence. Children aged 0

- 4 also spent most of their outdoor time in a 

### 1 residential outdoor location. On averag e, the fraction of time spent outdoors by school 

### 2 age respondents was 2.62 percentage points higher than working respondents, 

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

### 4 accounting for activity level, this increased ti me spent outdoors is consistent with 

### 5

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

### 6

O 3 - related health effec ts. 

### 7

Table 4

- 4 Mean fraction of time spent in outdoor locations by various age groups in the NHAPS study

Age Group Residential

- Outdoor Other Outdoor Total Outdoors

0

- 4 yr 5.38% 0.96% 6.34%

5

- 17 yr 5.05% 2.83% 7.88%

18

- 64 yr 2.93% 2.33% 5.26%

65+ yr 4.48% 1.27% 5.75%

Source: Data from Klepeis et al. (1996 )

.

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

- 5 

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

### 9 presented in Table 6

- 2 of the EPA’s Exposure Factors Handbook ( U.S. EPA, 2011b )

. 

### 10 Most of the age

- related variability is seen for moderate and high intensity activities, 

### 11 except for individuals under 1 year. For moderate intensity, vent ilation rate increases with 

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

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

### 14 <11 years have ventilation rates of approximately 40 L/min, while children aged 11+ and 

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

### 16 to <61 age group, at 53 L/min, with lower ventilation rates for older adults. 

### 17

### Draft

– Do Not Cite or Quote 4

- 23 June 2012 Table 4

- 5 Mean ventilation rates (L/min) at different activity levels for different age groups.

Age Group Sleep or Nap Sedentary/Passive Light Intensity Moderate Intensity High Intensity

Birth to <1 yr 3.0 3.1 7.6 14 26

1 to <2 yr 4.5 4.7 12 21 38

2 to < 3 yr 4.6 4.8 12 21 39

3 to <6 yr 4.3 4.5 11 21 37

6 to <11 yr 4.5 4.8 11 22 42

11 to <16 yr 5.0 5.4 13 25 49

16 to <21 yr 4.9 5.3 12 26 49

21 to <31 yr 4.3 4.2 12 26 50

31 to <41 yr 4.6 4.3 12 27 49

41 to <51 yr 5.0 4.8 13 28 52

51 to <61 yr 5.2 5.0 13 29 53

61 to <71 yr 5.2 4.9 12 26 47

71 to <81 yr 5.3 5.0 12 25 47

81+ yr 5.2 4.9 12 25 48

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

.

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

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

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

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

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

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

### 6 factors tend to increase dose normalized to lung surface area. 

### 7

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

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

### 9 age, gender, employment, and lifestyle

- dependent factors. These differences may 

### 10 manifest as higher mea n exposures or more frequent high

- exposure episodes for some 

### 11 individuals. The extent to which longitudinal variability in individuals contributes to the 

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



### 13 person varia nce to total variance in time spent in different locations and activities (the 

### 14
intraclass correlation coefficient, ICC). Xue et al. (2004 ) quantified ICC values in time



### 15 activity data collected by Harvard University for 160 children aged 7

– 12 years in 

### 16

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

### 17 approximately 0.15, indicating tha t 15% of the variance in outdoor time was due to 

### 18

### Draft

– Do Not Cite or Quote 4

- 24 June 2012 between

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

### 1

groups. 

### 2

Source: Reprinted with permis sion of Nature Publishing Group ( Kle peis et al., 2001 )

.


### Figure 4

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

- min diary data.

The EPA’s National Exposure Research Laboratory (NERL) has con solidated the most 

### 3 important human activity databases into one comprehensive database called the 

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

### 5 data from nineteen human activity pattern studies (including NHAPS), which we re 

### 6 evaluated to obtain over 33,000 person

- days of 24

- h human activities in CHAD 

### 7
( McCurdy et al., 2000 )

. The surveys include probability

- based recall studies conducted 

### 8 by EPA and the California Air Resources Board, as well as real

- time diary studies 

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

- based and 

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

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

### 12 defined by start time, duration, activity type, and microenvironment classification 

### 13
(i.e., location). Ac tivities vary from one minute to one hour in duration, with longer 

### 14 activities being subdivided into clock

- hour durations to facilitate exposure modeling. 

### 15 CHAD also provides information on the level of exertion associated with each activity, 

### 16

### Draft

– Do Not Cite or Quote 4

- 25 June 2012 which can be us ed by exposure models, including the APEX model (Section 4.5.3 ), to 

### 1 estimate ventilation rate and pollutant dose. 

### 2

4.4.2 Ozone Averting Behavior

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

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

### 4 or time spent being active outdoors on high

- O 3 days. To assist the public in avoiding 

### 5 exposure to air pollution on d ays with high pollutant concentrations, EPA has developed 

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

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

### 8

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

### 9

O 3 (and other individual pollutants) in six color

- coded categories of air

- quality, ranging 

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

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

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

### 13 affected. For example, the orange level indic ates that the general population is not likely 

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

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

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

### 17 actual conditions typically are reported to the public during high

- O 3 months through local 

### 18 media outlets, using various versions of this air

- quality categorization scheme. People are 

### 19 advised to change th eir behavior to reduce exposure depending on predicted O 3 

### 20 concentrations and the likelihood of risk. Behavioral recommendations include being 

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

### 22 being active outdoors o n high

- O 3 days. Staying indoors to reduce exposure is only 

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

### 24

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

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

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

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

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

### 29

30 min on a c ode red O 3 day relative to a code green, yellow, or orange day; however, the 

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

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

### 32

children. Bresnahan et al. (1997 ) examined survey data collected during 1985

- 86 from a 

### 33 panel of adults in the Los Angeles are a, many of whom had compromised respiratory 

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

### 35

### Draft

– Do Not Cite or Quote 4

- 26 June 2012 with smog

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

- day 

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

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

### 3

when the then

- current standard (1

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

### 4 those individuals spent about 40 minu tes less time outside per day on a typical high O 3 

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

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

### 7

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

### 8 outdoor activity, has been found to vary based on health status. In the Bresnahan e t al. 

### 9
(1997 ) study, 40 percent of respondents reported staying indoors on days when air quality 

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

- related symptoms were more 

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

### 12 respiratory conditions did not have a statistically significa nt e ffect on behavior. A study 

### 13 of parents of asthmatic children ( McDe rmott et al., 2006 ) suggests that parents are aware 

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

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

### 16

recommendations of the alerts t o restrict children’s outdoor activity, with more parents of 

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

### 18 parents complied with more than one

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

### 19

2006 )

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

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

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

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

### 23 12%). Respondents who had received advice from a health professional to reduce outdoor 

### 24 activity when air quality is poor were more likely to rep ort a reduction based on media 

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

### 26
individuals in Houston, TX and Portland, OR, Semenza et al. (2008 ) found that a 

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

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

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

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

### 31 of the people in the Semenza et al. (2008 ) study r eported that their behavioral changes 

### 32 were motivated by self

- perception of poor air quality rather than an air quality advisory. 

### 33

It should be noted that the McDermott et al. (2006 )

, Wen et al. (2009 ) , and Semenza et al. 

### 34
(2008 ) studies evaluated air quality in general and therefore are not necessarily specific to 

### 35

O 3 . 

### 36

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

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

### 38

### Draft

– Do Not Cite or Quote 4

- 27 June 2012 necessarily result in a change in an individual’s travel behavior ( Henry and Gordon, 

### 1

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

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

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

. 

### 4

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

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

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

### 7 conditioners. As with change s in time spent outdoors, individuals who reported 

### 8 experiencing smog

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

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

### 10 behavioral changes, such as increas ed likelihood of averting behavior among high school 

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

### 12
attendance at two outdoor facilities (i.e., a zoo and an observatory) was reduced by 

### 13

6

- 13% on days when smog alerts wer e announced, with greater decreases observed 

### 14 among children and older adults ( Neidell, 2010

, 2009 )

. 

### 15

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

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

### 17 experiencing smog

- related symptoms reduce their time spent outdoors and are more 

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

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

### 20 Commuters, even when aware of air quality advisories, tend n ot to change their 

### 21 commuting behavior. 

### 22

4.4.3 Population Proximity to Fixed

- Site Ozone Monitors

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

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

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

### 25 relevant for exposures of exercising visito rs as well as those who live in similar locations. 

### 26 They also serve as an important source of data for evaluating ecological effects of O 3 

### 27
( Chapter 9 ). Rural monitors tend to be less affected than urban monitors by strong and 

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

### 29

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

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

### 31

variab ility in O 3 concentration than is observed in urban areas. 

### 32

A variety of factors determine the siting of the O 3 monitors that are part of the SLAMS 

### 33 network reporting to AQS. As discussed in Section 3.5.6 , the number and location of 

### 34

### Draft

– Do Not Cite or Quote 4

- 28 June 2012 required O 3 monitors in an urban area depend on O 3 concentration and population, among 

### 1
other factors. Areas classified as serious, severe, or extreme nonattainment have 

### 2

addi tional monitoring requirements. Generally, high

- O 3 urban areas with a population of 

### 3 50,000 or greater are required to have at least one monitor; in low

- or moderate



### 4 concentration areas, the minimum population for a required monitor is 350,000. Most 

### 5 large U .S. cities have several monitors, as shown in Figure 3

- 76 through Figure 3

- 95

. 

### 6

As an illustration of the location of O 3 monitors and their concentrations with respect to 

### 7 population density, Figure 4

- 4 through Figure 4

- 6 present this information for Atlanta, 

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

### 9

represent a cross

- section with respect to geographic distribution, O 3 concentration, 

### 10 layout, geographic features, and other factors. The maps show the location of O 3 

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

### 12 along with the 2007

- 09 mean 8

- h daily max O 3 concentration for perspective on the 

### 13

variation in O 3 con centration across the urban area. Population density at the census 

### 14 block group level is also presented on the maps. 

### 15

### Draft

– Do Not Cite or Quote 4

- 29 June 2012 Figure 4

- 4 Map of the Atlanta CSA including ozone monitor locations and major roadways with respect to census block group population density estimates for 2009.

### Draft

– Do Not Cite or Quote 4

- 30 June 2012 Figure 4

- 5 Map of the Boston CSA including ozone monitor locations and major roadways with respect to census block group population density estimates for 2009.

### Draft

– Do Not Cite or Quote 4

- 31 June 2012 Figure 4

- 6 Map of the Los Angeles CSA including ozone monitor locations and major roadways with respect to census block group population density estimates for 2009.

### Draft

– Do Not Cite or Quote 4

- 32 June 2012 Similarities and differences are apparent among the cities. The spatial distribution of 

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

### 2 population density area and other monitors in surrounding areas of lower population 

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

### 4
somewhat lower concentrations are observed at sites I and J, which are located >50 km 

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

### 6

low and some high concentrations in urban and less

- populated areas. The differences in 

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

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

### 9 influence of New York and other northeastern cities contributing to concentration 

### 10 variability. 

### 11

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

### 12 geography. There are a large number of monitors located in multiple levels of po pulation 

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

### 14 near at least moderate population density areas, but there are some high

- density areas 

### 15

without a monitor. Concentrations increase in a somewhat radial or west

- east pattern 

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

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

### 18 AG, AO,and AR), which have lower population density, but more po tential for ecological 

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

### 20 titration by NO X and other atmospheric constituents, while high downwind 

### 21 concentrations reflect lack of local sources and increased photochemical pro cessing time. 

### 22

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

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

### 24 with population exposure as a monitoring objective, and the mo nitoring requirements in 

### 25 40 CFR Part 58, Appendix D include population density as one of several factors that 

### 26 would be considered in designing the O 3 monitoring program for an area. At least one site 

### 27 for each MSA is designed to be a maximum concentration s ite, which could presumably 

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

### 29 required upwind and downwind of high

- concentration urban areas. 

### 30

All three cities have some high population density areas without an O 3 mon itor. The 

### 31 siting considerations for NCore monitors generally target the neighborhood (0.5

- 4 km) or 

### 32
urban (4

- 50 km) scale to provide representative concentrations throughout the 

### 33 metropolitan area; however, a middle

- scale (0.1

- 0.5 km) site may be acceptable in cases 

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

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

### 36 land use and atmospheric chemistry conditions are similar. This is supported by the 

### 37

### Draft

– Do Not Cite or Quote 4

- 33 June 2012 correlation analyses in Chapter 3 . For example, in Los Angeles, monitors H and L are 

### 1

located in medium

- density areas and show m oderately high correlation (0.78), although 

### 2 they are some 50 km apart. 

### 3

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

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

### 5 monitor repre sentativeness is to calculate the fraction of the urban population living 

### 6

within a certain radius of a monitor. Table 4

- 6 presents the fraction of the population in 

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

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

### 9 population to the effects of O 3 . The data indicate that relative ly few people live within 

### 10

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

### 11

20 km of a monitor. Looking at the results for a 5

- km radius, corresponding roughly to 

### 12 the neighborhood scale (Section 3.5.6.1 ), generally 20

- 30% of the population lives within 

### 13

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

### 14

suc h 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 surrou nding 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 po pulation 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 4

- 4 ) 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, whi ch 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. 

### 28

### Draft

– Do Not Cite or Quote 4

- 34 June 2012 Table 4

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

Population Within 1 km Within 5 km Within 10 km Within 20 km

City Total <18 yr Total <18 yr Total <18 yr Total <18 yr Total <18 yr

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

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

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

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

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

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

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

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

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

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

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

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

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

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

St. Louis CSA 2,914,754 720,746 1.3% 1.5% 17% 18% 52% 53% 80% 82%

Atlanta population fractions for children (<18 years of age) are similar to those for the 

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

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

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

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

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

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

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

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

### 9 potentially susceptible populations. 

### 10

### Draft

– Do Not Cite or Quote 4

- 35 June 2012 

## 4.5 Exposure Modeling

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

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

- level 

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

### 3 epidemiologic studies or as stand

- alone assessments of the level of exposure likely to be 

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

### 5 approaches used to improve exposure estimates, including concentration surface 

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

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

### 8 characteristics and meteorological parameters; and microenvironment

- based exposure 

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

### 10 pattern simulations to estimate time

- weig hted exposures based on concentrations in 

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

### 12

summarized in Table 4

- 7 . Th e remainder of this section provides more detail on specific 

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

### 14

### Draft

– Do Not Cite or Quote 4

- 36 June 2012 Table 4

- 7 Characteristics of exposure modeling approaches.

Model Type Model Description Strengths Limitations

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

- concentration monitor can skew results; no location

- activity information
Chemistry

transport (e.g., CMAQ) Grid

- based O 3 co ncentrations are calculated from precursor emissions, meteorology, and atmospheric chemistry and physics First

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

resource

- intensive; no location

- activity information

Land

- use regression (LUR) Merges concentration data with local

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

- scale spatial var iability of O 3 ; location

- specific, limiting generalizability; no location

- activity information

Air Exchange Rate Mechanistic (LBL, LBLX) Uses database on building leakage tests to predict AER based on building characteristics and meteorological variables (including natural ventilation in LBLX) Physical characterization of driving forces for air exchange Moderate resource requirement; no location

activity information

Empirical Predicts AER based on factors such as building age and floor area Low input data requirements Cannot account for meteorology; no location

activity information

Integrated Microenvironmental Exposure and Dose Population (APEX, SHEDS) Stochastic treatment of air quality data, demographic variables, and activity pattern to generate

e stimates 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 (A PEX) Resource

- intensive; evaluation with measured exposures; underestimation of multiple high

- exposure events in an individual (APEX)



### 4.5.1 Concentration Surface Modeling

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

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

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

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

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

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

### 6 different microenvironments. This is an important consideration in the ut ility of these 

### 7 methods for exposure assessment; while improved local

- scale estimates of outdoor 

### 8 concentrations may contribute to better assignment of exposures, information on activity 

### 9

### Draft

– Do Not Cite or Quote 4

- 37 June 2012 patterns is needed to produce estimates of personal exposure. There are three main types 

### 1 of approaches: spatial interpolation of measured concentrations; statistical models using 

### 2 meteorological variables, pollutant concentrations, and other predictors to estimate 

### 3 concentrations at receptors in the domain; and rigorous first

- p rinciple models, such as 

### 4 chemistry

- transport models or dispersion models incorporating O 3 chemistry. Some 

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

### 6 applied over urban, regional, or national spatial scales, and can b e used to estimate daily 

### 7 concentrations or longer

- term averages. This discussion will focus on short

- term 

### 8

concentrations estimated across urban areas. 

### 9

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



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

### 11 Christakos and Vyas, 1998a

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

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

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

### 14

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

### 15
concentrations in Vancouver, BC. The in vestigators assigned a daily average O 3 

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

- code centroids using one of the following 

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

### 18

of all monitors within 10 km; (3) the inve rse

- distance

- weighted (IDW) average of all 

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

### 20
Method 1 (the nearest

- monitor approach) and Method 4 (IDW

- 50 km) had similar mean 

### 21

and median estimated annual

- and monthly

- aver age concentrations, although the 10th



### 22 90th percentile range was smaller for IDW

- 50. This is consistent with the averaging of 

### 23

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

### 24 the two methods was 0.93 for monthly

- average co ncentrations and 0.78 for annual



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

- optimal and were excluded 

### 26 from further analysis. In the case of Method 2, a single downtown high

- concentration 

### 27 monitor skewed the results in the vicinity, partiall y as a result of the asymmetric layout of 

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

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

### 30 immediately adjacent to a monitoring site. CMA Q concentration estimates using a 

### 31

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

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

### 33

and Method 4 estimates for both annual and monthly avera ge concentrations. This may 

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

- roadway 

### 35

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

### 36 was similar between CMAQ and the interpolation techniques, b ut the monthly average 

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

### 38

### Draft

– Do Not Cite or Quote 4

- 38 June 2012 Bell (2006 ) compared CMAQ estimates for northern Georgia with nearest

- monitor and 

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

- weighted 

### 2

concentratio n estimates from CMAQ indicated areas of spatial heterogeneity that were 

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

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

### 5 exposure in c ertain situations, such as for rural areas. Using the concentration from the 

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

### 7

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

### 8 pollutants, p redominantly NO 2 , as a function of several land use factors, such as land use 

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

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

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

### 12

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

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

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

### 15
et al. (2008 ) warn of several limitations of LUR, including distinguishing real 

### 16 associations between pollutants and covariates fr om those of correlated copollutants, 

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

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

### 19 in epidemiologic studies. These limitation s may partially explain the lack of LUR models 

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

### 21

of LUR and IDW

- based spatial

- interpolation models in epidemiologic analyses for 

### 22 several different pollutants in Vancouver, B C and suggested that LUR is appropriate for 

### 23 directly

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

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

### 25 Although O 3 is also a secondary pollutant, its reactivity and high small

- scale spatial 

### 26 variability near high

- traffic roadways indicates this conclusion may not apply for O 3 . 

### 27

At a much larger spatial scale, EU

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

### 28

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

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

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

- km resolution), explaining a higher 

### 31

percen tage of the variability (R 2
= 0.62) than for other pollutants. However, at the urban 

### 32
scale (1

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

### 33 (high

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

### 34 kriging was the best method for the large

- scale composite EU concentration map, for O 3 

### 35

as well as for NO 2 and PM 10 , with an R 2 value for O 3 of 0.70. The authors noted that 

### 36 these methods were not designed to capture spatial variation in concentrations that are 

### 37

### Draft

– Do Not Cite or Quote 4

- 39 June 2012 know n to occur within tens of meters of roadways (Section 3.6.2.1 ), which could partially 

### 1 explain poor model performance at the urban scale. 

### 2

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

### 3 roadways. Mcconnell et al. (2006 ) developed a regression model to predict residential O 3 

### 4 concentrations in southern California using estimates of residential NO X calculated from 

### 5

traffi c data with the CALINE4 line source dispersion model. The annual average model 

### 6

results were well

- correlated (R 2
= 0.97) with multi

- year average monitoring data. The 

### 7

authors estimated that local traffic contributes 18% of NO X concentrations measured in 

### 8

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

### 9 regression model indicates that residential NO X reduces residential O 3 concentrations by 

### 10

0.51 ppb (SE 0.11 ppb) O 3 per 1 ppb NO X , and that a 10th

- 90th percentile increase in 

### 11

local NO X results in a 7.5 ppb decrease in local O 3 concentrations. This intra

- urban 

### 12

traffic

- related variability in O 3 concentrations suggests that traffic patterns are an 

### 13 important factor in the relationship between central site monitor and residential O 3 , and 

### 14 that differences in traffic density between the central site monitor and individual homes 

### 15

could result in either an overestimate or underestimate of residential O 3 . 

### 16

A substantial number of researchers have used geostatistical methods and chemistry



### 17

transpo rt models to estimate O 3 concentrations at urban, regional, national, and 

### 18
continental scales, both in the U.S. and in other countries (Section 3.3 ). In addition to 

### 19

short

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

### 20 for long

- term exposure assessment, O 3 forecasts, or evaluating emission control 

### 21 strategies. However, as discussed at the beginning of this section, cavea ts regarding the 

### 22 importance of activity pattern information in estimating personal and population exposure 

### 23 should be kept in mind. 

### 24

4.5.2 Residential Air Exchange Rate Modeling

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

### 25 an important mechanism for entry of ambient O 3 . As described in Section 4.3.2 , the 

### 26

indoor

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

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

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

### 29
such as APEX, SHEDS, and EMI (discussed in Section 4.5.3 ) ( U.S. EPA, 2011c

, 2009b ; 

### 30
Burke et al., 2001 ) . Since the appropriate AER measurements may not be available for 

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

- based) AER models can be 

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

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

### 34

### Draft

– Do Not Cite or Quote 4

- 40 June 2012 (e.g., window open ing), climatic region, and meteorology (e.g., local temperature and 

### 1 wind speed). Mechanistic AER models use these meteorological parameters to account 

### 2 for the physical driving forces of the airflows due to pressure differences across the 

### 3 building envelope from wind and indoor

- outdoor temperature differences ( A SHRAE, 

### 4

2009 ) . Empirical AER models do not consider the driving forces from the wind and 

### 5

indoor

- outdoor temperature differences. Instead, a scaling constant can be used based on 

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

. 

### 7

Single

- zone mechanistic models re present a whole

- building as a single, well

- mixed 

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

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

- building pressurization 

### 10

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

### 11
and McWilliams, 2007 ) . Recently, the LBL air infiltr ation model was linked with a 

### 12 leakage area model using population

- level census and residential survey data ( Sherm an 

### 13
and McWilliams, 2007 ) and individual

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

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

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

### 16

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

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

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

### 19

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

- predicted and 

### 20
measured AER, the median absolute difference was 43% (0.17 h - 1 ) and 40% (0.17 h - 1 ) for 

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

### 22 measurements (accuracy of 20

- 25% for occupied homes), these results demonstra te the 

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

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

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

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

### 27 predict indoor concentrations of ambient O 3 that may be subs tantial for conditions of high 

### 28 AER such as open windows. 

### 29

4.5.3 Microenvironment

- Based Models

Population

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

### 30 Human Exposure and Dose Simulation (SHEDS) integrated microenvironmental 

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

### 32

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

### 33

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

- related and 

### 34

individual

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

### 35

### Draft

– Do Not Cite or Quote 4

- 41 June 2012 breathing rate respectively, to compute the distribution of individual exposures across the 

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

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

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

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

### 5

time

- weighted exposure for modeled individuals by summing exposure in each 

### 6 microenvironment visited during the exposure period. 

### 7

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

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

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

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

### 11
factors. Microenvironments modeled include indoor residences; other indoor locations, 

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

### 13

mic roenvironments and exertion levels during the exposure period is determined from 

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

### 15 profile for each simulated individual by sampling from distributions of demographic 

### 16

variab les such as age, gender, and employment; physiological variables such as height 

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

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

### 19

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

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

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

### 22 expenditures, and the corresponding dose may then be computed. APEX has a n algorithm 

### 23

to estimate O 3 dose and changes in FEV 1 resulting from O 3 exposure. Summaries of 

### 24

individual

- and population

- level metrics are produced, such as maximum exposure or 

### 25 dose, number of individuals exceeding a specified exposure/dose, and number of 

### 26

p erson

- days at or above benchmark exposure levels. The models also consider the 

### 27 nonambient contribution to total exposure. Nonambient source terms are added to the 

### 28 infiltration of ambient pollutants to calculate the total concentration in the 

### 29

microenvironme nt. Output from model runs with and without nonambient sources can be 

### 30 compared to estimate the ambient contribution to total exposure and dose. 

### 31

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

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

### 33
Total Risk Studies) in a simulation of O 3 exposure in Philadelphia over a 2

- week period 

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

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

### 36 over the modeling domain were calculated from interpolation of photochemical modeling 

### 37

results and fixed

- site monitor concentrations. These concen trations were then used as 

### 38

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- 42 June 2012 input data for SHEDS. Median microenvironmental concentrations predicted by SHEDS 

### 1 for nine simulated microenvironments were strongly correlated with outdoor 

### 2
concentrations, a result consistent with the lack of indoor O 3 sources i n the model. A 

### 3 regression of median microenvironmental concentrations against outdoor concentrations 

### 4 indicated that the microenvironmental concentrations were appreciably lower than 

### 5 outdoor concentrations (regression slope = 0.26). 95th percentile microenv ironmental 

### 6

concentrations were also well correlated with outdoor concentrations and showed a 

### 7 regression slope of 1.02, although some microenvironmental concentrations were well 

### 8 below the outdoor values. This suggests that in most cases the high

- end concent rations 

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

### 10 exposure statistics for the population, their dose calculations also indicated that O 3 dose 

### 11 due to time spent outdoors dominated the upper percentiles of the popula tion dose 

### 12 distribution. They found that both the 50th and 95th percentile O 3 concentrations were 

### 13

correlated with census

- tract level outdoor concentrations estimated by photochemical 

### 14 modeling combined with spatiotemporal interpolation, and attributed this c orrelation to 

### 15

the lack of indoor sources of O 3 . Relationships between exposure and concentrations at 

### 16

fixed

- site monitors were not reported. 

### 17

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

### 18 uncertainty in input parameters an d databases to the uncertainty in model outputs 

### 19
( Langstaff, 2007 ) . The Monte Carlo analysis indicates that the uncertainty in model 

### 20 exposure estimates for asthmat ic children during moderate exercise is small to moderate, 

### 21

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

### 22
70, and 80 ppb (8

- h avg) However, APEX appears to substantially underestimate the 

### 23 frequency of multiple high

- ex posure events for a single individual. The two main sources 

### 24 of uncertainty identified were related to the activity pattern database and the spatial 

### 25 interpolation of fixed

- site monitor concentrations to other locations. Additional areas 

### 26

identified in the un certainty analysis for potential improvement include: further 

### 27 information on children’s activities, including longitudinal patterns in the activity pattern 

### 28 database; improved information on spatial variation of O 3 concentrations, including in 

### 29

near

- roadway and indoor microenvironments; and data from personal exposure monitors 

### 30 with shorter averaging times to capture peak exposures and lower detection limits to 

### 31 capture low indoor concentrations. A similar modeling app roach has been developed for 

### 32 panel epidemiologic studies or for controlled human exposure studies, in which activity 

### 33 pattern data specific to the individuals in the study can be collected. Time

- activity data is 

### 34 combined with questionnaire data on housing c haracteristics, presence of indoor or 

### 35 personal sources, and other information to develop a personalized set of model input 

### 36 parameters for each individual. This model, the Exposure Model for Individuals, has been 

### 37 developed by EPA’s National Exposure Researc h Laboratory ( U.S. EPA, 2011c ; 

### 38
Zartarian and Schultz, 2010 )

. 

### 39

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- 43 June 2012 

## 4.6 Implications for Epidemiologic Studies

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

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

### 2 an important contributor to variability in epidemiologic study results. Time

- series studies 

### 3

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

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

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

### 6 community

- averaged concentration of an air pollutant measured at central

- site monitors 

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

### 8 addition, panel studies, which consist of a relatively small sample (typ ically tens) of 

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

### 10

the health effects associated with short

- term exposure to ambient concentrations of air 

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

### 12

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

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

### 14 followed over several years or decades ( Jerrett et al., 2009 ) . Concentrations are gene rally 

### 15 aggregated over time and by community to estimate exposures. 

### 16

Exposure error can under

- or over

- estimate epidemiologic associations between ambient 

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

### 18
from the null, and tends to widen confidence intervals around those estimates ( She ppard 

### 19
et al., 2005 ; Zeger et al., 2000 ) . Exposure misclassification can also tend to obscure the 

### 20 presence of potential thresholds for health effects, as demonstrated by a simulation study 

### 21 of nondifferential exposure misclassification ( Brauer et al., 2002 ) . The importance of 

### 22 exposure misclassification varies with study design and is dependent on the spatial and 

### 23

t emporal aspects of the design. For example, the use of a community

- averaged O 3 

### 24

concentration in a time

- series epidemiologic study may be adequate to represent the day



### 25

to

- day temporal concentration variability used to evaluate health effects, but may not 

### 26

ca pture differences in the magnitude of exposure due to spatial variability. Other factors 

### 27 that could influence exposure estimates include nonambient exposure, topography of the 

### 28 natural and built environment, meteorology, measurement errors, use of ambient O 3 

### 29 concentration as a surrogate for ambient O 3 exposure, and the presence of O 3 in a mixture 

### 30 of pollutants. The following sections will consider various sources of error and how they 

### 31 affect the interpretation of results from epidemiologic studies of differe nt designs. 

### 32

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- 44 June 2012 4.6.1 Non

- Ambient Ozone Exposure

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

### 1 personal exposure. There are relatively few indoor sources of O 3 ; as a result, personal O 3 

### 2 exposure is expected to be dominate d by ambient O 3 in outdoor microenvironments and 

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

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

### 5

with an O 3 generator, this nonambient exposure is unlikely to be temporally correlated 

### 6

with ambient O 3 exposure ( Wilson and Suh, 1997 ) , and therefore would not affect 

### 7 epidemiologic associations between O 3 and a health effect ( Sheppard et al., 2005 ) . In 

### 8 simulations of a nonreactive pollutant, Sheppard et al. (2005 ) concluded that nonambient 

### 9 exposure does not influence the health outcome effec t estimate if ambient and 

### 10 nonambient concentrations are independent. Since personal exposure to ambient O 3 is 

### 11
some fraction of the ambient concentration, it should be noted that effect estimates 

### 12 calculated based on personal exposure rather than ambient con centration will be 

### 13 increased in proportion to the ratio of ambient concentration to ambient exposure, and 

### 14 daily fluctuations in this ratio can widen the confidence intervals in the ambient 

### 15 concentration effect estimate, but uncorrelated nonambient exposure will not bias the 

### 16

effect estimate ( Sheppard et al., 2005 ; Wilson and Suh, 1997 )

. 

### 17

4.6.2 Spatial and Temporal Variability

Spatial and temporal variability in O 3 concentrations can contribute to exposure error in 

### 18 epid emiologic studies, whether they rely on central

- site monitor data or concentration 

### 19 modeling for exposure assessment. Spatial variability in the magnitude of concentrations 

### 20 may affect cross

- sectional and large

- scale cohort studies by undermining the assumpt ion 

### 21

that intra

- urban concentration and exposure differences are less important than inter



### 22 urban differences. This issue may be less important for time

- series studies, which rely on 

### 23 day

- to

- day temporal variability in concentrations to evaluate health effect s. Low inter



### 24 monitor correlations contribute to exposure error in time

- series studies, including bias 

### 25

toward the null and increased confidence intervals. 

### 26

4.6.2.1 Spatial Variability

Spatial variability of O 3 concentrations is highly dependent on spatial scale; in effect, O 3 

### 27 is a regional pollutant subject to varying degrees of local variability. In the immediate 

### 28 vicinity of roadways, O 3 concentrations are reduced due to reaction with NO and other 

### 29 species (Section 4.3.4.2 ); over spatial scales of a few kilometers, O 3 may be more 

### 30

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– Do Not Cite or Quote 4

- 45 June 2012 homogeneous due to its formation as a secondary pollutant; over scales of tens of 

### 1 kilometers, atmospheric processing can result in hi gher concentrations downwind of an 

### 2

urban area than in the urban core. Local

- scale variations have a large impact on the 

### 3 relative magnitude of concentrations among urban monitors, while conditions favoring 

### 4 high or low rates of O 3 formation (e.g., temperatur e) vary over large spatial scales. This 

### 5 suggests that neighborhood monitors are likely to track one another temporally, but miss 

### 6

small

- scale spatial variability in magnitude. This is supported by an analysis in Atlanta 

### 7 that found correlations greater than 0.8 for daily O 3 concentration metrics (1

- h max, 

### 8

8

- h max, and 24

- h avg) measured at monitors 10

- 60 km apart ( Darrow et al., 2011a ) . In 

### 9 rural areas, a lower degree of fluctuation in O 3 precursors such as NO and VOCs is likely 

### 10 to make the diel concentration profile less varia ble than in urban areas, resulting in more 

### 11 sustained ambient levels. Spatial variability contributes to exposure error if the ambient 

### 12

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

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

### 14
residence and/or worksite (in the absence of indoor O 3 sources). Averaging data from a 

### 15 large number of samplers will dampen intersampler variability, and use of multiple 

### 16 monitors over smaller land areas may all ow for more variability to be incorporated into 

### 17 an epidemiologic analysis. 

### 18

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

### 19 several subcommunities having different sources and topographies, such as the 

### 20

Los Angeles CSA (S ection 3.6.2.1 and Section 4.4.3 ). Ozone monitors in Los Angeles 

### 21 had a much wider range of intermonitor correlations (

- 0.06 to 0.97) than Atlanta (0.61 to 

### 22 0.96) or Boston (0.56 to 0.97) using 2007

- 2009 data. Although the neg ative and near

- zero 

### 23

correlations in Los Angeles were observed for monitors located some distance apart 

### 24
(>150 km), some closer monitor pairs had low positive correlations, likely due to changes 

### 25 in land use, topography, and airflow patterns over short distan ces. Lower COD values, 

### 26 which indicate less variability among monitors in the magnitude of O 3 concentrations, 

### 27
were observed in Atlanta (0.05

- 0.13) and Boston (0.05

- 0.19) than Los Angeles 

### 28
(0.05

- 0.56), although a single monitor (AM) was responsible for all Lo s Angeles COD 

### 29 values above 0.40. The spatial and temporal variability in O 3 concentration in 24 MSAs 

### 30

across the U.S. was also examined in the 2006 O 3 AQCD by using Pearson correlation 

### 31 coefficients, values of the 90th percentile of the absolute difference i n O 3 concentrations, 

### 32 and CODs. No clear discernible regional differences across the U.S. were found in the 

### 33 ranges of parameters analyzed. 

### 34

An analysis of the impact of exposure error due to spatial variability and instrument 

### 35 imprecision on time

- series epid emiologic study results indicated that O 3 has relatively 

### 36 low exposure error compared to other routinely monitored pollutants, and that the 

### 37 simulated impact on effect estimates is minor. Goldman et al. (2011 ) computed 

### 38

### Draft

– Do Not Cite or Quote 4

- 46 June 2012 population

- weighted scaled semivariances and Pearson correlation coefficients for daily 

### 1

concentr ation metrics of twelve pollutants measured at multiple central

- site monitors in 

### 2

Atlanta. 8

- h daily max O 3 exhibited the lowest semivariance and highest correlation of 

### 3 any of the pollutants. Although this indicates some degree of urban

- scale homogeneity 

### 4

fo r O 3 , the analysis did not account for near

- road effects on O 3 concentrations. 

### 5

Studies evaluating the influence of monitor selection on epidemiologic study results have 

### 6

found that O 3 effect estimates are similar across different spatial averaging scales a nd 

### 7 monitoring sites. A study in Italy compared approaches for using fixed

- site monitoring 

### 8

data in a case

- crossover epidemiologic study of daily O 3 and mortality ( Zauli Sajani et 

### 9
al., 2011 ) . O 3 effect estimates were found to be similar whether the nearest monitor was 

### 10 used, or whether single

- city, three

- city, or six

- city regional averages were used for 

### 11 exposure assessment. In contrast, ef fect estimates for PM 10 and NO 2 increased with 

### 12 increasing scale of spatial averaging. Confidence intervals increased with increasing 

### 13 spatial scale for all pollutants. The authors attributed the consistency of O 3 effect 

### 14 estimates to the relative spatial hom ogeneity of O 3 over multi

- km spatial scales, and 

### 15 pointed to the high (0.85

- 0.95) inter

- monitor correlations to support this. The use of 

### 16 background monitors rather than monitors influenced by local sources in this study 

### 17 suggests that local

- scale spatial var iation in O 3 , such as that due to titration by traffic 

### 18 emissions, was not captured in the analyses. A multi

- city U.S. study of asthmatic children 

### 19 found comparable respiratory effect estimates when restricting the analysis to the 

### 20

monitors closest to the chi ld’s zip code centroid as when using the average of all 

### 21

monitors in the urban area ( Mortimer et al., 2 002 ) , suggesting little impact of monitor 

### 22

selection. Sarnat et al. (2010 ) studied the spatial variability of O 3 , along with PM 2.5 , NO 2 , 

### 23 and CO, in the Atlanta, GA, metropolitan area and evaluated how spatial variability 

### 24 affects interpretation of epidemiologic results, using time

- series data for circulatory 

### 25

disease ED visits. The authors found that associations with ambient 8

- h daily maximum 

### 26

O 3 concentration were similar among all sites tested, including multiple urban sites and a 

### 27 rural site some 38 miles from the city center. This result was also observed for 24

- h PM 2.5 

### 28 concentrations. In contrast, hourly CO and NO 2 showed different associations for the 

### 29 rural site than the urban sites, although the urban site associations were similar to one 

### 30 another for CO. This suggests that the choice of monitor may have little impact on the 

### 31

results of O 3 time

- series studies, consistent with the moderate to high inter

- monitor 

### 32
correlations observed in Atlanta ( Chapter 3 ). 

### 33

One potential explanation for this finding from the study by Sarnat et al. (2010 ) is that 

### 34 although spatial variability at different scales contributes to a complicated pattern of 

### 35 variations in the magnitude of O 3 concentrations between near

- road, urb an core, and 

### 36 urban downwind sites, day

- to

- day fluctuations in concentrations may be reflected across 

### 37 multiple urban microenvironments. In addition, time

- averaging of O 3 and PM 2.5 

### 38

### Draft

– Do Not Cite or Quote 4

- 47 June 2012 concentrations may smooth out some of the intra

- day spatial variability obser ved with the 

### 1 hourly CO and NO 2 concentrations. However, some uncertainty in observed effect 

### 2 estimates due to spatial variability and associated exposure error is expected to remain, 

### 3 including a potential bias towards the null. 

### 4

4.6.2.2 Seasonality

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

### 5 vary by season, with at least three factors potentially contributing to this variation: 

### 6 differences in building ventilation (e.g., air conditioning or heater use versus open 

### 7

window vent ilation), higher O 3 concentrations during the O 3 season contributing to 

### 8 increased exposure and improved detection by personal monitors; and changes in activity 

### 9 pattern resulting in more time spent outside. Evidence has been presented in studies 

### 10

conducted i n several cities regarding the effect of ventilation on personal

- ambient and 

### 11

indoor

- outdoor O 3 relationships (see Section 4.3.2 and Section 4.3.3 ). More limited 

### 12 evidence is available regarding the specific effects of O 3 detection limits and activity 

### 13 pattern changes on O 3 relationships. 

### 14

Several studies have found increased summertime correlations or ratios between personal 

### 15 exposure and ambient concentration ( Sarnat et al., 2005 ; Sarnat et al., 2000 ) or between 

### 16

indoor and outdoor O 3 concentrations ( Geyh et al., 2000 ; Avol et a l., 1998a ) . However, 

### 17 others have found higher rati os in fall than in summer ( Sarnat et al., 2006a ) or equivalent, 

### 18

near

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

### 19 use of air conditioners decreases building air exchange rates. It should be noted that O 3 

### 20 concentrations during winter are generally much lower than summertime concentrations, 

### 21 possibly obscuring wintertime relationshi ps due to detection limit issues. Studies 

### 22 specifically evaluating the effect of ventilation conditions on O 3 relationships have found 

### 23 increased correlations or ratios for individuals or buildings experiencing higher air 

### 24 exchange rates ( Sarnat et al., 2006a ; Geyh et al., 2000 ; Sarnat et al., 2000 ; Romieu et al., 

### 25

1998a )

. 

### 26

Increased correlations or ratios between personal exposure and ambient conc entration, or 

### 27 between indoor and outdoor concentration, are likely to reduce error in exposure 

### 28 estimates used in epidemiologic studies. This suggests that studies conducted during the 

### 29

O 3 season or in periods when communities are likely to have high air exc hange rates 

### 30 (e.g., during mild weather) may be less prone to exposure error than studies conducted 

### 31 only during winter. Year

- round studies that include both the O 3 and non

- O 3 seasons may 

### 32 have an intermediate level of exposure error. 

### 33

### Draft

– Do Not Cite or Quote 4

- 48 June 2012 4.6.3 Exposure Duration

Epidem iologic studies of health effects associated with short

- term and long

- term 

### 1 exposures use different air pollution metrics and thus have different sources of exposure 

### 2 error. The following subsections discuss the impact of using different short

- term and 

### 3 long

- term exposure metrics on epidemiologic results. 

### 4

4.6.3.1 Short

- Term Exposure

The averaging time of the daily exposure metrics used to evaluate daily aggregated health 

### 5 data (e.g., 1

- h or 8

- h daily maximum vs. 24

- h avg concentration) may also impact 

### 6 epidemiologic results, since different studies report different daily metrics. Correlations 

### 7

between 1

- h daily max, 8

- h daily max, and 24

- h avg concentrations for U.S. monitoring 

### 8 sites are presented in Section 3.6.1 ( Figure 3

- 23 and accompanying text). The two daily 

### 9 peak values (1

- h max and 8

- h max) are well correlated, with a median (IQR) correlation 

### 10
of 0.97 (0.96

- 0.98). The correlation between the 8

- h max and 24

- h avg are somewhat less 

### 11
well correlated with a median (IQR) correlation of 0.89 (0.86

- 0.92). While this may 

### 12 complicate quantitative comparisons between epidemiologic studies using different daily 

### 13 metrics, as well as the interpretation of studies using metric s other than the current 8

- h 

### 14 standard, the high inter

- metric correlations suggest it is a relatively small source of 

### 15 uncertainty in comparing the results of studies using different metrics. This is supported 

### 16 by a study comparing each of these metrics in a time

- series study of respiratory ED visits 

### 17
( Darrow et al., 2011a ) , which found positive associations for all metrics, with the 

### 18 strongest association for the 8

- h daily max exposure metric (Section 6.2.7.3 ). 

### 19

The ratios of 1

- h daily max, 8

- h daily max, and 24

- h avg concentrations to one another 

### 20

have been found to differ across communities and across time within individual 

### 21

communities ( Anderson and Bell, 2010 ) . For example, 8:24 hour ratios ranged from 

### 22

1.23

- 1.83, with a median of 1.53. Lower ratios were generally observed in the spring and 

### 23 summer compared to fall and winter. O 3 concentration was identified as the most 

### 24 importa nt predictor of ozone metric ratios, with higher overall O 3 concentrations 

### 25 associated with lower ratios. In communities with higher long

- term ozone concentrations, 

### 26

the lower 8:24 hour ratio is attributed to high baseline O 3 , which results in elevated 24

- h 

### 27 average values. Differences in the representativeness of O 3 metrics introduces uncertainty 

### 28 into the interpretation of epidemiologic results and complicates comparison of studies 

### 29 using different metrics. Preferably, studies will report results using multipl e metrics. In 

### 30 cases where this does not occur, the results of the study by Anderson and Bell (2010 ) can 

### 31 inform the uncertainty associated with using a standard increment to adjust effect 

### 32

estimat es based on different metrics so that they are comparable (Chapter 6 ). 

### 33

### Draft

– Do Not Cite or Quote 4

- 49 June 2012 A study compared measures of spatial and temporal variability for 1

- h daily m ax and 

### 1

24

- h daily avg O 3 concentrations in Brazil ( Bravo and Bell, 2011 ) . The 1

- h daily max 

### 2 value was found to have higher correlation between monitors (i.e., lower temporal 

### 3 variability) and lower COD (a measure of spatiotemporal variability which incorporates 

### 4 differences in concentration magnitude, with lower values indicating lower variability; 

### 5

see Chapter 3 ) than the 24

- h avg value. The range of correlation coefficients and COD 

### 6
values was similar between the two metrics, altho ugh the variation was lower for the 1

- h 

### 7 daily max, as indicated by the R 2 value for the regression of correlation coefficient on 

### 8

inter

- monitor distance. 

### 9

4.6.3.2 Long

- Term Exposure

Long

- term O 3 exposure studies are not available that permit evaluation of the relati onship 

### 10 between long

- term O 3 concentrations and personal or population exposure. The value of 

### 11

short

- term exposure data for evaluating long

- term concentration

- exposure relationships is 

### 12 uncertain. If the longer averaging time (annual vs. daily or hourly) smoo ths out short



### 13 term fluctuations, long

- term concentrations may be well

- correlated with long

- term 

### 14 exposures. However, lower correlation between long

- term exposures and ambient 

### 15 concentration could occur if important exposure determinants change over a period of 

### 16 several years, including activity pattern and residential air exchange rate. 

### 17

A study in Canada suggests that an exposure metric based on a single year can represent 

### 18 exposure over a multi

- decade period. The authors compared exposure assessment 

### 19

methods fo r long

- term O 3 exposure and found that the annual average concentration in 

### 20 the census tract of a subject’s residence during 1980 and 1994 was well

- correlated (0.76 

### 21 and 0.82, respectively) with a concentration metric accounting for movement among 

### 22

census sub divisions during 1980

- 2002 ( Guay et al., 2011 ) . This may have been due in 

### 23 part to a relatively low rate of movement, with subjects residing on average for 71% of 

### 24

the 22

- year period in the same census subdivision they were in during 1980. 

### 25

Analysis of the exposure assessment methodology in a recent study of mortality 

### 26 associated with long

- term O 3 exposure ( Jerrett et al., 2009 ) is illustrative. In this study, 

### 27 the authors computed quarterly averages of the daily 1

- h max O 3 co ncentration, averaged 

### 28 the two summer quarters together to produce an annual value, then calculated a 23

- year 

### 29 average value for each city in the study. Producing a single value for each city enables a 

### 30 comparison of relatively cleaner cities with relatively more polluted cities. In this case, 

### 31 the average was calculated using the 1

- h daily max value; if the 24

- h avg value had been 

### 32 used, concentrations would have been lower and potentially more variable, based on 

### 33 analyses in Chapter 3 . According to 

### 34

### Draft

– Do Not Cite or Quote 4

- 50 June 2012 Table 3

- 7 , the 2007

- 2009, 3

- year average 1

- h daily max value during the warm season 

### 1 was approximately 50% higher than the corresponding 24

- avg value on a nationwide 

### 2

basis. Correlation between the two m etrics varies by site, indicating the differential 

### 3 influence of the overnight period on 24

- h avg concentrations. The median correlation 

### 4

between 1

- h daily max and 24

- h avg is 0.83, with an IQR of 0.78

- 0.88. It is not clear, 

### 5 however, that a different exposur e assignment method would yield different results. 

### 6

Long

- term O 3 trends, as discussed in Chapter 3 , show gradually decreasing 

### 7

concentrations. Figure 3

- 48 shows that concentrations have decreased most for the 90th 

### 8 percentile, with relatively little change among the 10th percentile monitors. The decr ease 

### 9 has been greater in the eastern U.S. than in the western part of the country (excluding 

### 10 California). For the most part, the rank order of regions in terms of O 3 concentration has 

### 11
remained the same, as shown in Figure 3

- 50 , with the Northeast, Southeast, and 

### 12 California exhibiting the highest concentrations. The decreasing trend is consistent across 

### 13 nearly all monitors in the U.S., with only 1

- 2% of monitors reporting an increase of more 

### 14

than 5 ppb between the 2001

- 2003 and 2008

- 2010 time periods ( Figure 3

- 52 and 

### 15 Figure 3

- 53 ). This provides some evidence that epidemiologic studies of long

- term 

### 16 exposure are not affected by drastic changes in O 3 concentration, such as a relatively 

### 17 clean city becoming highly polluted or the reverse. 

### 18

A few epidemiologic studies have evaluated the impact of distance to monitor on 

### 19 associations between long

- term O 3 concentration and reproductive outcomes, as 

### 20 discussed in Chapter 7 . It is not clear from this evidence whether using a local monitor 

### 21

for these multi

- month concentration averages improves exposure assessment. Jalaludin et 

### 22
al. (2007 ) found somewhat higher effect estimates for women living within 5 km of a 

### 23

fixed

- site O 3 monitor than for all women in the Sydney metropolitan area, suggesting that 

### 24 increased monitor proximity reduced exposure misclassification. In contrast, Darrow et 

### 25
al. (2011b ) found no substantial difference between effect esti mates for those living 

### 26

within 4 mi of a fixed

- site monitor and those living in the five

- county area around 

### 27 Atlanta. This result could be due to spatial variability over smaller scales than the 4

- mi 

### 28 radius evaluated, time spent away from the residence impac ting O 3 exposure, or 

### 29 similarity in monitor location and representativeness across the urban area (see 

### 30 Figure 4

- 4 ). At this time, the effect of exposure error on long

- term exposure 

### 31 epidemiologic studies is unclear. 

### 32

4.6.4 Exposure to Copollutants and Ozone Reaction Products

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

### 33

same reactions that reduce O 3 indoors form part iculate and gaseous species, including 

### 34

### Draft

– Do Not Cite or Quote 4

- 51 June 2012 other oxidants, as summarized in Section 4.3.4.3 . Exposures to these reaction products 

### 1

would therefore be ex pected to be correlated with ambient O 3 concentrations, although no 

### 2 evidence was identified regarding personal exposures. Such exposure could potentially 

### 3 contribute to health effects observed in epidemiologic studies. 

### 4

4.6.5 Averting Behavior

As described in Sect ion 4.4.2 , several recent studies indicate that some populations alter 

### 5 their behavior on high ozone days to avoid exposure. Such behavioral respons es to 

### 6 information about forecasted air quality may introduce systematic measurement error in 

### 7 air pollution exposure, leading to biased estimates of the impact of air pollution on health. 

### 8 For example, studies have hypothesized that variation in time spent o utdoors may be a 

### 9 driving factor behind the considerable heterogeneity in ozone mortality impacts across 

### 10

communities ( Bell et al., 2004 ) . If averting behavior reduces outdoor O 3 exposure, then 

### 11 studies that do not account for averting behavior may produce effect estimates that are 

### 12

bia sed towards the null (Section 6.2.7.2 ). 

### 13

This is supported by an epidemiologic study that examined the association between 

### 14 exposure to ambient ozone concentrations and asthma hospitalizations in Southern 

### 15 California during 1989

- 1997, which indicates that controlling for avoidance behavior 

### 16 increases the effect estimate for both children and older adults, but not for adults aged 

### 17

20

- 64 ( Neidell and Kinney, 2010 ; Neidell, 2009 )

. Figure 4

- 7 and Figure 4

- 8 , reproduced 

### 18

from Neidell (2009 ) , show covariate

- adjusted asthma hospital admissio ns as a function of 

### 19 daily maximum 1

- h O 3 concentration for all days (gray line) and days when no O 3 alert 

### 20 was issued (black line). Stage 1 smog alerts were issued by the State of California for 

### 21 days when ambient O 3 concentrations were forecast to be above 0.20 ppm; however, the 

### 22

concentration

- response functions are based on measured O 3 concentrations. For children 

### 23 aged 5

- 19 ( Figure 4

- 7 ), hospital admissio ns were higher on high

- O 3 days when no alert 

### 24 was issued, especially on days with O 3 concentrations above 0.15 ppm (150 ppb). The 

### 25

concentration

- response curves for all days and days with no alert diverge at measured O 3 

### 26

concentrations between 0.10 and 0.15 p pm because smog alerts begin to be issued more 

### 27 frequently in this range. This suggests that in the absence of information that would 

### 28 enable averting behavior, children experience higher ozone exposure and subsequently a 

### 29 greater number of asthma hospital ad missions than on alert days with similar O 3 

### 30 concentrations. The lower rate of admissions observed when alert days were included in 

### 31 the analysis suggests that averting behavior reduced O 3 exposure and asthma hospital 

### 32
admissions. In both cases, O 3 was found to be associated with asthma hospital 

### 33 admissions, although the strength of the association is underestimated when not 

### 34 accounting for averting behavior. A different result was observed when examining 

### 35

### Draft

– Do Not Cite or Quote 4

- 52 June 2012 associations for adults aged 20

- 64 ( Figure 4

- 8 ), who had similar rates of hospital 

### 1

admissions on non

- alert days as on all days. The lack of change for adults aged 20

- 64, 

### 2 which is primary employment age, may reflect lower response to air quality alerts due to 

### 3 the increased opportunity cost of behavior change. The finding that air quality 

### 4 information reduces the daily asthma hospitalization rate in these populations provides 

### 5 additional support for a link betwee n ozone and health effects. 

### 6

Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Pres s ( Neidell, 2009 )

.


### Figure 4

- 7 Adjusted asthma hospital admissions by age on lagged ozone by alert statu s, ages 5

- 19.

### Draft

– Do Not Cite or Quote 4

- 53 June 2012 Source: Reprinted with permission of the Board of Regents of the University of Wisconsin System, University of Wisconsin Pres s; Neidell (2009 )

.


### Figure 4

- 8 Adjusted asthma hospital admissions by age on lagged ozone by alert statu s, ages 20

- 64.

4.6.6 Exposure Estimation Methods in Epidemiologic Studies

Epidemiologic studies use a variety of methods to assign exposure. Study design, data 

### 1 availability, and research objectives are all important factors for epidemiologists when 

### 2 selecting an exposure assessment method. Common methods for assigning exposure 

### 3 using monitoring data include using a single fixed

- site monitor to represent population 

### 4 exposure, averaging concentrations from multiple monitors, and selecting the closest 

### 5 monitor. Investig ators may also use statistical adjustment methods, such as trimming 

### 6 extreme values, to prepare the concentration data set. Panel or small

- scale cohort studies 

### 7 involving dozens of individuals may use more individualized concentration 

### 8 measurements, such as p ersonal exposures, residential indoor or outdoor measurements, 

### 9 or concentration data from local study

- specific monitors. For long

- term epidemiologic 

### 10 studies, the lack of personal exposure data or dedicated measurements means that 

### 11 investigators must rely on fixed

- site monitoring data. These data may be used directly, 

### 12 averaged across counties or other geographic areas, or used to construct geospatial or 

### 13 regression models to assign concentrations to unmonitored locations. Longer

- term 

### 14 averages (months to years) are typically used (e.g., in studies discussed in 

### 15

Section 7.3.1.1 ). Chapters 6 and 7 describe the exposure assessment methods used in the 

### 16

### Draft

– Do Not Cite or Quote 4

- 54 June 2012 epidemiologic studies described therein, providing additional detail on studies with 

### 1 innovative or expanded techniques designed to improve exposure assessment and reduce 

### 2

exposure error. 

### 3

The use of O 3 measurements from central ambient monitoring sites is the most common 

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

- site 

### 5

measurements do not account f or the effects of spatial variation in O 3 concentration, 

### 6

ambient and non

- ambient concentration differences, and varying activity patterns on 

### 7 personal exposures ( Brown et al., 2009 ; Chang et al., 2000 ; Zeger et al., 2000 ) . The use 

### 8

of fixed

- site concentrations results in minimal exposure error when: (1) O 3 concentrations 

### 9

a re uniform across the region; (2) personal activity patterns are similar across the 

### 10 population; and (3) housing characteristics, such as air exchange rate and indoor reaction 

### 11 rate, are constant over the study area. Since these factors vary by location and population, 

### 12 there will be errors in the magnitude of total exposure based solely on ambient 

### 13 monitoring data. 

### 14

Modeled concentrations can also be used as exposure surrogates in epidemiologic studies, 

### 15

as discussed in Section 4.5 . Geostatistical spatial interpolation techniques, such as IDW 

### 16 and kriging, can provide finer

- scale estimates of local concentration over urban areas. A 

### 17

microenvironmental modeli ng approach simulates exposure using empirical distributions 

### 18 of concentrations in specific microenvironments together with human activity pattern 

### 19 data. The main advantage of the modeling approach is that it can be used to estimate 

### 20 exposures over a wide ran ge of population and scenarios. A main disadvantage of the 

### 21 modeling approach is that the results of modeling exposure assessment must be compared 

### 22 to an independent set of measured exposure levels ( Klepeis, 1999 ) . In addition, 

### 23

resource

- intensive development of validated and representative model inputs is required, 

### 24 such as human activity patterns, distributions of air exchange rate, and deposition rate. 

### 25 Therefore, modeled exposures are used much less frequently in epidemiologic s tudies. 

### 26



## 4.7 Summary and Conclusions

This section will briefly summarize and synthesize the main points of the chapter, with 

### 27 particular attention to the relevance of the material for the interpretation of epidemiologic 

### 28

studies. 

### 29

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

### 30 exposure (Section 4.3.1 ). The detection limit of the badges for a 24

- h sampling period is 

### 31

appro ximately 5

- 10 ppb, with lower detection limits at longer sampling durations. In low



### 32 concentration conditions this may result in an appreciable fraction of 24

- h samples being 

### 33 below the detection limit. The use of more sensitive portable active monitors, inc luding 

### 34

### Draft

– Do Not Cite or Quote 4

- 55 June 2012 some that have recently become available, may help overcome this issue and improve 

### 1 personal monitoring in the future. 

### 2

Since there are relatively few indoor sources of O 3 , indoor O 3 concentrations are often 

### 3 substantially lower than outdoor concentrat ions due to reactions of O 3 with indoor 

### 4
surfaces and airborne constituents (Section 4.3.2 ). Air exchange rate is a key determinant 

### 5

of the I/O ratio , which is generally in the range of 0.1

- 0.4 ( Table 4

- 1 ), with some 

### 6 evidence for higher ratios during the O 3 season when concentrations are higher. 

### 7

Per sonal exposure is moderately correlated with ambient O 3 concentration, as indicated 

### 8 by studies reporting correlations generally in the range of 0.3

- 0.8 ( Table 4

- 2 ). Hourly 

### 9 concentration correlations are more variable than those averaged over 24 hours or longer, 

### 10
with correlations in outdoor microenvironments (0.7

- 0.9) much higher than those in 

### 11
residential indoor (0.1) or other indoor (0.3

- 0.4) microenvir onments. Some studies report 

### 12 substantially lower personal

- ambient correlations, a result attributable in part to low air 

### 13 exchange rate and O 3 concentrations below the sampler detection limit, conditions often 

### 14 encountered during wintertime. Low correlations may also occur for individuals or 

### 15 populations spending substantial time indoors. 

### 16

The ratio between personal exposure and ambient concentration varies widely depending 

### 17 on activity patterns, housing characteristics, and season, with higher personal

- ambient 

### 18 ratios generally observed with increasing time spent outside, higher air exchange rate, 

### 19
and in seasons other than winter ( Table 4

- 3 ). Personal

- ambien t ratios are typically 

### 20

0.1

- 0.3, although individuals spending substantial time outdoors (e.g., outdoor workers) 

### 21 may have much higher ratios (0.5

- 0.9). 

### 22

Personal exposure to other pollutants shows variable association with personal exposure 

### 23

to O 3 , with diff erences in copollutant relationships depending on factors such as season, 

### 24 city

- specific characteristics, activity pattern, and spatial variability of the copollutant 

### 25
(Section 4.3.4 ). In near

- road and on

- road microenvironments, correlations between O 3 

### 26

and traffic

- related pollutants are moderately to strongly negative, with the most strongly 

### 27 negative correlations observed for NO 2 (

- 0.8 to

- 0.9). This is consistent with the 

### 28 chemistry of NO oxidation, in which O 3 is consumed to form NO 2 . The more moderate 

### 29 negative correlations observed for PM 2.5 , PM 1.0 , and VOC may reflect reduced 

### 30

concentrations of O 3 in polluted environments due to other scavenging reacti ons. A 

### 31 similar process occurs indoors, where infiltrated O 3 reacts with airborne or surface



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

### 33

reactions decrease indoor O 3 exposure, they result in increasing exposure to o ther species 

### 34 which may themselves have health effects. 

### 35

### Draft

– Do Not Cite or Quote 4

- 56 June 2012 Variations in ambient O 3 concentrations occur over multiple spatial and temporal scales. 

### 1 Near roadways, O 3 concentrations are reduced due to reaction with NO and other species 

### 2
(Section 4.3.4.2 ). Over spatial scales of a few kilometers and away from roads, O 3 may 

### 3 be somewhat more homogeneous due to its formation as a secondary pollutant, while 

### 4

ove r scales of tens of kilometers, additional atmospheric processing can result in higher 

### 5 concentrations downwind of an urban area. Although local

- scale variability impacts the 

### 6 magnitude of O 3 concentrations, O 3 formation rates are influenced by factors that vary 

### 7 over larger spatial scales, such as temperature (Section 3.2 ), suggesting that urban 

### 8 monitors may track one another temporally but miss small

- scale variability in magnitude. 

### 9 The resulting uncertainty in exposure contributes to exposure measurement error in 

### 10 epidemiologic studies. 

### 11

Another factor that may influence epidemiologic results is the tendency for people to 

### 12

avoid O 3 exposure by altering t heir behavior (e.g., reducing time spent outdoors) on high



### 13

O 3 days. Activity pattern has a substantial effect on ambient O 3 exposure, with time spent 

### 14 outdoors contributing to increased exposure (Section 4.4.2 ). Averting behavior has been 

### 15 predominantly observed among children, older adults, and people with respiratory 

### 16 problems. Such effects are less pronounced in the general population, possibly due to the 

### 17 opportunity cost of behavior modification. Evidence from one recent epidemiologic study 

### 18

indicates increased asthma hospital admissions among children and older adults when O 3 

### 19 alert days were excluded from the analysis (presumably thereby eliminating a verting 

### 20 behavior based on high O 3 forecasts). The lower rate of admissions observed when alert 

### 21 days were included in the analysis suggests that estimates of health effects based on 

### 22

concentration

- response functions which do not account for averting behavior may be 

### 23

biased towards the null. 

### 24

The range of personal

- ambient correlations reported by most studies (0.3

- 0.8) is similar 

### 25

to that for NO 2 ( U.S. EPA, 2008b ) and somewhat lower than that for PM 2.5 ( U.S. EPA, 

### 26

2009d ) . To the extent that relative changes in central

- site monitor concentration are 

### 27 associated with relative changes in exposure concentration, this indicates that ambient 

### 28

monitor concentrations are representative of day

- to

- day changes in average total personal 

### 29 exposure and in personal exposure to ambient O 3 . The lack of indoor sources of O 3 , in 

### 30

contrast to NO 2 and PM 2.5 , is partly responsible for low indoor

- outdoor ratios (generall y 

### 31

0.1

- 0.4) and low personal

- ambient ratios (generally 0.1

- 0.3), although it contributes to 

### 32 increased personal

- ambient correlations. The lack of indoor sources also suggests that 

### 33

fluctuations in ambient O 3 may be primarily responsible for changes in persona l 

### 34 exposure, even under low

- ventilation, low

- concentration conditions. Nevertheless, low 

### 35 personal

- ambient correlations are a source of exposure error for epidemiologic studies, 

### 36 tending to obscure the presence of potential thresholds, bias effect estimates t oward the 

### 37 null, and widen confidence intervals, and this impact may be more pronounced among 

### 38

### Draft

– Do Not Cite or Quote 4

- 57 June 2012 populations spending substantial time indoors. The impact of this exposure error may 

### 1 tend more toward widening confidence intervals than biasing effect estimates, since 

### 2 epidemiologic studies evaluating the influence of monitor selection indicate that effect 

### 3 estimates are similar across different spatial averaging scales and monitoring sites. 

### 4

### Draft

– Do Not Cite or Quote 4

- 58 June 2012 

### References

Anderson, GB; Bell, ML. (2010). Does one size fit all? The suitability of standard ozone exposure metric conversion ratios and implications for epidemiology. J Expo Sci Environ Epidemiol 20: 2

- 11

. http://dx.doi.org/10.1038/jes.2008.69 Anderson, SE; Wells, JR; Fedorowicz, A; Butterworth, LF; Meade, BJ; Mu nson, AE. (2007). Evaluation of the contact and respiratory sensitization potential of volatile organic compounds generated by simulated indoor air chemistry. Toxicol Sci 97: 355

- 363. http://dx.doi.org/10.1093/toxsci/kfm043 Aoki, T; Tanabe, S. (2007). Generation of sub

- micron particles and secondary pollutants from building materials by ozon e reaction. Atmos Environ 41: 3139

- 3150. http://dx.doi.org/10.1016/j.atmosenv.2006.07.053 ASHRAE (American Society of Heating, Refrigerating and Air

- Conditioning Engineers). (2009). The 2009 ASHRAE handbook: Fundamentals. Atlanta, GA: American Society of Heating, Refrigerating and Air

- Conditioning Engineers Inc. http://www.ashrae.org/publications/page/2283 Avol, EL; Navidi, WC; Colome, SD. (1998a). Modeling ozone levels in and around southern Califo rnia homes. Environ Sci Technol 32: 463

- 468. Beckerman, B; Jerrett, M; Brook, JR; Verma, DK; Arain, MA; Finkelstein, MM. (2008). Correlation of nitrogen dioxid e with other traffic pollutants near a major expressway. Atmos Environ 42: 275

- 290. http://dx.doi.org/10.1016/j.atmosenv.2007.09.042 Beelen, R; Hoek, G; Pebesma, E; Vienneau, D; de Hoogh, K; Briggs, DJ. (2009). Mapping of background air pollution at a fine spatial scale across the European Union. Sci Total Environ 407: 1852

- 1867. http://dx.doi.org/10.1016/j.scitotenv.2008.11.048 Bekö, G; Clausen, G; Weschler, CJ. (2007). Further studies of oxi dation processes on filter surfaces: Evidence for oxidation products and the influence of time in service. Atmos Environ 41: 5202

- 5212. http://dx.doi.org/10.1016/j.atmosenv.2006.07.063 Bell, ML. (2006). The use of ambient air quality modeling to estimate individual and population exposure for human health research: A case study of ozone in the Northe rn Georgia region of the United States. Environ Int 32: 586

- 593. Bell, ML; McDermott, A; Zeger, SL; Samet, JM; Dominici, F. (2004). Ozone and short

- term mortal ity in 95 US urban communities, 1987

- 2000. JAMA 292: 2372

- 2378. http://dx.doi.org/10.1001/jama.292.19.2372 Blanken, PD; Dillon, J; Wismann, G. (2001). The impact of an air quality advisory program on voluntary mobile source air pollution reduction. Atmos Environ 35: 2417

- 2421. Blondeau, P; Iordache, V; Poupard, O; Genin, D; Allard, F. (2005). Relationship between outdoor and indoor air quality in eight French schools. Indoor Air 15: 2

- 12. Brauer, M; Brook, JR. (1997). Ozone personal exposures and health effects for selected groups residing in the Fraser Valley. Atmos Environ 31: 2113

- 2121. Brauer, M; Brumm, J; Vedal, S; Petkau, AJ. (2002). Exposure misclassification and threshold concentrations in time series analyses of air pollution health effects. Risk Anal 22: 1183

- 1193. Brauer, M; Lencar, C; Tamburic, L; Koehoorn, M; Demers, P; Karr, C. (2008). A cohort study of traffic

related air pollution impacts on birth outcomes. Environ Health Per spect 116: 680

- 686. http://dx.doi.org/10.1289/ehp.10952 Bravo, MA; Bell, ML. (2011). Spatial heterogeneity of PM10 and O3 in Sao Paulo, Brazil, and implications for human health studies. J Air Waste Manag Assoc 61: 69

- 77. Breen, MS; Breen, M; Williams, RW; Schultz, BD. (2010). Predicting residential air exchange rates from questionnaires and meteorology: Model evaluation in central North Carolina. Environ Sci Technol 44: 9349

- 9356. http://dx.doi.org/10.1021/es101800k Bresnahan, BW; Dickie, M; Gerking, S. (1997). Averting behavior and urban air pollution. Land Econ 73: 34

- 57. Briggs, DJ; Collins, S; Elliott, P; Fischer, P; Kingham, S; Lebret, E; Pryl, K; Van Reeuwijk, H; Smallbone, K; Van Der Veen, A. (1997). Mapping urban air pollution using GIS: A regression

- based approach. Int J Geogr Inform Sci 11: 699

- 718.

### Draft

– Do Not Cite or Quote 4

- 59 June 2012 Brown, K; Sarnat, J; Suh, H; Coull, B; Koutrakis, P. (2009). Factors influencing relationships between personal and ambient con centrations of gaseous and particulate pollutants. Sci Total Environ 407: 37543765. http://dx.doi.org/10.1016/j.scitotenv.2009.02.016 Burke, JM; Zufall, MJ; Ozkaynak, H. (2001). A population exposure model for particulate matter: Case study results for PM2.5 in Philadelphia, PA. J Expo Sci Environ Epide miol 11: 470

- 489. Chan, WR; Nazaroff, WW; Price, PN; Sohn, MD; Gadgil, AJ. (2005b). Analyzing a database of residential air leakage in the United States. Atmos Environ 39: 3445

- 3455. Chang, LT; Koutrakis, P; Catalano, PJ; Suh, HH. (2000). Hourly personal exposures to fine particles and gaseous pollutants

-- Results from Baltimore, Maryland. J Air Waste Manag Assoc 50: 1223

- 1235. Chen, X; Hopke, PK; Carter, WP. (2011). Secondary organic aerosol from ozonolysis of biogenic vol atile organic compounds: Chamber studies of particle and reactive oxygen species formation. Environ Sci Technol 45: 276

- 282. http://dx.doi.org/10.1021/es102166c Christakos, G; Vyas, VM. (1998a). A composite space/time approach to studying ozone distribution over eastern United States. Atmos Environ 32: 2845

- 2857. http://dx.doi.org/10.1016/S1352

- 2310(98)00407

- 5 Christakos, G; Vyas, VM. (1998b). A novel method for studying population health impacts of spatiotemp oral ozone distribution. Soc Sci Med 47: 1051

- 1066. Darrow, LA; Klein, M; Sarnat, JA; Mulholland, JA; Strickland, MJ; Sarnat, SE; Russell, AG; Tolbert, PE. (2 011a). The use of alternative pollutant metrics in time

- series studies of ambient air pollution and respiratory emergency department visits. J Expo Sci Environ Epidemiol 21: 10

- 19. http://dx.doi.org/10.10 38/jes.2009.49 Darrow, LA; Klein, M; Strickland, MJ; Mulholland, JA; Tolbert, PE. (2011b). Ambient air pollution and birth weight in full

- term infants in Atla nta, 1994

- 2004. Environ Health Perspect 119: 731

- 737. http://dx.doi.org/10.1289/ehp.1002785 Delfino, RJ; Coate, BD; Zeiger, RS; Seltzer, JM; Street, DH; Koutrakis, P. (1996). Daily asthma severity in relation to personal ozone exposure and outdoor fungal spores. Am J Respir Crit Care Med 154: 633

- 641. Georgopoulos, PG; Purushothaman, V; Chiou, R. (1997). Comparative evaluation of methods for estimating potential human exposure to ozone: Photochemical modeling and ambient monitoring. J Expo Sci Environ Epidemiol 7: 191

- 215. Georgopoulos, PG; Wang, SW; Vyas, VM; Sun, Q; Burke, J; Vedantham, R; McCurdy, T; Ozkaynak, H. (2005). A source

- to

- dose assessment of population exposure s to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode. J Expo Sci Environ Epidemiol 15: 439

- 457. Geyh, AS; Roberts, PT; Lurmann, FW; Schoell, B M; Avol, EL. (1999). Initial field evaluation of the Harvard active ozone sampler for personal ozone monitoring. J Expo Sci Environ Epidemiol 9: 143

- 149. Geyh, AS; Wolfson, JM; Koutrakis, P; Mulik, JD; Avol, EL. (1997). Development and evaluation of a small active ozone sampler. Environ Sci Technol 31: 2326

- 2330. Geyh , AS; Xue, J; Ozkaynak, H; Spengler, JD. (2000). The Harvard Southern California chronic ozone exposure study: Assessing ozone exposure of grade

- school

- age children in two southern California communities. Environ Health Perspect 108: 265

- 270. Gilliland, F; Avol, E; Kinney, P; Jerrett, M; Dvonch, T; Lurmann, F; Buckley, T; Breysse, P; Keeler, G; de Villiers, T; Mcconnell, R. (2005). Air pollution exposure asse ssment for epidemiologic studies of pregnant women and children: Lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research. Environ Health Perspect 113: 1447

- 1454. http://dx.doi.org/10.1289/ehp.7673 Glen, G; Smith, L; Isaacs, K; Mccurdy, T; Langstaff, J. (2008). A new method of longitudinal diary assembly for human exposu re modeling. J Expo Sci Environ Epidemiol 18: 299

- 311. http://dx.doi.org/10.1038/sj.jes.7500595 Goldman, GT ; Mulholland, JA; Russell, AG; Strickland, MJ; Klein, M; Waller, LA; Tolbert, PE. (2011). Impact of exposure measurement error in air pollution epidemiology: Effect of error type in time

series studies. Environ Health Global Access Sci Source 10: 61. http://dx.doi.org/10.1186/1476

- 069X

10

- 61 Grosjean, D; Hisham, MWM. (1992). A passive sampler for atmosphe ric ozone. J Air Waste Manag Assoc 42: 169

- 173.

### Draft

– Do Not Cite or Quote 4

- 60 June 2012 Guay, M; Stieb, DM; Smith

- Doiron, M. (2011). Assessment of long

- term exposure to air pollution in a longitudin al national health survey. J Expo Sci Environ Epidemiol 21: 337

- 342. http://dx.doi.org/10.1038/jes.2010.37 Henry, GT; Gordon, CS. (2003). Driving less for better air: Impacts of a public information campaign. J Policy Anal Manage 22: 45

- 63. http://dx.doi.org/10.1002/pam.10095 Héroux, ME; Clark, N; Van Ryswyk, K; Mallick, R; Gilbert, NL; Harrison, I; Rispler, K; Wang, D; Anastassopoulos, A; Guay, M; MacNeill, M; Wheeler, AJ. (2010). Predictors of indoor air

concentr ations in smoking and non

- smoking residences. Int J Environ Res Public Health 7: 3080

- 3099. http://dx.doi.org/10.3390/ijerph7083080 Hoek, G; Beelen, R; de Hoogh, K; Vienneau, D; Gulliver, J; Fischer, P; Briggs, D. (2008). A review of land

use regression models to assess spatial variation of outdoor air pollution [Review]. Atmos Environ 42: 7561

- 7578. Hyttinen, M; Pasanen, P; Kalliokoski, P. (2006). Removal of ozone on clean, dusty and sooty supply air filters. Atmos Environ 40: 315

- 325. Jalaludin, B; Mannes, T; Morgan, G; Lincoln, D; Sheppeard, V; Corbett, S. (2007). Impact of ambient air pollution on gestational age is modified by season in Sydney, Australia. E nviron Health 6: 16. http://dx.doi.org/10.1186/1476

- 069X

- 6

- 16 Jerrett, M; Burnett, RT; Pope, CA, III; Ito, K; Thurston, G; Krewski, D; Shi, Y; Calle, E; Thun, M. (2009). Long

- term ozone exposure and mortality. N Engl J Med 360: 1085

- 1095. http://dx.doi.org/10.1056/NEJMoa0803894 Kanno, S; Yanagisawa, Y. (1992). Passive ozone/oxidant sampler with coulometric determination using iodine/nylon

- 6 charge

- transfer complex. Environ Sci Technol 26: 744

- 749. http://dx.doi.org/10.1021/es00028a012 Karner, AA; Eisinger, DS; Niemeier, DA. (2010). Near

- roadway air quality: Synthesi zing the findings from real

- world data. Environ Sci Technol 44: 5334

- 5344. http://dx.doi.org/10.1021/es100008x Klepeis, NE. (1999). An introduction to the indirect exposure assessment approach: Modeling human exposure using microenvironmental measurements and the recent National Human Activity Pattern Survey. Environ Health Perspect 107: 365

- 374. Klepeis, NE; Nelson, WC; Ott, WR; Robinson, JP; Tsang, AM; Switzer, P; Behar, JV; Hern, SC; Engelmann, WH. (2001). The National Human Activity Pattern Survey (NHAPS): A resour ce for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol 11: 231

- 252. Klepeis, NE; Tsang, AM; Behar, JV. (1996). Analysis of the na tional human activity pattern survey (NHAPS) respondents from a standpoint of exposure assessment [EPA Report]. (EPA/600/R

- 96/074). Washington, DC: U.S. Environmental Protection Agency. Koutrakis, P; Wolfson, JM; Bunyaviroch, A; Froehlich, SE; Hirano, K; Mulik, JD. (1993). Measurement of ambient ozone using a nitrite

- coated filter. Anal Chem 65: 209

- 214. Langstaff, JE. (2007). Analysis of uncertainty in ozone population exposure modeling [technical memorandum]. Research Triangle Park, NC: U.S. Environmental Protection Agency, Office of Air Quality Planning and Standa rds. Lee, K; Parkhurst, WJ; Xue, J; Ozkaynak, H; Neuberg, D; Spengler, JD. (2004a). Outdoor/indoor/personal ozone exposures of children in Nashville, Tennessee . J Air Waste Manag Assoc 54: 352

- 359. Leech, JA; Nelson, WC; Burnett, RT; Aaron, S; Raizenne, ME. (2002). It's about time: A comparison of Canadian and Ameri can time

- activity patterns. J Expo Anal Environ Epidemiol 12: 427

- 432. http://dx.doi.org/10.1038/sj.jea.7500244 Liard, R; Zureik, M; Le Moullec, Y; Soussan, D; Glorian, M; Grimfeld, A; Neukirch, F. (1999). Use of personal passive samplers for measurement of NO2, NO, and O3 levels in panel studies. Environ Res 81: 339

- 348. Linn, WS; Shamoo, DA; Anderson, KR; Peng, RC; Avol, EL; Hackney, JD; Gong, H, Jr. (1996). Short

- term air pollution exposures and responses in Los Angeles area schoolchildren. J Expo Sci Environ Epi demiol 6: 449

- 472. Liu, LJS; Koutrakis, P; Leech, J; Broder, I. (1995). Assessment of ozone exposures in the greater metropolitan Toronto area. J Air Waste Man ag Assoc 45: 223

- 234. López

- Aparicio, S; Smolík, J; Mašková, L; Součková, M; Grøntoft, T; Ondráčková, L; Stankiewicz, J. (2011). Relationship of indoor and ou tdoor air pollutants in a naturally ventilated historical building envelope. Build Environ 46: 1460

- 1468. http://dx.doi.org/10.1016/j.buildenv.2011.01.013

### Draft

– Do Not Cite or Quote 4

- 61 June 2012 Mansfield, CA; Johnson, FR; Van Houtven, GL. (2006). The missing piece: Valuing averting behavior for children's ozone exposures. Resource Energ Econ 28: 215

- 228. http://dx.doi.org/10.1016/j.reseneeco.2006.02.002 Marshall, JD; Nethery, E; Brauer, M. (2008). Wi thin

- urban variability in ambient air pollution: Comparison of estimation methods. Atmos Environ 42: 1359

- 1369. http://dx.doi.org/10.1016/j.atmosenv.2007.08.012 Mcconnell, R; Berhane, K; Yao, L; Lurmann, FW; Avol, E; Peters, JM. (2006). Predicting residential ozone deficits from nearby traffic. Sci Total Environ 363: 166

- 174. http://dx.doi.org/10.1016/j.scitotenv.2005.06.028 McCurdy, T; Glen, G; Smith, L; Lakkadi, Y. (2000). The National Exposure Research Laboratory's consolidated human activity database. J Expo Sci Environ Epidemiol 10: 566

- 578. McDermott, M; Srivastava, R; Croskell, S. (2006). Awaren ess of and compliance with air pollution advisories: A comparison of parents of asthmatics with other parents. J Asthma 43: 235

- 239. http://dx.doi.org/10.1080/02770900600567114 Mortimer, KM; Neas, LM; Dockery, DW; Redline, S; Tager, IB. (2002). The effect of air pollution on inner

city children with asthma. Eur Respir J 19: 699

- 705. http://dx.doi.org/10.1183/09031936.02.00247102 Neidell, M. (2009). Information, avoidance behavior, and health: The effec t of ozone on asthma hospitalizations. Journal of Human Resources 44: 450

- 478. Neidell, M. (2010). Air quality warnings and outdoor activities: Evidence from Southern California using a regression discontinuity design. J Epidemiol Community Health 64: 921

- 926. http://dx.doi.org/10.1136/jech.2008.081489 Neidell, M; Kinney, PL. (2010). Estimates of the association between ozone and asthma hospitalizations that account for behavioral responses to air quality information. Environ Sci Pol 13: 97

- 103. http://dx.doi.org/10.1016/j.envsci.2009.12.006 O'Neill, MS; Ramirez

- Aguilar, M; Meneses

- Gonzalez, F; Her nandez

- Avila, M; Geyh, AS; Sienra

- Monge, JJ; Romieu, I. (2003). Ozone exposure among Mexico City outdoor workers. J Air Waste Manag Assoc 53: 339

- 346. Ogawa & Co (Ogawa & Company). (2007). Ambient air passive sampler for NO

- NO2, NOx, SO2, O3, NH3. Pompano Beach, FL: Ogawa & Company USA, Inc. http://www.ogawausa.com/passive.html Ramírez

- Aguilar, M; Barraza

- Villarreal, A; Moreno

- Macías, H; Winer, AM; Cicero

- Fernández, P; Vélez

Márquez, MG; Cortez

- Lugo, M; Sienra

- Monge, JJ; Romieu, I. (2008). Assessment of personal
exp osure to ozone in asthmatic children residing in Mexico City. Salud Publica Mex 50: 67

- 75. Reiss, R; Ryan, PB; Koutrakis, P; Tibbetts, SJ. (1995a). Ozone reactive chemistry on interior latex paint. Environ Sci Technol 29: 1906

- 1912. Reiss, R; Ryan, PB; Tibbetts, SJ; Koutrakis, P. (1995b). Measure ment of organic acids, aldehydes, and ketones in residential environments and their relation to ozone. J Air Waste Manag Assoc 45: 811

- 822. Riediker, M; Williams, R; Devlin, R; Griggs, T; Bromberg, P. (2003). Exposure to particulate matter, volatile organic compounds, and other air pollutants inside patrol cars. Environ Sci Technol 37: 2084

- 2093. http://dx.doi.or g/10.1021/es026264y Romieu, I; Lugo, MC; Colome, S; Garcia, AM; Avila, MH; Geyh, A; Velasco, SR; Rendon, EP. (1998a). Evaluation of indoor ozone concentration a nd predictors of indoor

- outdoor ratio in Mexico City. J Air Waste Manag Assoc 48: 327

- 335. Ryan, PH; LeMasters, GK. (2007). A review of land

- use regression mo dels for characterizing intraurban air pollution exposure [Review]. Inhal Toxicol 19: 127. Sarnat, JA; Brown, KW; Schwartz, J; Coull, BA; Koutrakis, P. (2005). Ambient gas concentrations and personal particulate matter exposures: implications for studying the health effects of particles. Epidemiology 16: 385

- 395. Sarnat, JA; Koutrakis, P; Suh, HH. (2000). Assessing the relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD. J Air Waste Manag Assoc 50: 1184

- 1198. Sarnat, JA; Schwartz, J; Catalano, PJ; Suh, HH. (2001). Gaseous pollutants in particulate matter epidemiology: Confounders or surrogates? Environ Health Perspect 109: 1053

- 1061. Sarnat, SE; Coull, BA; Schwartz, J; Gold, DR; Suh, HH. (2006a). Factors affecting the association between ambient concentrations and personal exposures to particles and g ases. Environ Health Perspect 114: 649

- 654.

### Draft

– Do Not Cite or Quote 4

- 62 June 2012 Sarnat, SE; Klein, M; Sarnat, JA; Flanders, WD; Waller, LA; Mulholland, JA; Russell, AG; Tolbert, PE. (2010). An e xamination of exposure measurement error from air pollutant spatial variability in time

series studies. J Expo Sci Environ Epidemiol 20: 135

- 146. http://dx.doi.org/10.1038/jes.2009.10 Semenza, JC; Wilson, DJ; Parra, J; Bontempo, BD; Hart, M; Sailor, DJ; George, LA. (2008). Public perception and behavior change in relationship to hot weather and air pollution. E nviron Res 107: 401

- 411. http://dx.doi.org/10.1016/j.envres.2008.03.005 Sheppard, L; Slaughter, JC; Sc hildcrout, J; L

- JS, L; Lumley, T. (2005). Exposure and measurement contributions to estimates of acute air pollution effects. J Expo Sci Environ Epidemiol 15: 366

- 376. Sherman, M; McWilliams, J. (2007). Air leakage of U.S. homes: Model prediction. (LBNL

- 62078). Berkeley, CA: Lawrence Berkeley National Laboratory. http://epb.lbl.gov/publications/lbn l

- 62078.pdf Sherman, MH; Grimsrud, DT. (1980). Infiltration

- pressurization correlation: Simplified physical modeling. In ASHRAE Transactions. Berkeley, CA: Law rence Berkeley Laboratory. http://eetd.lbl.gov/ie/pdf/LBL

- 10163.pdf Shu, S; Morrison, GC. (2011). Surfac e reaction rate and probability of ozone and alpha

- terpineol on glass, polyvinyl chloride, and latex paint surfaces. Environ Sci Technol 45: 4285

- 4292. http://dx.doi.org/10.1021/es200194e Suh, HH; Zanobetti, A. (2010). Exposure error masks the relationship between traffic

- related air pollution and heart rate variability [Erratum]. J Occup Environ Med 52: 1138. http://dx.doi.org/10.1097/JOM.0b013e3181fd2632 U.S. EPA (U.S. Environmental Protection Agency). (2008b). Integrated science assessment for oxides of nitrogen: Health criteria [EPA Report]. (EPA/600/R

- 08/071). Research Triangle Park, NC. h ttp://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645 U.S. EPA (U.S. Environmental Protection Agency). (2009a). Consolidated Human Activity Database. Avail able online at http://www.epa.gov/chadnet1/ (accessed August 27, 2009). U.S. EPA (U.S. Environmental Protection Agen cy). (2009b). Human exposure modeling: Air pollutants exposure model (APEX/TRIM.Expo Inhalation). Available online at http://www.epa.gov/ttn/fera/human_apex.html (accessed August 27, 2009). U.S. EPA (U.S. Environmental Protection Agency). (2009d). Integrated science assessment for particulate matter [EPA Report]. (EPA/600/R

- 08/139F). Research Triangle Par k, NC. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546 U.S. EPA (U.S. Envir onmental Protection Agency). (2011a). AirNow. Available online at http://www.airnow.gov/ (accessed January 28, 2011). U.S. EP A (U.S. Environmental Protection Agency). (2011b). Exposure factors handbook 2011 edition (final) [EPA Report]. (EPA/600/R

- 09/052F). http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm? deid=236252 U.S. EPA (U.S. Environmental Protection Agency). (2011c). Exposure model for individuals. Available online at http://www.epa.gov/heasd/products/emi/emi.html (accessed July 1, 2011). Wallace, L; Williams, R; Suggs, J; Jones, P. (2006). Estimating contributions of outdoor fine particles to indoor concentrations and personal exposures: Effects of household characteristics and personal activities. (EPA/600/R

- 06/023). Research Triangle Park, NC: U.S. Environmental Protection Agency. Wen, XJ; Balluz, L; Mokdad, A. (2009). Association between media alerts of air quality index and change of outdoor activity among adult asthma in six states, BRFSS, 2005. J Community Health 34: 40

- 46. http://dx.doi.org/10.1007/s10900

- 008

- 9126

- 4 Weschler, CJ; Shi elds, HC. (1997). Potential reactions among indoor pollutants. Atmos Environ 31: 3487

- 3495. White, IR; Martin, D; Muñoz, MP; Petersson, FK; Henshaw, SJ; Nickl ess, G; Lloyd

- Jones, GC; Clemitshaw, KC; Shallcross, DE. (2010). Use of reactive tracers to determine ambient OH radical concentrations: Application within the indoor environment. Environ Sci Technol 44: 6269

- 6274. http://dx.doi.org/10.1021/es901699a Williams, R; Rea, A; Vette, A; Croghan, C; Whitaker, D; Stevens, C; Mcdow, S; Fortmann, R; Sheldon, L; Wilson, H; Thornburg, J; Phillips, M; Lawless, P; Rodes, C; Daughtrey, H. (2009b). The design and field implementation of the Detroit exposure and aerosol research study. J Expo Sci Environ Epidemiol 19: 643

- 659. http://dx.doi.org/10.1038/jes.2008.61 Wilson, KL; Birks, JW. (2006). Mechanism and elimination of a water vapor interference in the measurement of ozone by UV absorbance. Environ Sci Technol 40: 6361

- 6367. http://dx.doi.org/10.1021/es052590c

### Draft

– Do Not Cite or Quote 4

- 63 June 2012 Wilson, WE; Mage, DT; Grant, LD. (2000). Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: Why and how. J Air W aste Manag Assoc 50: 1167

- 1183. Wilson, WE; Suh, HH. (1997). Fine particles and coarse particles: Concentration relationships relevant to epidemiologic studies . J Air Waste Manag Assoc 47: 1238

- 1249. Xue, J; Liu, SV; Ozkaynak, H; Spengler, JD. (2005). Parameter evaluation and model validation of ozone
exposure assess ment using Harvard Southern California Chronic Ozone Exposure Study data. J Air Waste Manag Assoc 55: 1508

- 1515. Xue, J; McCurdy, T; Spengler, J; Ozkaynak, H. (2004). Understanding variability in time spent in selected locations for 7

- 12

- year old children. J Expo Anal Environ Epidemiol 14: 222

- 233. http://dx.doi.org/10.1038/sj.jea.7500319 Zartarian, VG; Schultz, BD. (2010). The EPA's human exposure research program for assessing cumulative risk in communities. J Expo Sci Environ Epidemiol 20: 351

- 358. http://dx.doi.org/10.1038/jes.2009.20 Zauli Sajani, S; Hänninen, O; Marchesi, S; Lauriola, P. (2011). Comparis on of different exposure settings in a

case

- crossover study on air pollution and daily mortality: Counterintuitive results. J Expo Sci Environ Epidemiol 21: 385

- 394. http://dx.doi.org/10.1038/jes.2010.27 Zeger, SL; Thomas, D; Dominici, F; Samet, JM; Schwartz, J; Dockery, D; Cohen, A. (2000). Exposure measurement error in time

- series studies of air pollution: Conc epts and consequences. Environ Health Perspect 108: 419

- 426. Zidek, JV; Shaddick, G; Meloche, J; Chatfield, C; White, R. (2007). A framework for predicting pe rsonal exposures to environmental hazards. Environ Ecol Stat 14: 411

- 431.