MEMORANDUM

To: Bryan Hubbell – Leader, Risk and Benefits Group OAQPS

From: Stephen Graham – Physical Scientist, Risk and Benefits Group OAQPS

Date: July 15, 2012

Re: Comprehensive review of published averting behavior studies and 

available technical documents.

Overview

The following review of the averting behavior literature draws upon key results from several published 

studies (including the ISA evaluation of these studies [ISA, section 4.1.1]), though also extending the 

literature reviewed to include several available technical reports. The discussion that follows is 

separated into two broadly identified components of averting behavior: (1) the prevalence of air quality 

system awareness and (2) the common behavior modifications performed in response to either the air 

quality alert information or high ambient pollutant concentrations. We attempt to distinguish the 

various characteristics influencing each of these two components, though recognize that neither exists 

solely in isolation. In addition, the implications of averting behavior relevant to our ozone exposure and 

risk assessment are then briefly summarized, also separated into two categories: (1) the impact to 

epidemiologic concentration-response functions, (2) the effect of using time location activity pattern 

data for persons that may be averting when simulating population ozone exposures. At the end of this 

document is a table that summarizes the relevant findings from each publication reviewed.

1. Air Pollution Alert Awareness

There are three components of a fully functioning local-scale air quality system that can influence a 

personal behavioral response to an air quality alert: (1) awareness of local ambient air quality 

conditions, (2) understanding the relationship between air pollutant exposure and adverse health 

effects, and (3) knowledge of local air pollution management programs designed to reduce pollutant 

concentrations and/or personal exposure. The magnitude of impact the air quality system information 

can have will be directly influenced by the type and amount of communication offered by both National 

and local health and environmental protection agencies. A number of the studies reviewed here 

collected relevant information on these three factors, commonly in the form of recall or panel surveys 
designed to gauge the particular degree of awareness in survey participants.1

 As the types of survey 

questions and study participants used were wide ranging and dependent on the particular study 

objectives, the responses and participation rates were also highly variable and dependent on a variety of 

factors. The objective in this current evaluation is to generalize the findings regarding people’s 

knowledge of air quality systems and to recognize the important variables influencing awareness.

In considering the two commuter panel studies reviewed, awareness of AQ systems in urban areas can 

be quite prevalent (>90%; Blanken et al, 2001) with general air pollution awareness raised by factors 

such as time (day-of-week, month-of-year), residential proximity to urban core,2and personal 

demographics (i.e., those individuals of higher income-level, obtaining higher education, or are selfdescribed as a non-minority) (Blanken et al., 2001; Henry and Gordon, 2002). In several other largerscale population-based random surveys, I found a large portion of study participants were also aware of 

air quality alert information (e.g., 42.7%, KS DOH, 2006; 33%, Mansfield and Corey, 2003; 61% Mansfield 

et. al, 2006; 91% Mansfield et al., 2009; 82-89%; Semenza et al., 2008) and those with greater awareness 

had similar characteristics as described above for commuters (e.g., higher income level, older age, reside 

in an urban location). 

Perhaps more importantly though, awareness was also found consistently heightened in persons having 

a compromised health condition relative to those without (e.g., 46% of asthmatics vs. 42.5% of nonasthmatic, KS DOH, 2006; 95% of asthmatic children versus 80% of non-asthmatic children, McDermott 

et al., 2006). The likelihood of checking for air quality alerts is also influenced by health status, as 25% 

of parents with asthmatic children check the forecast daily, compared with only 9% of parents of nonasthmatic children (Mansfield et al., 2009). Further, Houtven et al. (2003) also reports increased 

awareness among asthmatic children compared to non-asthmatics, noting also the degree of disease 

severity as a statistically significant explanatory variable. 

There were a few studies that collected information on how persons felt about the relationship between 

air pollution and health effects, and when considered, the resulting fraction of persons making the 

connection between the two factors was generally small. For example, it was estimated that only 6% of 

the Kansas population thought that an illness or symptom they had was caused by an outdoor air 

pollution event (KS DOH, 2006). Though still, the ability to make a connection between air pollution and 

health effects remains a significant factor increasing overall awareness of air quality alerts (Henry and 

Gordon, 2002). It is possible that the small percent of persons able to make this connection is the result 

of their having limited communication with their health care providers about this issue. For example, it 

was estimated that only 4% of Kansans surveyed were advised by a health professional to reduce 

outdoor activity level when air quality is poor (KS DOH, 2006). Further, it is possible that only a few 

surveyed persons actually experienced a high enough exposure to elicit a health response, allowing one 

 

1

For example, survey questions commonly include “are you aware of the local air quality advisory program?” or 

“poor air quality affects me or my family directly”, etc. 

2

Not surprisingly, the urban areas are likely to have greater pollution and an associated alert system while rural 

areas do not.
to make a firm connection between air pollution and health effects, though none of the studies I 

reviewed evaluated this factor.

That said, it would be misleading to suggest that all studies indicated persons were unable to make 

connection between exposure and health risk. As an example of an upper bound estimate, 91% of 

parents from a convenience survey of parents in Salt Lake City, Utah linked poor air quality with asthma 

and other illness, with little difference observed between asthmatic and non-asthmatic cohorts 

(McDermott, 2006). It is possible that the observed variability in awareness indicates that personal 

awareness and perception regarding air pollution and health is influenced by geography, beyond simple 

urban versus rural classifications. For example, six states (Colorado, Florida, Indiana, Kansas, 

Massachusetts, and Wisconsin) reported AQI awareness and outdoor activity level information for the 

2005 Behavioral Risk Factor Surveillance System (BRFSS) (Wen et al., 2009). In total, 49% of asthmatics 

and 46% of non-asthmatics were aware of media alerts on air quality. Data for one of the states, 

Kansas, reported a lower percent for each cohort, i.e., 46% and 42.5%, respectively (KS DOH, 2006).3 

Similarly, small but statistically significant differences in alert system awareness were observed in 

surveyed populations in Portland (82.3%) and Houston (89.1%) (Semenza et al., 2008).

To summarize, the prevalence of awareness was variable. I estimate about 50% to 90% of survey study 

participants acknowledge or were familiar with air quality systems, with asthmatics or parents of 

asthmatic children having a consistently greater degree of awareness (approximately a few to 15 

percentage points) when compared to that of non-asthmatics. Advice from a health care provider also 

increased awareness, and while the portion of the population receiving this communication is small, it 

most likely correlated with persons having a health condition. Some of the personal variables found 

associated with increasing awareness, generally in order of greatest importance, include health status 

(particularly asthmatics), urban proximity, and a suite of commonly correlated demographic attributes 

(i.e., education, income level, non-minority, and age). 

I suggest that one influential factor not adequately explored is city- or region-specific differences in 

awareness. Most studies took place in a single area and few if any that did take place in more than one 

study area had the proper data to evaluate such differences while controlling for all potential influential 

variables. Another important factor not analyzed in the reviewed literature that may remain un-testable 

for some time, is changes in awareness within a location over time. It is highly likely that public 

awareness of air quality systems is increasing across the US. For example, there are now 

communications via the internet and television (both government and news media sources), availability 

 

3

There could be important factors other than the geographic region influencing the cohort prevalence differences 

(e.g., age, gender, education, income level) versus actual alert system variables (e.g., number of alerts, years 

system in place, etc.) comprising each State’s data set, however regional differences were not evaluated in Wen et 

al., (2009), controlling for such confounders and remain as a potential influential variable for both awareness and 

averting behavior. In addition, the KS DOH (2006) used both questions regarding asthma (‘EVER’ and ‘STILL’) to 

identify asthmatics while Wen et al. (2009) used ‘EVER’ only, a difference that would support the overall pattern, 

though uncertainty remains regarding the magnitude of influence the ‘STILL’ versus ‘EVER’ variable would have on 

the awareness.
of mobile phone applications,4

drives for participation in AQI school flag programs,5among several 

others sources, of which, most have only been developed within the past decade or few recent years.

This increased communication and heightened public awareness is likely to yield greater a rate of 

averting behavior in response to air quality alerts, supporting further the importance of our 

investigations into understanding the impact averting may have on exposure and risk assessments. 

2. Averting Behaviors

There can be a wide range of behavior modifications performed in response to environmental 

pollutants. For example, one of the earliest studies I reviewed took place in an area having historical 

groundwater contamination and analyzed factors that influenced local residents’ choice of drinking 

water source and their attendance at public meetings (Smith and Desvousges, 1986). While perhaps 

informative in stimulating generalized questions regarding how and the degree of which persons might 

react in response to environmental pollutants, for our purposes, I focused my review on those studies 

primarily designed to evaluate air pollution averting behaviors. I characterized the averting behaviors of 

interest as either activities performed that would reduce pollutant emissions or actions taken to reduce 

personal exposures to ambient pollutant concentrations.

2.1 Averting behavior to reduce pollutant emissions

As part of an air quality management plan to reduce pollutant emissions, local environmental and health 

agencies may strongly encourage daily commuters to use alternative modes of transportation rather 

than use single occupied passenger vehicles to get to their workplace. Even though many commuters 

are keenly aware of air quality alerts and requests for pollutant emission reductions, many of the early 

studies showed that adult commuting patterns are not dramatically affected by air quality alerts 

(Blanken et al., 2001; Henry and Gordon, 2002). For example, while the total commuter miles driven 

may be reduced on alert days (e.g., by about 15%, Henry and Gordon, 2002), rates for this averting 

behavior tend to range from low (e.g., 12% of commuters said they changed pattern on alert day, 

however 76% do not, Blanken et al., 2001; 3-3.5% reduction in traffic volume on alert days, Cutter and 

Neidell, 2009) to at most, a moderate extent (e.g., 28% of adults said they adjusted their driving pattern 

on alert days, Mansfield and Corey, 2003). Further, there was one instance where commuter miles were 

not reduced but increased in response to smog alerts (Noonan, 2010, 2011).6 One recent study 

conducted in Salt Lake City, Utah reported a large percent of parents (71%) say they reduce fossil fuel 

 

4

The AIRNow mobile application provides real-time air quality information. See 

http://m.epa.gov/apps/airnow.html. 

5

The Flag Program uses colored flags based on the AQI forecast for each day to notify school administrators, 

teachers, students, and others about outdoor air quality conditions. See 

http://airnow.gov/index.cfm?action=school_flag_program.index. The list of schools currently registered for this 

voluntary program is found http://airnow.gov/index.cfm?action=airnow.schoolflagprogramlist.

6

Noonan (2010) speculates smog alerts “encourage pedestrians to seek shelter in cars”. Elsewhere it was 

suggested that on alert days, the expectation that traffic congestion may be reduced by other persons not 

commuting inadvertently decreases the likelihood of a person averting that day. 
use when air quality is poor (McDermott et al., 2006), possibly indicating that air quality awareness may 

be increasing over time and potentially influencing the rate of averting as suggested above in the 

discussion regarding awareness. However, a study conducted by Semenza et al. (2008) may suggest 

otherwise for Portland, OR and Houston, TX where participants indicated they neither reduced driving 

nor postponed refueling events on air pollution advisory days. As was discussed regarding awareness, 

perhaps the study location (or particular geography) is the prevailing influential variable, though, in the 

absence of multi-city data from the McDermott et al. (2006) study, this is purely speculation.

There were a few factors identified in the reviewed studies that appear to influence whether or not 

someone participates in an averting behavior to reduce local pollution emissions from motor vehicles. 

First, the perceived inconvenience of mass transportation and limited employer support may be a 

significant driver behind the low response rate observed. For example, public employees exhibited a 

greater reduction in both number of trips and miles travelled when compared with their private 

counterparts, suggesting the likelihood of a public policy or supportive position regarding commuting to 

these workplaces (Henry and Gordon, 2002). In addition, those persons more educated along with 

having higher income tend to drive more than their counterparts, implying a strong regularity in their 

commuting pattern and an inherent non-conformance to air quality alerts (Henry and Gordon, 2002; 

Mansfield and Corey, 2003). However, the perceived efficacy of the behavior may also contribute to low 

rate of averting activities as very few commuters feel that reducing their mobile source emissions has an 

impact on local air quality (Blanken et al., 2001). As was described above for air quality alert awareness, 

parents of asthmatic children were more likely to say they reduced pollutant emissions on poor air 

quality days compared with parents of non-asthmatic children (79% vs. 61%, respectively; McDermott et 

al., 2006), again highlighting the importance of health condition regarding averting decisions made. And 

finally, time-of-day had a significant influence on observed traffic volume reductions, particularly during 

the morning rush hour compared with other times in the day (Cutter and Neidell, 2009).

One other averting activity noted to reduce pollutant emissions and its associated ambient air 

concentrations is the placing of a restriction on wood burning. It is entirely possible that having 

mandatory restrictions could positively bias estimated participation, however the strong response rate 

(i.e., 86%, Blanken et al., 2001) suggests a strong perceived link between the emissions and the local 

ambient air pollution. Another activity, adjusting the time of vehicle refueling events, was only briefly 

mentioned in one study and found to have negligible participation (Semenza et al., 2008).

To summarize, there was limited participation in activities that might reduce pollutant emissions, 

therefore there are limits to the number of variables identified as significantly influential to these types 

of averting behaviors. The ability to participate in and/or having active support for such voluntary 

emission reduction programs (i.e., car-pooling, ride-share), as well as being vested in the ultimate 

response (e.g., you or your child has a health condition known to be affected by pollutant emissions) 

appear to be the most influential variables.
2.2 Averting behavior to reduce exposure

Many of the reviewed studies were designed to investigate averting behavior in response to an air 

quality alert. Commonly, the air pollutant responsible for most alert notifications is ozone hence, a 

variable of interest used to evaluate averting behavior is how a person’s outdoor activities are changed 

on alert days compared to non-alert days. The exertion level of the person (e.g., performing less 

vigorous activities outdoors) as well as modified time expenditure in microenvironments (e.g., increasing 

time indoors or less time outdoors) are typical averting behaviors that may potentially reduce exposure 

concentrations to ambient air pollutants, particularly ozone, because ozone does not persist in indoor 

environments. These two features, exertion level and time expenditure, are discussed below. For the 

most part, we attempt to distinguish between the two features, although at times for convenience and 

flow, the overall discussion theme may be driven largely by the influential variable (e.g., day-of-week). 

We begin the discussion with an evaluation of the literature that reported changes in outdoor activity 

level.

Studies have identified three important variables that influence outdoor activity level, particularly 

considering those persons most susceptible to the effects of air pollution. These variables are 1) alert 

awareness, 2) understanding the link between air quality and health responses, and 3) personal health 

status. As discussed above regarding awareness, those individuals potentially at greatest risk are not 

only more aware of their local air quality system but also more likely to have been informed of health 

risks by a health professional (KS DOH, 2006). Asthmatic adults, either aware of the alert system or able 

to link poor air quality with adverse health responses, are estimated as having a two-fold likelihood of 

reducing their outdoor exertion level on alert days than those adults without asthma (KS DOH, 2006; 

Wen et al., 2009). Those advised by a health professional had an even greater likelihood of adjusting 

outdoor activity level, regardless of whether they were asthmatic or not (KS DOH, 2006; Wen et al., 

2009). Noonan (2010, 2011) also reports potential reduction in outdoor event participation on smog 

alert days, particularly notable for those observed exercising (i.e., a 25% reduction). While awareness 

was not directly measured, participation in outdoor activities on smog alert days was also reduced for 

three other study groups: a cohort comprised of all study participants, the second representing children, 

and the third constituting elderly persons, though only the elderly were found to be statistically 

significant (Noonan, 2010, 2011). 

While this link between professional advice and attention paid to the air quality alerts is strong, there 

may be a number of reasons for the limited rate of participation in averting activities. It may be that too 

few persons are getting this health communication (as discussed above regarding awareness), perhaps 

persons are overstating what they might do in response to air pollution alerts in response to the survey 

questionnaire, or it is possible that they are simply inconvenienced by a rigid daily (and weekly) 

schedule. For example, a recent study indicated that 55% of parents stated they restrict children’s 

outdoor play7on bad air quality days (McDermott et al., 2006). In an area that had, on average, 31 

 

7

In the McDermott et al. (2006) study, it is not clear if the changes in “outdoor play” pertain to adjusted outdoor 

time or activity level, and in my opinion, a lack of distinction I found common in many of the studies.
pollution advisories per year, most parents (74%) indicated that they would only restrict their child’s 

outdoor activity for fewer than 6 days per year (McDermott et al., 2006). While asthmatic children were 

more likely than non-asthmatics to restrict their outdoor activities 3 to 5 days per year (34% vs. 17%, 

respectively), there was no difference between the two cohorts regarding a greater degree of 

compliance with air quality alerts. Simply put, there appears to be a limit to how many days per ozone 

season one might adhere to air quality alerts and associated recommendations, even for persons most 

at risk of adverse health effects.8 

Zivin and Neidell (2009) evaluated such an effect, i.e., changes in the averting response across three 

consecutive days, using outdoor facility attendance records in Los Angeles. Significant reductions in 

attendance were observed on the first alert day (i.e., about 15% lower at the Los Angeles Zoo, 8% at the 

Griffith Observatory9) however, on the two consecutive days that followed with smog alerts, there were 

no statistically significant reductions in attendance at either facility. These analyses suggest that even 

the notably significant, albeit overall limited degree of participation in averting eventually wanes in 

response to multiple (and consecutive) alerts. Interestingly though, this apparent lack of attention paid 

to the alert appears short-lived and averting “rebounds” when evaluating a slightly different 3-day 

consecutive period: the first an alert day, followed by no alert, and ending the period with an alert day. 

Zivin and Neidell (2009) reported a reduction in attendance on the 3rd day (an alert day) similar to that 

observed on the 1st day (also an alert day). Similarly,10 Sexton (2011) evaluated time spent engaged in 

vigorous outdoor activities and the effect of three consecutive days air quality alerts, though 

compensation in performing outdoor activities appeared to be delayed to the third day (that is, averting 

was noted on days 1 and 2). There were no studies indicating longer term effects of time and alert 

status on averting behavior related to outdoor time, though Henry and Gordon (2002) do find 

awareness significantly increasing over summer months, peaking in August then decreasing in 

September, suggesting a similar pattern may exist for interest in air quality alerts and active 

participation in averting behavior.11

One other potentially influential variable not thoroughly evaluated was the potential for regional 

differences in averting activities. While there were no significant differences in observed averting 

behavior rates, there were significant differences in evaluating whether air quality was poor or not in 

Houston, TX when compared with the rate in Portland, OR (Semenza et al., 2008). As stated above, the 

perception of how poor air quality is linked with poor health is an important driver for reducing outdoor 

 

8

It is also worth noting that the magnitude of the perceived threat, which could vary based on health condition, 

would tend also to drive the decision to avert for any single day (Whitehead, 2005) or perhaps multiple days.

9

The observatory has both indoor and outdoor recreation opportunities. 

10 Sexton (2011) had single, current day information on outdoor time/activity and evaluated the 1-day prior effects 

and the 2-day prior effects of alerts on that one day (i.e., the same single day was evaluated across a 3-day alert 

scenario). Zivin and Neidell (2009) evaluated the day-to-day variability in outdoor facility attendance for 3 days 

and the corresponding effect of alerts on each consecutive day. 

11 Henry and Gordon (2002) do not find a significant month-of-year influence on averting performed by either 

miles driven or trips taken, behavior already noted as having limited participation rate for the population studied. 

As discussed here, there is a stronger relationship between pollution avoidance behaviors and awareness than 

pollutant emission reduction behaviors.
activity level (KS DOH, 2006; Wen et. al, 2009). Geographic differences may explain why slightly fewer 

Kansans (21.8% of asthmatics, 7.7% of non-asthmatics) changed their outdoor activity level compared 

with that estimated for six states (25.6% of asthmatics, 12.0% of non-asthmatics). A smaller though not 

similarly patterned difference was observed between Kansans (32.6% of asthmatics, 13.7% of nonasthmatics) and in six States (31.1% of asthmatics, 16.1% of non-asthmatics) considering those who 

adjusted their outdoor activity base on air quality alerts. Mansfield et al. (2009) and Houtven et al. 

(2003) report variable influence of location on outdoor time, though mainly not statistically significant, 

in three broadly defined regions (Northeast, Southeast, and West Coast).

Three key features affecting outdoor time reduction appear to be the magnitude of the alert or ozone 

concentrations, the particular time-of-day, along with health condition severity. As far as changes in 

time expenditure, Mansfield et al. (2006) estimates that asthmatic children reduce their time spent 

outdoors by 30 minutes on code-red alert days relative to green, yellow, or orange days.12 In a related 

unpublished paper, Mansfield et al. (2009) observed that in air pollution sensitive asthmatics, the 

reduction in time spent outdoors can occur during the afternoon hours when ozone concentrations are 

highest. Neidell (2010) also found that averting during the daytime hours can be greatest when the 

highest alert level is issued, rather than when lower alert thresholds are exceeded or when an outdoor 

event can take place during evening hours. Sexton (2011) showed that reduction in time spent engaged 

in vigorous outdoor activities for the general population was on average about 21 minutes, occurring 

more so during the afternoon when compared to morning hours of an alert day (and 18 minute versus 

10 minute reduction, respectively). One significantly affected population subgroup from the same 

study, elderly persons, showed greatest reductions in outdoor time (approximately 65 minutes) in 

response to AQI values ranging from 175-199. Outdoor time expenditure appears to be influenced by 

high ozone concentrations, as Bresnahan et al., (1997) estimates people may spend about 40 minutes 

less per day when 1-hour daily maximum ozone concentration exceeds 120 ppb. A similar ozone effect 

was not shown in analyses performed by Yen et al. (2004) and Eiswerth et al., (2005) using either the 

daily maximum 1-hour ozone concentrations or ozone as a binary variable (exceed standard or not) 

though it was reported the severity of study participants asthma significantly influenced overall time 

expenditure (either indoors and outdoors) and their associated activity levels.

 

12 The citation for this reported estimate is an unpublished RTI report, “Mansfield et al. (2004). Averting behavior 

with respect to ozone alerts: do parents respond?” This specific report was not obtained. A related report, 

Houtven et al. (2003) analyzes a complete data set (768 households of all participating children aged 2-12, rather 

than the subset used in Mansfield et al., 2006). There is also a supporting analysis of the same “ozone averting 

behavior” (OAB) data that are now in CHAD in a technical communication authored by Graham Glen of Alion (WA4-

29). Neither of these evaluations reported a reduction in outdoor time for asthmatic children and, in fact, both 

indicated greater time spent outdoors by asthmatics on code red days. Interestingly, when sub-setting for those 

persons identified as checking alerts did not change the results. However, an updated analysis of the same data (a 

draft report by Mansfield et al., 2009) does indicate that children with asthma (and for those noted by parent as 

“sensitive to air pollution”) do spend 0.33-0.35 hours less time outdoors (or about 20 minutes) on high ozone days 

when compared with low ozone days, while non-asthmatics show no such difference. This result may loosely 

correspond to the estimated outdoor time reduction of 30 minutes cited from Mansfield et al. (2004).
Furthermore, as described above for commuting behavior, the limited rate of averting by adjusting time 

spent outdoors for much of the population may also be influenced by personal circumstance or 

convenience, particularly noteworthy for employed adults. For example, Neidell (2009) found a 

significantly greater reduction in attendance at an outdoor facility on pollution alert days for children 

(22-25%) and the elderly (19%) when compared to the total attending population (13-15%). Sexton 

(2011) evaluated participation in vigorous outdoor activities among persons aged 15 and greater, 

showing the majority of outdoor time and activity reduction contributed by elderly persons. Children 

and elderly population groups are less likely to have rigid daily summer schedules and thus are more 

likely to avoid high pollution days when compared to working adults.

To summarize, averting to reduce exposure, whether through limiting outdoor time or exertion level, is 

evident, albeit not overly prevalent, among the study participants. Influential factors are similar to 

those identified above for awareness, that is, persons having a respiratory health condition and its 

severity (e.g., asthma) and whether the subject is aware of alert notifications and are able to connect 

poor air quality to a potential adverse health response. I estimate that perhaps 30% of asthmatics 

reduce their outdoor activity level on alert days, with the percent for the general population about half 

that (i.e., 15%). Children and the elderly are more likely to avert, while non-elderly adults appear to 

demonstrate very limited to no averting. Alert level also appears to be a factor, the greater the severity 

of the notification, the greater the likelihood of an averting response. When an averting activity is 

performed, I estimate that outdoor time/exertion, particularly during afternoon hours may be reduced 

by about 20-40 minutes in response to an air quality alert notification. There may be limits to the 

number of averting events one might engage in over short periods of time, and potentially longer term 

patterns influenced by the month of the year and the number and accuracy of alerts issued. 

3. Impact of Averting Behavior

Based on the above discussion, the increased awareness of air quality alerts and corresponding averting 

behavior is beneficial for preventing adverse health outcomes in persons most susceptible to air 

pollutants, that is, individuals having asthma and/or other comprised respiratory condition or at a select 

life-stage. From an exposure and risk modeling perspective, the more prevalent public awareness 

becomes and the likelihood of engaging in averting activities increases, the greater the challenge in 

accurately estimating of epidemiologic-based concentration response functions and population 

exposure distributions if ignoring the influence of these factors. The general impact of each of these is 

described below.

3.1 Epidemiologic-related

Individual level exposure to ambient air pollutants is neither modeled nor measured in most 

epidemiologic studies, though inhalation exposure has increasingly become an important consideration 

in such studies, particularly when short-term health effects are of interest and when exposure 

concentrations are expected to have greater spatial and temporal variability than ambient monitor 

concentrations. When ambient monitor concentration variability alone does not fully explain observed 

heterogeneity in health effect responses in epidemiologic studies (e.g., Bell et al., 2004), factors not 
accounted for such as variability in time expenditure or outdoor exertion level (examples of so-called 

exposure or measurement errors) are identified as potential explanatory variables (ISA section 4.1.2). 

Simply put, the strong correlation between personal ozone exposure and ambient concentrations (0.3-

0.8, ISA section 4.3.3) will be influenced by averting behavior, such that as averting behavior increases 

on high ozone concentration days, the estimated correlation between the two will fall. It follows that, 

when ignoring averting behavior which reduces ozone exposure (and is predicted to increase over time 

with improvements to air quality systems), existing and newly estimated ambient concentration-health 

response functions used to estimate risk could be biased low (Bateson et al., 2007), perhaps even 

approaching a null relationship (ISA section 4.12). 

The ISA exemplifies a recent epidemiologic study supporting this effect (Neidell and Kinney, 2010; 

Neidell, 2009), whereas the numbers of asthma-related hospitalizations for children and elderly persons 

were estimated to be reduced by a factor of 2.6 and 1.4, respectively when not accounting for averting 

behavior. Ultimately, this influential factor is one of the identified uncertainties in our health risk 

estimates. This potential underestimation bias in estimated concentration-response functions would 

vary based on the expected magnitude of averting that may occur by the types of persons within a 

particular epidemiologic study. Over time however, averting behavior will become increasingly 

important to account for when estimating these epidemiologic concentration health response functions 

as more persons may become aware and thus actively participate in averting to reduce their exposure.

3.2 Exposure-Related

Critical to any inhalation exposure calculation are the elements of duration, pattern, frequency, and 

magnitude of exposure, each of which are driven by the location of pollutant contact and the activities 

persons perform. When one ignores the spatial and temporal patterns in pollutant concentrations13 and 

human-time-location activity patterns, upper estimates of exposure (commonly the persons of greatest 

interest in an exposure or risk assessment) will always be biased low. That is why when modeling 

human population inhalation exposure, the best approach uses real time-location-activity diaries to 

more accurately simulate contact with pollutants in their immediate breathing environment.

As described in the ozone REA (section 5.3.2), the diaries used in our APEX modeling were obtained from 

several different studies, data gradually collected over the past few decades and structured specifically 

for use in our time-series exposure modeling approach. The original studies themselves had their own 

set of objectives, some of which were exposure-related, while others were not, though most of the 

studies were not designed to estimate or directly capture averting behavior per se. This is not to suggest 

that there are not diaries where persons may have exhibited a particular averting behavior. All of the 

earlier studies in the Consolidated Human Activity Database (CHAD; many are from 1980’s-2000) could 

contain diary data from individuals that may have performed averting to some extent, either influenced 

by elevated ozone concentrations occurring at the time the diary record was created or perhaps 

 

13 This is particularly notable for ozone, as concentrations vary both temporally (typically, peak concentrations 

occur in the afternoon hours) and spatially (principally, outdoor microenvironments have the highest 

concentrations).
resulting from the individual’s knowledge of the AQI or other local alert system (albeit a likely limited 

fraction of persons). It is possible that there are a greater number of individuals that engaged in 

averting behaviors considering the most recent study data that are now incorporated into CHAD 

compared with some of the historical CHAD data. The NSAS study14 is one such recent study that, in 

addition to perhaps capturing a relatively greater number of persons aware of air quality systems and 

alert notifications, was also designed to potentially capture averting on alert days (i.e., there was an 

over-sampling of data from ozone alert days). However, even considering this somewhat non-random 

study design, the highest alert level (i.e., code red) occurred only on 8.6% of diary days. Therefore, 

given all the CHAD diary days available (>40,000 person-days) and considering the rate of averting 

behavior, it is likely that only a small percentage of the diaries would represent persons exhibiting 

averting behavior. It is also important to add that at this time, none of the diary days used by APEX have 

been identified as representing days where a person did or did not perform an averting behavior to 

reduce their exposure.

When using all of these diaries to represent what people might do and where they might go within our 

exposure modeling domain, and have them applied somewhat randomly regarding averting behavior,

the anticipated effect on exposure estimates is somewhat complicated and dependent on how and how 

often the particular diaries are used in constructing daily activity pattern profiles. To further elaborate, 

in modeling multi-day exposures we use an approach that links CHAD diaries together based on a userselected, important exposure variable, e.g., time spent outdoors. The longitudinal diary profile 

generated for each individual is neither perfectly correlated among all days nor completely random, 

though each simulated person will have a diary profile that maintains some degree of correlation across 

a multi-day period (i.e., simulated persons who spend more time outdoors than others do so 

consistently over the duration of the model run). Therefore, even though the diaries that may have 

expressed averting behavior due to ozone alerts are not directly identified, it is possible that a simulated 

person who typically spends significantly more time outdoors than most other persons, will have a diary 

day (or more, depending on the duration of the model run) where time spent outdoors is limited.

The daily diary chosen by the model is not directly matched with ozone concentrations using this 

longitudinal approach however, we do use temperature as a selection variable to best match the 

weather conditions of the day, an important variable influencing time spent outdoors. As the weather 

conditions are generally linked to ozone concentrations, the diary days will, some of the time, be 

correlated with ozone concentrations. I state “some of the time” because neither of the two 

relationships, outdoor time-to-temperature and temperature-to-ozone, are entirely linear. For 

example, it is possible that some simulated individuals that normally spend a lot of time outdoors will 

appear to ‘avert’ because a diary was selected that had reduced outdoor time in response to a high 

temperature (rather than high ozone, if occurring on that day). 

 

14 The National Scale Activity Study (NSAS) conducted in 2009 was designed to capture additional elements of 

averting behavior not commonly investigated, that is, by collecting individual questionnaire responses along with 

daily time location activity patterns. We plan on evaluating this new several city data set and presenting results in 

future drafts of the ozone REA.
Regardless, I could speculate any number of scenarios that could occur in our exposure model 

simulations, some simulated persons will appear to exhibit averting to high ozone on some days, while 

others will not. Averting may be taken into account in our exposure modeling, albeit to an unknown 

degree,15 though definitely generating exposure estimates that would be biased low if one were 

interested in estimating exposures that would occur in the complete absence of averting behavior. 

The principal issue is that neither the simulated nor correct number/fraction of persons averting is a 

known quantity for any study area. We could attempt to better characterize the affect of this influential 

variable on exposure estimates by perhaps, constructing a shorter-term exposure scenario (e.g., 2 

weeks) using an unbalanced (but proportionally known) selection of diaries designated as 

“representative” of averting behavior and compare exposure results with the standard diary selection 

approach. However, we expect that, given the duration of the standard exposure simulations (i.e., an 

entire ozone season) , understanding variability in human time-location-activity patterns,16 and the 

potential for decreasing likelihood in multi-day averting for “real” people, it is likely that even if the 

proper proportion of the population (including asthmatics) exhibiting averting behaviors was known, the 

most significant impact would be to estimates of multi-day exposures above high exposure levels 

occurring in a year. Based my review, in areas having several or more days at or above the highest alert 

levels, I estimate that accounting for averting would likely have little to no effect on our estimated 

number of persons with at least one exposure at or above a selected upper exposure level and likely 

little effect on the number of persons with a few (e.g., two to three) multi-day exposures in a year at or 

above the same upper exposure level. Further, in areas having the only a few ozone alert days in a year, 

accounting for averting could potentially affect the number of estimated persons at or above select 

exposure levels for both single- and multi-day exposure in a year. 

 

15 Neither the participation rate nor the duration of averting for simulated persons is being strictly controlled for by 

the model.

16 While we use of the term “pattern” that may imply there is a rigid regularity to our daily lives as individuals, it is 

truly not the case.
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Respect to Ozone Alerts. For presentation at AEA/ASSA Meeting, San Diego CA, January 2004.

KS DOH. (2006). Environmental Factors, Outdoor Air Quality, and Activity Level. Results from 2005 

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Zivin JG, Neidell M. (2009). Days of haze: Environmental information disclosure and intertemporal 

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Economic Research (NBER) Working Paper 17004. Available at: 

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effective population exposure. Environ Monit Assess. 156(1-4):539-549.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Blanken 2001 Atmospheric 

Environment

281 commuters 

(aged 18-65) in 

Boulder/Westmi

nster, CO. Nov 

1999 

yes AWARE: 94% of 

persons aware and 

93% know what it 

means.

Commuting: 76% of car commuters said 

never change commute mode, 12 % 

adjusted commute on red advisory days 

when convenient.

Self administered mail survey to 1,000 

commuters. 76% of commuters were single 

passenger cars. For the mandatory restriction 

of wood burning, participation may be biased 

(lawful desirability) a bit high.

Wood Combustion: 86% of those who 

burn wood said did not burn on red 

alert days (it is a mandatory restriction).

Blomquist 2004 Review of 

Economics of 

the Household

no Mainly about VSL and WTP, theory and such, 

nothing relevant for our project that I could 

discern.

Bresnahan 1997 Land 

Economics

226 COPD 

(presumed 

adults mean age 

48, mostly 

males) subjects 

over 2-5 days in 

a year (928 total 

days) in Los 

Angeles (July 

1985-86), 

containing a 

“disproportionat

e number of 

individuals with 

compromised 

respiratory 

function”

yes Leisure Activity: 39% said altered when 

"smoggy"

Experiencing symptoms was 

significant for activity and indoor 

(Table 3). Having chronic 

respiratory impairments (asthma, 

hay fever, other) was not 

significant. Education (HS diploma) 

was weakly significant (and 

positive) with respect to altered 

activity. Their “composite averting 

variable” also significant with 

symptoms (Table 4). 

The author defined “smog-related symptoms” 

are used to indicate sensitivity to air pollution. 

The symptoms (eye irritation, headache, chest 

tightness) are broadly applicable (e.g., 

allergens) though the inquiry includes the 

experience is aligned with “smoggy 

conditions” (page 345). The authors do not 

define “altered”, “more”, and “smoggy”. In 

my opinion, HS diploma vs no HS diploma are a 

weak representation of “schooling” or 

education, even considering 1985.

Indoor Time: 40% said increased when 

"smoggy"

Ran A/C: 20% said when "smoggy"
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Outdoor Time: reduced by few 

variables (see Table 5). Outdoor time 

could be reduced by 40 min/day on bad 

ozone days for sensitive individuals. 

Note also a reduction in the high temp 

by 4 degrees F would reduce outdoor 

time similarly to that of 0.1 ppm 

increase in ozone above 0.12 ppm 

(about 6 minutes per day). 

Significant: #workdays (-, most 

significant), hayfever (-), other 

chronic resp conditions (+), 

temperature (+), high O3 

concentrations (> 0.12 ppm) . Not 

significant: Asthma (-), symptoms (-

+), humidity (-), pollutants (+-). 

The variable symptoms is now 

virtually negligible, though when 

linked by interaction with pollutant 

concentrations, collectively are 

significant (though individually not). 

I wonder why “workdays” was not considered 

in the first set of analyses where “symptoms” 

were driving much of the averting response. 

Respiratory conditions did not significantly 

influence the averting behaviors reported 

(table 3 and 4), though having hay fever 

reduced outdoor time by nearly an hour per 

day while having other chronic pulmonary 

conditions actually increased outdoor time by 

about 3⁄4 hour per day (Table 5). This attribute 

was not well discussed. The temperature 

variable (daily max) is not likely linear, as there 

is a range of values that will be comfortable –

deviations will occur outside the range (too 

cold or too hot).

Bresahan 1995 Journal of 

Environmental 

Economics and 

Management

no There is no data on averting, just theory and 

models.

Cheng 2007 Science of the 

Total 

Environment

AQ analysis at 5 

monitoring 

stations in 

Taiwan using the 

6 criteria 

pollutants (6 for 

AQI/RAQI, 5 for 

PSI – no PM)

no Compared old PSI with standard AQI a 

revised AQI (RAQI). The RAQI and AQI 

indicate more unhealthy AQ days 

(though mistermed “accuracy” in the 

paper)

There are inadequate comparisons of AQI to 

RAQI. Conclusions are drawn regarding the 

“accuracy” of the RAQI and its ability to 

distinguish local sources from sandstorms, 

however, similar treatment is not given to the 

AQI. Data in the text (pg 194) for one station 

indicate AQI more sensitive than RAQI.

Also, of course the AQI and RAQI are sensitive 

to PM2.5 than the PSI, they both use the 

PM2.5 data.

Courant 1981 J Environ Econ 

Manage

no Logical discussion regarding willingness to pay 

for air quality and averting. Conclusions are 

inconclusive and thus uninformative to the 

discussion.

Cutter 2009 J Env Econ 

Manage

Traffic counts 

and Mass Transit 

usage in San 

Francisco and LA 

CA, jun-oct 

2001-2004

yes Commuting: Auto traffic volumes 

decrease by 3-3.5% on STA days. Rail 

use increases 1% (though not 

significant)

morning time of day significantly 

depressed volumes

STA is spare the air in SF. LA does not have a 

corresponding program.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Henry 2002 Journal of 

Policy Analysis 

and 

Management

2,935 Adults 

(head of house) 

in 13 county 

metro Atlanta, 

GA. Single-day 

rolling 

population 

survey over 

May-Sep 1998, 

703 persons on 

alert days.

yes AWARE: of O3? Significant (+): Knowledge of alert, 

age, income, non-minority, day of 

week, month, urban location, read 

front page newspaper, commuting 

pattern.

Support for program appears to be an 

important variable, much like in the averting, 

recommendations from a health care 

professional are highly influential in modifying 

behavior.

Commuting: Significantly less miles 

driven on alert days (15.5% reduction) 

compared with non-alert day. Number 

of trips was less on alert day but not 

significant.

No diff in gov’t employees drive 

miles (and # of trips) 

(reinforced/supported SIP goals) vs 

non-gov. but on alert days, gov 

miles (and # of trips) were 

significantly reduced.

Kansas 

Department 

of Health 

and 

Environmen

t

2006 Technical 

Report

~4,300 KS adults 

(BRFSS 2005 

data, see Wen 

for all BRFSS)

yes AWARE: 42.7% Total 

Population; “urban” 

Kansas City MSA 

(70%) compared 

with Wichita and 

Topeka (37%).

Health: asthmatic 46%, 

nonasthmatic 42.5% among other 

conditions. Income (+), age (+), 

urban (+).

Asthma cohort 

defined by ‘Yes' 

to both “have 

you EVER been 

told by a doctor, 

nurse, or other 

health 

professional that 

you had 

asthma”? And 

'STILL' have 

asthma?

Outdoor Activity Level and AQI: Total 

Pop: 15.2% of those aware of AQI

changed outdoor activity level.

Asthmatic: 32.6%, Nonasthmatic: 

13.7%; Urban (19.4%) non-urban 

(<= 10%); income (weakly -) 

In comparison with Wen (2009) data above, 

most of these percentages are on the lower 

end of the range indicating potential for 

regional differences in percentages, though 

overall may not be a very influential attribute. 

The one small difference was asthmatics 

aware of AQI, Wen reported 31% reduced 

activity level (compared with 32.6% here for 

KS).

PERCEPTION: 6% of 

population felt poor 

AQ affects health

Outdoor Activity Level and Perception: 

Total Pop: 8.7% of population reduced 

outdoor activity level because they felt 

AQ was bad. 64% of those who 

perceived AQ as bad modified behavior 

based on AQI (only 8% of those who did 

not perceive AQ as bad and were aware 

of AQI modified behavior)

Asthmatic: 21.8%; Nonasthmatic: 

7.7%; diability 14.0% nondisability 

7.5%; urban 10.7%, non urban 

<8.2%; income (weakly -), age 

(weakly +)

Outdoor Activity Level and Advice:

Very few persons (4%-total, 15.7% 

asthmatics, 3.1% non)) received advice 

to reduce activity level when AQ poor

~41% of those advised did alter 

activity level at least once because 

they felt AQ was bad, 46% of those 

advised did based on AQI

The link between professional advice and 

attention to AQI is strong, though the word is 

not getting out regarding the AQI to the health 

professionals.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Mansfield 2003 Technical 

Report: Task 4 

RTI report for 

BHubbell.

National survey, 

6,106 persons 

aged 18+ years 

at county level, 

year 2000

yes AWARE: 33% of 

total population 

aware of AQI 

age (+), gender (M-), race (blk+,w+), 

health (+), education (+, BA/BS 

graduates), employed (+), income 

(+); increased to 37% when 

selecting county that had alerts 

currently/previously (+)

Table 4-1 is lacking some of the appropriate 

N’s to reproduce the reported percentages.

Outdoor Time: Of those persons 

reporting high AQI accurately, 57% 

reported spending less time outdoors 

on ozone alert days (orange or greater).

male (-), white (-), high income (-), 

health (-), whether county had 

alerts currently/previously (+)

Note only 422 persons ID'd the high alert day 

and adjusted outdoor activities (that’s 26%), 

suggesting again that most people that are 

aware still do not really adjust behavior. It's 

probably because of the other vars: employed, 

income, thus time restricted already

Commuting: Of those persons reporting 

high AQI accurately, 56% reported 

driving less, (combined - 48%) on ozone 

alert days (orange or greater).

male (-), white (-), high income (-), 

health (-),whether county had 

alerts currently/previously (+)

About 28% of those aware of overall system 

(not those who were correct) modified 

behavior

Mansfield/H

outven

2009/

2004

ASSA Meeting 469 parents of 

children 2-12 

years old - OAB 

CHAD

yes AWARE: 91% asthma (+), number of children in 

family (+), white (+)

Outdoor Time: Stated behavior - 68% 

said would reduce time spent outdoors, 

39% said would reduce activity level

time: asthma (-), red alert (-), 

number of children (+), 

temperature (-); level: asthma (-), 

red alert (-), temperature (+)

762 parents of 

children 2-12 

years old - OAB 

CHAD

Outdoor Time: Diary Data - Indicate 20 

minutes less time spent outdoors by air 

pollution sensitive asthmatics on code 

red days compared with others

Note Figure 6 shows in air pollution 

sensitive indivduals, the reduction 

is greatest during the afternoon 

hours

The general findings in Houtven (2003) are 

consistent with Alion WA4-29 memo written 

by graham glen, an analysis of the OAB data in 

CHAD (that is, no difference). Reanalysis by 

Mansfield (2009) considered more personal 

attributes of the asthmatics

Mansfield 2006 Resource and 

Energy 

Economics --

see also 

Mansfield 

(2009)/Houtve

n (2004)

Sub sample of 

231 parents of 

children 2-12 

years, 45% 

asthmatic, from 

35 MSA’s with 

most AQ alerts, 

2002 ozone 

season (OABCHAD study)

yes AWARE: 61% said 

aware of AQI

Paper is mostly about WTP. A working paper is 

cited for the second result, “averting behavior 

with respect to ozone alerts: do parents 

respond?” I do not have this paper though it is 

found in Mansfield (2009) as 20 minutes. This 

used some of the “OAB” diary data we have in 

CHAD. 

Outdoor Time: persons with asthma 

reduced time by 30 minutes on Red AQ 

days relative to Green, Yellow, or 

Orange days.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

McDermott 2006 Journal of 

Asthma

Parents of 

nonasthmatic 

(98) and 

asthmatic (110) 

children > 4 

years old in Salt 

Lake city, UT. 

Convenience 

survey. Jan-Sep 

2003

yes AWARE: 88% aware 

of AQ alerts

95% asthmatics vs 80% non The asthma cohort was defined as "parents 

who reported that their children had asthma”, 

note 25% of asthmatics were 

moderate/severe, 72% believe are air 

pollution sensitive, 88% controlled. No tests 

were done to discern any relationships of 

asthma severity with survey responses.

PERCEPTION: 91% 

felt poor AQ affects 

health

no sig diff in asthma vs nonasthma

Fossil Fuel use: 71% say reduce when 

AQ is poor 

asthmatics 79% vs non-asthmatics 

61%

Outdoor play: 55% say restrict when 

AQ poor

64% asthma vs 45% nonasthmatics.Restriction is largely limited to only a few days 

per year for both cohorts (between 0-5 days), 

though greater percentage of asthmatics 

(31%) restrict 3-5 days vs 17% for non 

asthmatics

Unclear whether metric is outdoor activity 

level and/or time.

Neidell 2009 The Journal of 

Human 

Resources --

Note: 

associated 

with Zivin 

(2009), Neidell 

(2010), Neidell 

and Kinney 

(2010) and 

Neidell (2005) 

Working Paper 

TR25. 

Attendees of 

two outdoor 

public arenas in 

Los Angeles for 

years 1989-1997, 

the LA Zoo 

(1,949) and 

Griffith Park 

Observatory 

(1,770). 

yes Outdoor time surrogate: reduction in 

attendance at both public arenas on 

ozone ‘smog’ alert days (Zoo 13-15%, 

Observatory 3-6%).

Children (22-25%), elderly (19%), 

and locals (GLAZA club members, 

19%) were more likely to avoid 

outdoor zoo event in response to 

an alert than others.

1) GLAZA member cohort may be biased 

regarding age distribution (possibly having 

more children and elderly) compared to 

others. 

2) Not implicit as to whether outdoor time 

reduced, just attendance at these outdoor 

events. Given increases (not statistically 

significant) in attendance at Dodger/Angel 

baseball games (control) for alert days: 1) 

there may be an active decision in response to 

the alert (avoid zoo/observatory and attend 

game instead) OR 2) having plans for baseball 

game (some coinciding with O3 alert day) 

precluded attendance at zoo/observatory.

3) In Neidell (2006), Similarly modeled 

attendance reductions for an Arboretum (10%) 

are similar to the Zoo (11%).
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Asthma hospitalizations: Effects 

estimates (hospital admissions) are 

reduced by a factor of 2.6 in children 

and by a factor of 1.4 in the elderly 

when not accounting for alert info. Not 

significantly different C-R function in 

adults. Increase in hospitalizations per 

10 ppb ozone: 0.037% children, 0.022% 

adults, 0.031 elderly.

4) While they conclude that the averting 

response for adults is insufficient to affect the 

C-R relationship, is it also possible that adults 

are not responding to ozone as would a 

susceptible population like children or elderly?

Neidell 2010 J Epidemiol 

Community 

Health -- Note: 

associated 

with Zivin 

(2009) and 

Neidell (2009). 

Attendees of 

two outdoor 

public arenas in 

Los Angeles for 

years 1989-1997, 

the LA Zoo 

(1,949) and 

Griffith Park 

Observatory 

(1,770). 

yes Outdoor time surrogate: greatest 

reduction in attendance on ozone 

‘smog’ alert days (15%), compared with 

health advisory days (7%) and 

'unhealthful' advisories (2%, not sig). 

Reduction at observatory (3%) but no 

difference across advisory days.

Patterns among children, elderly 

and GLAZA were significantly higher 

than overall population as observed 

in Neidell (2009)

Not implicit as to whether outdoor time 

reduced, just attendance at these outdoor 

events. 

Time of day could be a significant 

influential factor, as the 

observatory is mostly an evening 

hour event

Neidell 2004 Journal of 

Health 

Economics

California 

children (0-18) 

asthma hospital 

admissions, 

1992-1998, 

monthly data.

no Asthma hospitalizations: O3 negatively 

correlated with hospital admissions, 

though if accounting for smog alerts, 

the effect became less.

SES was determined significant 

factor for estimated ambient 

pollution levels for cohort as well as 

asthma hospitalization admission 

rate.

It seems unusual that CO is significantly 

correlated with asthma admissions, while 

other pollutants (with far better health effects 

relationships like O3 and NO2) are not.

Noonan 2010/

2011

Fourth World 

Congress of 

Environmental 

and Resource 

Economists/AE

I Working 

Paper 20011-

08

Commuting: 

8,069 Atlanta, 

GA households 

(21,323 

persons), April 

2001-02, no July. 

Outdoor Park: 

4,258 groups in 

summer 2005. 

Also, ATUS data 

analysis 12,810 

observations 

(2004-06) 

yes Commuting: vehicle miles traveled 

(VMT) are 26% higher on smog alert 

days (>0.084 ppm) . ATUS daytime 

(6AM-3PM) travel is reduced on smog 

alert days. 

income (+), education (+), weekday 

(+)

Noonan (2011) reports range of 3-18% higher 

VMT on smog alert days and not statistically 

significant, apparently using 2001 data alone.

Note overly influential were data 

for two long distance travelers 

(page 22)

Should have controlled for long distance 

travelers as they are likely doing planned trips, 

whether vacation or business related, would 

not likely be affected by alert.

Outdoor time surrogate: Park

attendance: Elderly and exercisers 

appear less inclined to perform outdoor 

recreation on smog alert days. Outdoor 

afternoon sports time not significantly 

affected on smog alert days.

Admittedly the Park generated data are 

potentially non-scientific, with graduate 

student judgments made regarding ages and 

activity levels.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Semenza 2008 Environmental 

Research

“Older” Portland 

(1,254) and 

Houston (708) 

residents 

(random), 

summer 2005-

06, Mean ages 

52 and 48,

respectively.

yes AWARE: Houston 

(89%) and Portland 

(82%) heard of 

ozone "action" or 

"watch" day

I could not reproduce some of the numbers 

reported in Table 4; I submitted an inquiry to 3 

authors to explain; no response to date. 

Unclear as to what health conditions were 

included, termed “extreme health”. Other 

health responses of “skin”, “fever” and 

“muscle” and “nausea” were not significant 

wrt heat index and are also poorly defined. 

Portland appears to respond to the heat 

questions which may be correlated with their 

answers regarding AQ.

Changed Behavior: 10.5% of Portland 

and 9.7% Houston residents changed 

behavior on AQ alert days, though 

largely in response to perceived poor 

air quality.

Behavior was said to be changed, not sure 

what constitutes a change. Results comparing 

influential factors in the two cities certainly 

indicate regional differences in perception, 

though limited differences in “averting”.

Commuting and Refueling: No change 

observed between advisory or control 

days in either city.

data not shown in paper, just mentioned.

Sexton 2011 Technical 

Report

~54,000 ATUS 

diaries (2003-

10), 15-85 years 

in age

yes Outdoor Time/Vigorous Activity: Total 

population reduced vigorous outdoor 

activity time by about 21 minutes, 

participation was about 3%. Elderly: 

59% or 65 minute reduction, 15% 

participation, driving much of what is 

observed for total population.

Elderly respond greatest to higher 

alert levels (AQI 175-199). Time of 

day: 31% or 18 minute reduction in 

afternoon, 79% or 10 minute 

reduction in morning. 

There are some issues with the ATUS data, 

mostly that the outdoor time at home is 

unknown, though some of the activity codes 

used could compensate (e.g., playing soccer 

"at home or yard" is likely outdoors).

Indoor Time/Vigorous Activity: No 

significant increase on alert days

For both of these, the total population was 

evaluated and not the elderly subgroup that 

appeared to be the only portion of the 

population averting.

Multiday Response: Not transitory. On 

two consecutive alert days, people 

appear to continue to avert

Shofer 2007 Am J Med Sci no Generally an informative plea for health care 

professionals to advise persons at risk of 

effects from bad air quality. Article actively 

promotes AIRNOW, AQI, NAAQS, and EPA. 

Creates a new acronym for use by clinicians, 

AIR: Ask, Inform, React 

Smith 1986 Economics 

Letters

425 households 

in Acton and 

Boston MA. 

Acton had 

no Install Water filters: positive (+) 

relationship with respect to 

individual perception of harm from 

hazwaste in water.

Older paper but serves to reasonably capture 

influences on one’s risk perception, though 

deck is a bit stacked: The influence of the 

media and study location are the strongest 
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

several incidents 

of hazwaste 

water 

contamination.

Drink bottled water: age (+), 

hazwaste perception (+), 

confidence in water supply (-), 

smoker (-)

variables positively affecting the selected 

averting behavior, next to smoking which 

correlated with a negative adjustment to 

behavior.

Attend public meetings: resident of 

Acton (+) and reader of hazwaste

news articles on town (+)

Wen 2009 Journal of 

Community 

Health --(see 

also KS DOH, 

2006)

2005 BRFSS 

data: About 

24,000 

nonasthmatic, 

4,000 asthmatic 

persons in CO, 

FL, IN, KS, MA, 

WI MSAs. 

Asthma cohort 

defined by ‘Yes” 

to “have you 

'EVER' been told 

by a doctor, 

nurse, or other 

health 

professional that 

you had 

asthma”?

AWARE: 46-49% 

were aware of AQI 

or AQ alerts. 

Asthmatic (49%) and non-asthmatic 

(46%) aware

Outdoor Activity Level and AQI: no 

totals, just asthma cohorts

Asthmatics (31%) and nonasthmatics (16%) reduced outdoor 

activity level. Gender (Females 

less), Disability (yes, less)

Curious as to the correlation between number 

of black/non-Hispanic women age>35 BMI > 

30 with a disability and under advisement of a 

doctor. This could be an important cohort to 

explain differences between the asthma and 

non-asthma cohort?

Outdoor Activity Level and Perception: 

no totals, just asthma cohorts

Asthmatic (26%) and non asthmatic 

(12%) persons say changed outdoor 

activity level because they felt AQ 

was poor. Combined AQI 

awareness and perception of AQ as 

bad was influential in reducing 

outdoor activity level (75%-

asthmatics, 68%-non-asthmatics).

Unclear what constitutes outdoor activity 

level, how much the change was, what is it 

relative too (are they very active persons or 

sedentary), and was the change also related to 

time spent outdoors. No measure of whether 

it was as often as the reported AQ alerts or 

whether they did in fact do it on a day when 

AQ was poor. Severity of asthma or

medication usage is not indicated as measured 

or influential. Also, region/state was not 

evaluated as influential.

Outdoor Activity Level and Advice: no 

totals, just asthma cohorts

Professional recommendation to 

reduce outdoor activity level when 

AQ is bad was a highly influential 

variable for both asthmatics (49%) 

and non asthmatics (42%)

The media alerts and perception of AQ as bad 

are important, however it appears that they 

are not linked very well, even with the 

professional advice being one of the most 

important variables. 

Whitehead 2005 Environmental 

and Resource 

Economics

895 persons, 

coastal NC. 

Follow up: 490 

same persons

no Significant finding on relationship 

between what is said is done and what 

is actually done regarding averting (an 

assessment of predictive validity of 

averting modeling).

The magnitude of adverse consequences and 

the degree of awareness will surely drive the 

level of predictive validity of the model. 

People are very likely to believe in the effects 

of a hurricane given the wide/thorough media 

coverage exhibiting the devastation associated 

with them. This is may be why the rate 

averting air pollution is not high; it is likely that 

most people are not aware of the warnings or 

are confused regarding the effects.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Wilson 2005 International 

Journal of 

Social 

Economics

203 Farmers in 

Sri Lanka

no Averting Related to Pesticide Exposure Defensive exposure measures is 

significantly related to education 

level, number of crops grown, freq 

of pesticide usage, number of 

pesticides used.

They should have evaluated the classes of 

pesticides separately (or insecticides alone) as 

the mammalian health effects vary widely.

Somewhat bothered by the relationship of 

cost incurred = amount of safety. "Most 

pesticide users do not even wear shoes!" 

However, it could be that variable protective 

expenditure serves as an indicator of 

associated training/education offered to 

prevent exposure. This is may be the best way 

to effectively reduce exposure in these areas 

(not simply an 8th grade education), rather 

than the seemingly wanton use of protective 

gear that many may not find effective or 

culturally acceptable to prevent health issues 

from occurring.

Yen/Eiswert

h

2004/

2005

Rev Econ Hous 1,779 diary days

of 64 adult 

asthmatics, 

Glendora, CA, 

Fall 1983.

yes Indoor/Outdoor/Work Active/Inactive 

Time: No significant effect noted except 

for time spent 'inactive outdoor leisure' 

(decrease with O3 > 120 ppb 1-h) and 

'indoor chores' (decrease with max 

daily 1-h O3).

Asthma severity was very 

important for decreasing outdoor 

time and increasing indoor time 

(Eiswerth, 2005)

The data are perhaps too old to observe an 

averting response (less likely people were 

aware and perceived air pollution as bad for 

health).

Zivin 2009 J Environment 

Econ Manag. 

Note: 

associated 

with Neidell 

(2009) and 

(2010). 

Attendees of 

two outdoor 

public arenas in 

Los Angeles, the 

Zoo (1,878) and 

Observatory 

(1,554) from 

1989-1978. 

Sample size is 

slightly smaller 

than Neidell 

(2009 and 2010).

yes Outdoor Time Surrogate: Significant 

reduction in attendance at both arenas 

on ozone ‘smog’ alert days (Zoo 15%, 

Observatory 8%).

Precipitation (68% reduction) and 

temperature are by far the greatest 

influential variables compared with 

the AQ alerts.

Interestingly, more people are concerned 

about getting wet than potential health effects 

associated with AQ.

Multiday Response: Transitory. On 

consecutive alert days, fewer to no 

people avert (2nd day: 5% Zoo, and 0% 

Observatory, 3rd day: 0% Zoo, 0% 

Observatory, none significant).

Children 2-12 exhibit the greatest 

avoidance on day 1 (~20%) while 

other age groups are barely 

distinguishable from the general 

population (~15%). However, 

children (<2 and 2-12) and elderly 

have less of a transitory response, 

they still are averting during 

consecutive alert days more so than 

typical adults.

GLAZA members may be comprised of a 

different population regarding age distribution 

(possibly having more children and elderly), 

possibly affecting conclusions (local vs not 

local). 

Zivin 2011 NBER Working 

Paper 17004

~1,600 CA 

Farmworkers 

over 155 days, 

2009-10

yes Outdoor Time Surrogate: farm worker 

schedule is not adjusted in response to 

ozone levels.

Although farmers in CA are only one type, this 

could have implications for outdoor workers in 

general.
Table 1. Raw notes extracted from all publications collected and reviewed that may have data or information relevant to the discussion of ozone averting behavior. 

1

st Author Year

pub.

Source Cohort Relevant AQ Alert Awareness 

or Perception

Averting Behavior Influential Variables My Notes and Comments

Zujic 2009 Environ Monit 

Assess

Belgrade, Serbia no Used population weighted (and street 

canyon adjusted) air quality indices 

calculated from pollutants (SO2, BS, 

NO2) measured at available monitoring 

locations to better compare individual 

pollution contribution to poor air 

quality in an urban area. 

Not surprisingly, “black smoke” was 

the driver for most of the “poor”, 

“critical” and “bad” AQ days.

Interesting article, though telling of 

environmental monitoring and research in 

other countries. No ozone, a big weak spot.