The Impact of an Anti-IDLING Campaign on Outdoor Air Quality

The impact of an anti-idling campaign on outdoor air
quality at four urban schools
Patrick H. Ryan,*ab
Tiina Reponen,b
Mark Simmons,b
Michael Yermakov,b
Ken Sharkey,c
Denisha Garland-Porter,c
Cynthia Eghbalniad
and Sergey A. Grinshpunb
Idling school buses may increase concentrations of air pollutants including fine particulate matter (PM2.5)
and elemental carbon (EC) near schools. Efforts to reduce vehicle idling near schools have rarely included
air sampling to objectively assess changes in concentrations of air pollutants. The objective was to
determine the impact of an anti-idling campaign on outdoor air quality at four schools with varying
exposure to bus and automobile traffic. Outdoor air sampling for PM2.5, EC and particle number
concentration (PNC) was conducted at four schools for five days before and after an anti-idling
campaign. Sampling began before the morning arrival of buses and concluded after their afternoon
departure. Sampling was simultaneously conducted at four corresponding community sites. Differences
in PM2.5, EC, and PNC measured at school and community sites for each sampling day were calculated
before and after the campaign. Before the campaign, the average outdoor concentration of PM2.5
during the school day at three of the four schools exceeded community background levels and the
difference was greatest (4.11 mg mÀ3
, p < 0.01) at the school with the most buses (n ¼ 39). The largest
difference in EC between school and community sites was also observed at the school with the greatest
number of buses (0.40 mg mÀ3
, p < 0.01). Following the anti-idling campaign, the average difference in
PM2.5 at the school with the most buses decreased from 4.11 mg mÀ3
to 0.99 mg mÀ3
(p < 0.05).
Similarly, at this school, the difference in the EC level decreased from 0.40 mg mÀ3
to 0.15 mg mÀ3
and
PNC decreased from 11 560 to 1690 particles per cm3
(p < 0.05). The outdoor concentrations of
pollutants at schools with fewer buses (n ¼ 5–11) were not significantly reduced. The concentration of
air pollutants near schools may significantly exceed community background levels, particularly in the
presence of idling school buses. Anti-idling campaigns are effective in reducing PM2.5, EC and PNC at
schools with significant amounts of buses and passenger cars.
Environmental impact
Children spend a signicant amount of their time at schools where exposure to traffic-related air pollutants may be elevated due to idling buses. Though efforts
have been made to reduce idling near schools, there have been no studies, to our knowledge, which have quantitatively measured changes in air quality before
and aer an anti-idling campaign. In this paper, we report that traffic-related air pollutants are frequently elevated in the vicinity of schools compared to
residential neighborhoods. In addition, we demonstrate that an anti-idling campaign, a simple public health intervention, can signicantly reduce air
pollutants at schools with potential benets to the health of children who attend these schools.
1. Introduction
The areas immediately surrounding major roadways are
frequently referred to as ‘traffic hot spots’ – locations where
levels of traffic-related air pollution (TRAP) are elevated.
Residing in close proximity to these traffic sources has been
associated with both exacerbation and development of
asthma.1–3
The microenvironment of children, however, includes
locations outside of the home, including schools and inside
vehicles during transit to and from school. The school environ-
ment may be especially relevant to TRAP exposure as a nationwide
survey found that 20–44% of public schools in nine metropolitan
regions were located within 400 m of an interstate or highway
where levels of TRAP have been shown to be elevated.4
Few
investigations have addressed the health impact of school expo-
sures, though a longitudinal cohort study in southern California
has reported both home and school exposure to be associated
with the development of new asthma in school-age children.1
a
Cincinnati Children's Hospital Medical Center, Division of Biostatistics and
Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, ML
5041, Cincinnati, OH 45229, USA. E-mail: patrick.ryan@cchmc.org; Fax: +1 513-
636-7509; Tel: +1 513-803-4704
b
University of Cincinnati, Department of Environmental Health, Cincinnati, OH, USA
c
Cincinnati Health Department, Cincinnati, OH, USA
d
Cincinnati Public Schools, Cincinnati, OH, USA
Cite this: Environ. Sci.: Processes
Impacts, 2013, 15, 2030
Received 16th July 2013
Accepted 3rd September 2013
DOI: 10.1039/c3em00377a
rsc.li/process-impacts
2030 | Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 This journal is ª The Royal Society of Chemistry 2013
Environmental Science
Processes & Impacts
PAPER

Exposure to TRAP near schools may also be elevated due to
the impact of local traffic and, in particular, diesel-fueled
schools buses. Diesel exhaust particles (DEP), the majority of
which are ultrane in size (aerodynamic diameter <0.1 mm), are
comprised of an elemental carbon core with a large surface area
to which chemicals including polycyclic aromatic hydrocarbons
(PAHs) and transition metals may be attached.5
The presence of
school buses has been associated with signicantly increased
particle number concentration (particularly in the ultrane
fraction) in the near-school ambient air.6,7
Studies have also
reported that school bus idling can be a signicant predictor of
black carbon and PM2.5 concentrations in the school vicinity.8,9
To improve air quality near schools, many communities
support anti-idling efforts, retrotting of school buses with
diesel oxidation catalysts, and the implementation of alterna-
tive fuels including low-sulfur diesel fuel. However, to our
knowledge, anti-idling efforts have not been objectively evalu-
ated with accompanying measurements of health-relevant air
pollutants. The Cincinnati Anti-Idling Campaign (CAIC), a
partnership between academic researchers and community
members from local schools and health departments, was
designed to determine whether children are exposed to
increased levels of TRAP, including ultrane particles and
diesel-related elements, while at school and to develop and
implement a community-driven anti-idling campaign to reduce
exposure to TRAP at schools. The objectives of this analysis were
to: (1) compare ambient air monitoring data collected outside of
four urban schools to the background levels measured at the
corresponding community sites and (2) compare results of pre-
and post-anti-idling measurements performed at four schools
participating in the CAIC study in order to quantitatively
demonstrate the effectiveness of an anti-idling campaign.
2. Methods
2.1. Study sites
The study included four public schools, henceforth referred to
as Schools A, B, C, and D, selected to participate based upon the
prevalence of asthma reported by parents and potential expo-
sure to TRAP emitted from nearby major roads and by school
buses. Participating schools were selected from grade schools
(pre-Kindergarten through grade 8) whose prevalence of parent
reported asthma exceeded 10%. Of these schools, one was
chosen to participate from each of the following a priori dened
categories: (1) major road <400 m from school, low bus traffic
(School A), (2) major road >400 m from school, high bus traffic
(School B), (3) major road <400 m from school, medium bus
traffic (School C), and (4) major road >400 m, low bus traffic
(School D). For each of the selected school, an outdoor air
monitoring site was established. School sampling stations were
generally located at a school entrance nearest to the bus drop
off/pick up areas. In addition, the geographic area where chil-
dren attending each school reside was identied and an
outdoor community air monitoring site was established within
this catchment area. Site selection criteria for community sites
included being greater than 400 m from the nearest major road,
having access during the school day and electrical power.
Neighborhood community centers served as community sites
for Schools A, B, and D. A private residence was used as the
community site for school C.
2.2. School and community air monitoring
Pre- and post-anti-idling campaign air monitoring was con-
ducted for each school and their corresponding community
sites for ve school days in the spring of 2010 and ve school
days in the spring of 2011. For each school, air sampling at the
school and community sites was conducted concurrently.
Sampling was scheduled for days with forecasted precipitation
<6 mm and no unusual activities at the sites. A complete
description of the sampling methods is available elsewhere.6
Briey, sampling at both the community and school sites began
approximately 30 minutes prior to the rst school bus arriving
in the morning and concluded approximately 30 minutes aer
the last school bus le in the aernoon. Average pollutant
concentrations during this approximately nine-hour sampling
period (7 AM to 4 PM) were derived. At each sampling site, two
Harvard-type PM2.5 impactors (Air Diagnostics and Engineering,
Inc. Harrison, ME) were operated in parallel. The Harvard
impactor has a cut size of 2.5 mm at a sampling ow rate of 20 L
minÀ1
and particles were collected on two 37 mm lters: one
Teon (Pall Corp., Ann Arbor, MI) for PM2.5 mass measurement
and elemental analysis by X-ray uorescence and one quartz lter
(Whatman, Inc., Clion, NJ) for elemental carbon analysis by
thermal-optical transmittance. Two blank samples were collected
and analyzed for each sampling location. All analyses were per-
formed by Chester LabNet Inc. (Tigard, OR).
A portable condensation nuclei counter (P-Trak, Model 8525,
TSI Inc., St. Paul, MN) was also operated at each sampling
location to monitor the particle number concentration of
aerosol particles during the sampling period. The P-Trak is
capable of non-size discriminating real-time counting of parti-
cles from 20 nm to >1 mm. The particle number concentration
was recorded as a time-series with a resolution of 1 minute. For
this analysis, the average particle number concentration (PNC)
during the peak morning drop-off period of each school was
calculated as the average of the 1 minute particle concentration
data from 30 minutes prior to the start of the school day until
the start of the school day by which time all children had
entered the school. Data obtained from the community-site P-
trak instrument were extracted for the identical time period of
each corresponding school and the average PNC concentration
was calculated. Results of P-Trak measurements are expressed
as particles per cm3
.
2.3. Cincinnati anti-idling campaign
The objectives, methods, and results of the Cincinnati Anti-
Idling Campaign (CAIC) have been described in detail else-
where.10
Briey, the CAIC consisted of four components
including research and development, campaign activities,
online training videos, and implementation of the EPA Tools for
Schools. Key contacts including teachers, administrators, and
parents at each participating school were enlisted to assist with
the campaign. A school-bus driver educational program was
This journal is ª The Royal Society of Chemistry 2013 Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 | 2031
Paper Environmental Science: Processes & Impacts

presented to all bus drivers followed by an anti-idling pledge
drive. Information was also provided to parents accompanied with
a pledge to reduce idling. Other CAIC activities included school
bus monitoring, all school air quality assemblies, and anti-idling
signs placed near the school drop-off/pick-up zones. CAIC activi-
ties were conducted during the fall and winter 2010–2011.
Following the anti-idling campaign, nearly 400 bus drivers volun-
tarily pledged to stop idling which was conrmed by a signicant
reduction in observed vehicle idling time post-campaign.10
2.4. Statistical analysis
The objective of the statistical analyses was to compare
concentrations of PM2.5, EC, and particle number concentra-
tion (PNC) at schools to their respective community sites and
also to assess changes in these pollutant concentrations aer
the anti-idling campaign. Sampling data collected in the spring
2010 were considered pre-anti-idling campaign (baseline) and
data collected in the spring of 2011 were considered post-anti-
idling campaign data. Summary statistics of the concentration
levels for the air quality measurements were obtained for each
school–year combination. In order to characterize air quality for
each school and year accounting for community/background
concentrations, the concentration of each pollutant at the
community location was subtracted from the concentration at
the school site for each day of sampling (i.e., values >0 indicate
that the average concentration at the school site exceeds back-
ground levels). Thus, the difference in concentration levels
between the school sites and the community/background
location for each site was the primary outcome variable in the
statistical analyses. A general linear model was developed for
each of the three air quality measurements (PM2.5, EC, PNC) as
a function of the two xed effects representing the school site
and year, along with an interaction effect:
(concentration difference of X) $ b0 + b1ischool sitei + b2jyearj +
b3ij(school site) Â (year) + 3ij
where X ¼ the air quality measure (PM2.5, EC, PNC), b0
s ¼
regression coefficients, i ¼ school site (i ¼ 1,.,4), j ¼ year (j ¼
1,2), and 3 ¼ the residuals.
Concentrations of the selected air pollutants were not
available perfectly for all days of sampling at all sites due to
equipment failure creating an unbalanced study design.
Specically, one day of PNC data for School A was not available
in 2010. For School C, one day of EC data and two days of PNC
data for both 2010 and 2011 were missing. For School D, one
day of PNC in 2010 as well as two days of PNC and EC were
missing in 2011. There were no missing data points for School
B. The statistical analysis was performed using SAS Version 9.2.
PROC MIXED was used to model the unequal variances among
the different school–year combinations. The model assump-
tions of normality, heterogeneity, and linearity were assessed
for the tted model. The signicance of each main effect and
the interaction effect was determined using an F-test statistic
and the overall goodness-of-t for the model was assessed by
the log-likelihood ratio statistic and the Akaike Information
Criterion (AIC) statistic. Since the study design was unbalanced,
the least-squares (LS) means were computed for each school–
year combination and a t-test was performed to test the null
hypothesis that each specic school–year LS-mean equals zero.
This hypothesis test of the concentration difference was equiva-
lent to testing whether or not the concentration levels at the school
bus drop-off location were different from the community levels.
A test of simple effects was performed using an F-test statistic to
test the effect of year for each level of school, and vice versa.
3. Results
The characteristics of the participating schools are presented in
Table 1. Schools A and C were located less than 400 m from a
major road. However, School A was near a federal interstate
highway with approximately 7 times the number of daily
passenger vehicle traffic (130 198 vs. 17 080) and nearly 60 times
the number of daily trucks (17 305 vs. 290) than School C. The
number of buses at each school ranged from 5 (School A) to 39
(School B). School B also had the highest average number of
cars during drop-off (n ¼ 77) during the weeks of air sampling.
3.1. PM2.5 results
The average PM2.5 concentrations at school and community sites
before and aer the anti-idling campaign are presented in Fig. 1.
The concentration of PM2.5 at community sites ranged from 12.2
to 17.6 mg mÀ3
and PM2.5 concentrations at schools sites were
similar (13.1–17.9 mg mÀ3
) (Fig. 1A). The highest overall average
PM2.5 concentration was observed at School B, which was not
located near major roads but had, on average, 39 buses per day.
Following the anti-idling campaign, the concentration of PM2.5
at school sites ranged from 9.0 to 20.5 mg mÀ3
while background
concentrations ranged from 9.8 to 21.0 mg mÀ3
(Fig. 1B).
The average differences of PM2.5 concentrations between
school and community sites before and aer the anti-idling
campaign are presented in Table 2. Prior to the campaign, the
concentrations of PM2.5 at schools exceeded those at the
background site at three of the four schools, and was signi-
cantly greater than the background concentration at School B
(average difference D ¼ 4.11 mg mÀ3
, p < 0.01). Following the
anti-idling campaign, the average level of PM2.5 at School B was
the only location exceeding the background site (D ¼ 0.99 mg
mÀ3
, p < 0.01). The change in average school-background
differences due to the anti-idling campaign was signicant for
Schools B (4.11 to 0.99 mg mÀ3
) and D (0.48 to À1.35 mg mÀ3
). In
the case of School D, average community concentrations of
PM2.5 exceeded school concentrations aer the anti-idling
campaign. This signicant change may be a result of reduced
idling at the school resulting in community levels exceeding
school concentrations or changes in background concentra-
tions of PM2.5 in the community.
3.2. EC results
Fig. 2 presents the average concentrations of EC before and aer
the anti-idling campaign at school and community sites. The
concentration of EC at the four schools prior to the anti-idling
campaign ranged from 0.06 at School C to 0.77 mg mÀ3
at School
Environmental Science: Processes & Impacts Paper

B. Corresponding community site levels ranged from 0.05 to
0.62 mg mÀ3
and were lower than those at school sites with the
exception of School A (Fig. 2). Following the anti-idling
campaign, the concentration of EC at both school and
community sites increased for Schools A and C while decreased
EC concentrations were observed at Schools B and D.
Prior to the anti-idling campaign, concentrations of EC at
School B were signicantly greater than the corresponding
community levels (D ¼ 0.40 mg mÀ3
, p < 0.05). There were no
other signicant differences observed for EC between school
and community sites prior to the campaign. Following the
campaign, the concentration of EC at School B was no longer
signicantly elevated compared to background levels (Table 2).
In addition, the change in average difference between school
and community levels at school B was signicantly reduced
(0.40 to 0.15 mg mÀ3
, p ¼ 0.05). However, at School A, the average
difference between school and community levels increased
(À0.18 to 0.22 mg mÀ3
, p ¼ 0.01).
3.3. Particle number concentration results
Both before and aer the anti-idling campaign, the average PNC
at community sites exceeded that at school sites with the
exception of the school with the greatest number of buses
(School B, Fig. 3). Prior to the anti-idling campaign, the lowest
PNCs at both school and community sites were observed at
Table 1 Characteristics of participating schoolsa
Characteristic
School
A B C D
Year built 2007 2007 1962 2005
Distance to the nearest
major road* (m)
303 526 243 2083
Average daily passenger
vehicle count on the
nearest major road
130 198 8310 17 080 136 530
Average daily truck count
on the nearest major road
17 305 210 290 19 710
Average number of buses
per arrival/departure
5 39 11 9
Average number of cars/
drop-off
18 77 27 24
Prevalence of parental
reported asthma
10% 10% 15% 12%
Description of school/
community
Urban school/community
located near a major
interstate highway and
other industrial and
transportation sources
Urban school/
community with no
nearby transportation
or industrial sources
Urban/suburban school/
community with moderate
nearby traffic sources
Urban school community
with nearby major
industrial sources
a
Major road dened as U.S. interstate, U.S. highway, or state highway.
Fig. 1 Pre- and post-anti-idling campaign average PM2.5 (+1 SE) at school and community sites.

School C (10 480 and 10 920 particles per cm3
) while the largest
observed PNCs occurred at School A sites (Fig. 3). As seen in
Fig. 3, PNC sampled aer the anti-idling campaign was
generally lower than before the campaign at both community
and school sites. The differences in average PNC between
school and community sites before and aer the anti-idling
Table 2 Average difference (D)* in PM2.5, elemental carbon (EC), and particle number concentration (PNC) between school and community sampling sitesa
School
A B C D
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
DPM2.5
(mg mÀ3
)
À0.95 À0.52 0.77 4.11** 0.99** 0.04 0.9 À4.71 0.33 0.48 À1.35 0.03
DEC (mg mÀ3
) À0.18 0.22** 0.01 0.40** 0.15 0.05 À0.01 À0.04 0.55 0.16 0.01 0.27
DPNC
(particles per c3
)
À15 500** À3630 0.02 11 560** 1690 0.01 À440 920 0.59 À7250** À4130** 0.01
a
*Community site concentration subtracted from school concentration. D > 0 indicates that the average concentrations at the school site exceeded
community levels. D < 0 indicates that community levels exceeded those at school sites. **Difference in school and community concentrations (p <
0.05) signicantly differs from 0.
Fig. 2 Pre- and post-anti-idling campaign average elemental carbon concentrations (+1 SE) at school and community sites.
Fig. 3 Pre- and post-anti-idling campaign average particle number concentration (+1 SE) at school and community sites.

campaign are presented in Table 2. Before the anti-idling
campaign, the average PNC at school sites was signicantly less
than that at community sites for Schools A and C (D ¼ À15 500
and D ¼ À7.250 particles per cm3
) while at one location (School
B) the PNC at the school site signicantly exceeded background
PNC levels (D ¼ 11560 particles per cm3
, p < 0.01) (Table 2).
Following the anti-idling campaign, only School D continued to
have a signicant difference in PNC between community and
school sites (D ¼ À4130 p cmÀ3
). In addition, the difference in
school and community PNC differences was signicantly
reduced at Schools A, B, and D (Table 2).
4. Discussion
To our knowledge, this is the rst study to assess the effec-
tiveness of an anti-idling campaign by pre- and post-campaign
air monitoring for PM2.5, EC, and PNC. The results of this study
support the assumption that anti-idling efforts are successful in
reducing traffic-related particle exposure. In particular, we
observed signicant reductions in PM2.5, EC, and PNC at
School B, which had the largest number of school buses (n ¼ 39)
in the school vicinity. Changes in idling behavior at schools
with fewer buses (5–11) were not reected by changes in
measured air pollutants.
This research effort was undertaken as part of an academic-
community partnership with the objectives of determining
whether children are exposed to increased levels of TRAP at
schools compared to their communities, to develop and
implement a community-driven anti-idling campaign, and to
evaluate the effectiveness of the intervention to reduce TRAP at
schools with the goal of improving the health of children who
attend the schools10
The concurrent sampling of community
and school outdoor concentrations of PM2.5, EC, and PNC
allowed examining the changes in air quality at the schools
relative to changes in community air quality. In a study of PM2.5
and black carbon (BC, surrogate for EC) near four schools in
New York City, variability in background concentrations was
assessed using sites throughout the city and found to contribute
approximately 80% of the variability in air pollutants at the
schools.8
This study also found local traffic and diesel idling to
be signicant contributors to outdoor concentrations of these
pollutants measured at school.8
In our study, we attempted to
identify community sites within the catchment area for each
school in order to obtain representative ambient air measure-
ments for the neighborhoods in which children resided. Using
this approach, we found that in 3 of the 4 schools, community
levels of PM2.5 were not signicantly different from those at
school sites. These ndings are not unexpected given the large
spatial variation of PM2.5 (ref. 11) which exceeds the distance
from school to community sites. Nevertheless, our nding of
PM2.5 concentrations at School B exceeding community levels
suggests that local bus and car traffic at the school (which was
not located near major roads) are signicant contributors to
PM2.5 at that site.
Despite efforts to reduce idling near schools, there have been
few studies which measured the inuence of idling school
buses on air quality. Idling diesel school buses and trucks
produce particles primarily in the ultrane size range and these
have been associated with respiratory and neurologic health
effects due to their deposition in the lower respiratory tract and
potential to impact the brain.12,13
The small size and large
surface area of diesel exhaust particles allow for numerous
compounds to be attached to the elemental carbon core
including low-molecular weight hydrocarbons such as alde-
hydes, benzene, polycyclic aromatic hydrocarbons (PAHs) and
nitro-PAHs. Diesel exhaust is also comprised of sulfate, nitrate,
metals, and other trace elements which have been linked to
health outcomes.14
In the US, approximately 24 million students
are transported to school on nearly 600 000 school buses, the
majority of which are diesel fueled.15
Compared to emissions
following engine restart, idling school buses have been found to
contribute to signicantly increased concentrations of ne PM,
BC, EC, and ultrane particle number concentration.4,7,8,16–18
Short term exposure to DEP and UFP, as may occur during
school bus idling, has been shown to elicit acute decreases in
lung function and increases in neutrophilic inammation
among asthmatics in a roadside environmental experiment.19
A frequent limitation of many epidemiologic studies of air
pollution and health outcomes is the use of only home
addresses to characterize exposure. Children, in particular,
spend most of their time outside their home and exposures at
schools or in transportation to school are likely to account for a
signicant proportion of a child's overall exposure to TRAP.20
Despite this, there have been few studies which have examined
or accounted for school exposures and associated health
outcomes. In a French cross-sectional study, indoor air pollut-
ants, including formaldehyde and PM2.5, were found to be
signicantly associated with rhinoconjunctivitis and asthma
symptoms in the previous year.21
In the Southern California
Children's Health Study, the risk for developing new onset
asthma was found to be associated with exposure to TRAP at
both homes and schools.1
Components of the TRAP mixture, including EC, ultrane
particles (particles <100 nm in diameter), NOx, and other
pollutants, exhibit high spatial variability with elevated
concentrations within approximately 400 m or less from major
roads.11,22,23
It has been recognized that schools located in the
vicinity of nearby roads may have elevated levels of these
pollutants outside and studies in both the U.S. and Canada have
found a signicant number of schools located near major
roads.4,24
The impact of nearby roads on both schools and
communities is evident for School A, which is located near a
major interstate highway, and had the highest levels of PM2.5
and EC during the post-anti-idling campaign sampling period.
There are, however, several limitations to this study. Schools
selected to participate in the anti-idling campaign were chosen
based upon the prevalence of asthma among students, the
number of school buses, and nearby sources of TRAP. Cincin-
nati Public Schools, however, are primarily neighborhood
schools with many students living in close proximity. Thus, the
average number of buses serving K-8 schools in CPS is 9 and in
our study the number of buses ranged from 5–39. The impact of
the anti-idling campaign on air quality was particularly evident
at School B which had 39 buses. Therefore, we speculate that

anti-idling campaigns are likely to have the greatest impact on
air pollutant concentrations in school districts with a greater
number of buses. Another limitation to this study is the number
of available air measurements (5 pre- campaign, 5 post-
campaign) which does not provide information on long-term
trends of air quality near the participating schools. Meteoro-
logical conditions are also important factors which inuence
the dispersion of emissions and their impact. In particular,
wind direction may play an important role in the impact of
nearby traffic sources on sampled pollutant concentrations.
However, our primary outcome (differences in pollutant
concentrations between school and community sites) is less
likely to be impacted by meteorological conditions as these are
not expected to signicantly vary from each school to its cor-
responding community site on each day of sampling. Never-
theless, we acknowledge that local, short-term wind patterns
may play a role in the sampled concentrations. A particular
strength of our analysis is the use of a mixed linear model which
allows for the difference in daily school-community site
concentrations to be examined rather than average concentra-
tions for the sampling period (over which meteorological
conditions are more likely to vary). This approach also increases
the power of the analysis to detect signicant differences.
In conclusion, we have shown that the outdoor concentra-
tions of PM2.5, EC, and PNC are signicantly elevated
compared to background levels at a school with a large number
of school buses. In addition we have quantitatively demon-
strated a reduction in PM2.5, EC, and PNC aer an anti-idling
campaign. Despite a nationwide anti-idling campaign initiated
by the U.S. EPA (National Idle-Reduction Campaign, NIRC) as a
part of its Clean School Bus USA program, the School Health
Policies and Programs Study (SHPPS) has recently reported that
56.6% of surveyed school districts had not implemented any
school bus idling reduction program.25
Implementation and
enforcement of anti-idling statutes near schools should be
considered as part of a multi-faceted approach to improve air
quality at schools where children spend large amounts of their
time and may be exposed to freshly emitted diesel exhaust when
entering, exiting, or playing near idling school buses.
Acknowledgements
The researchers would like to thank the participating Cincin-
nati Public Schools. This research was funded by the National
Institute of Environmental Health Sciences, grant number
R21ES017957.
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