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Associations of PM sub(2) sub(.) sub(5) and black carbon
concentrations with traffic, idling, background pollution, and
meteorology during school dismissals
Richmond-Bryant, J | Saganich, C | Bukiewicz, L | Kalin, R
Science of the Total Environment [Sci. Total Environ.]. Vol. 407,
no. 10, pp. 3357-3364. 1 May 2009.
An air quality study was performed outside a cluster of schools in
the East Harlem neighborhood of New York City. PM sub(2) sub(.)
sub(5) and black carbon concentrations were monitored using
real-time equipment with a one-minute averaging interval.
Monitoring was performed at 1:45-3:30 PM during school days over
the period October 31-November 17, 2006. The designated time
period was chosen to capture vehicle emissions during end-of-day
dismissals from the schools. During the monitoring period,
minute-by-minute volume counts of idling and passing school buses,
diesel trucks, and automobiles were obtained. These data were
transcribed into time series of number of diesel vehicles idling,
number of gasoline automobiles idling, number of diesel vehicles
passing, and number of automobiles passing along the block
adjacent to the school cluster. Multivariate regression models of
the log-transform of PM sub(2) sub(.) sub(5) and black carbon (BC)
concentrations in the East Harlem street canyon were developed
using the observation data and data from the New York State
Department of Environmental Conservation on meteorology and
background PM sub(2) sub(.) sub(5). Analysis of variance was used
to test the contribution of each covariate to variability in the
log-transformed concentrations as a means to judge the relative
contribution of each covariate. The models demonstrated that
variability in background PM sub(2) sub(.) sub(5) contributes
80.9% of the variability in log[PM sub(2) sub(.) sub(5)] and 81.5%
of the variability in log[BC]. Local traffic sources were
demonstrated to contribute 5.8% of the variability in log[BC] and
only 0.43% of the variability in log[PM sub(2) sub(.) sub(5)].
Diesel idling and passing were both significant contributors to
variability in log[BC], while diesel passing was a significant
contributor to log[PM sub(2) sub(.) sub(5)]. Automobile idling and
passing did not contribute significant levels of variability to
either concentration. The remainder of variability in each model
was explained by temperature, along-canyon wind, and cross-canyon
wind, which were all significant in the models.
 Descriptors: Article Subject Terms Air quality | Atmospheric pollution | Atmospheric
pollution by diesel engines | Atmospheric pollution by motor
vehicles | Atmospheric pollution models | Automotive exhaust
emissions | Combustion products | Conservation | Diesel engines | Emissions | Gasoline | Meteorological data | Meteorology | Motor
vehicles | Particle size | Particulate matter in urban air | Pollution monitoring | Regression models | Street microclimates | Temperature | Time series analysis | Trucks | Urban areas | Urban
atmospheric pollution | Urban microclimatology | Wind variability | black carbon | canyons | schools | time series analysis | traffic | Article Geographic Terms USA, New York, New York City
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