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J Expo Anal Environ Epidemiol. 2003 Sep;13(5):364-71.

Influence of traffic patterns on particulate matter and polycyclic aromatic hydrocarbon concentrations in Roxbury, Massachusetts.

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1
Department of Environmental Health, Harvard School of Public Health, Boston, MA 02215, USA. jilevy@hsph.harvard.edu

Abstract

Vehicle emissions have been associated with adverse health effects in multiple epidemiological studies, but the sources or constituents responsible have not been established. Characterization of vehicle-related exposures requires detailed information on spatial and temporal trends of various pollutants and the ability to predict exposures in unmonitored settings. To address these issues, in the summer of 2001 we measured continuously particle-bound polycyclic aromatic hydrocarbons (PAHs), ultrafine particles, and PM(2.5) at a number of sites in Roxbury, a neighborhood of Boston, Massachusetts with significant diesel and gasoline-fueled traffic. We took measurements at the side of the road and at varying distances from the road, with simultaneous collection of traffic counts and meteorological conditions. Across all nine sites, median roadside concentrations were 8 ng/m(3) of particle-bound PAHs (range: 4-57), 16,000 ultrafine particles/cm(3) (range: 11,000-53,000), and 54 microg/m(3) of PM(2.5) as measured with a DustTrak (range: 12-86). Concentrations of all pollutants were lower at greater distances from the road, upwind, and at higher wind speeds, with greater concentration gradients for PAHs and ultrafine particles. In linear mixed effects regression models accounting for temporal autocorrelation, large diesel vehicle counts were significantly associated with roadside concentrations of PAHs (P=0.02), with a moderate association with ultrafine particles and little relation with PM(2.5). Although more comprehensive information would be needed for epidemiological applications, these data demonstrate significant spatial and temporal heterogeneity for traffic-related pollutants during the summer in an urban center, with our monitoring and analytical methodology helping to inform source attribution.

PMID:
12973364
DOI:
10.1038/sj.jea.7500289
[Indexed for MEDLINE]
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