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Intracity Variability of Particulate Matter Exposure Is Driven by Carbonaceous Sources and Correlated with Land-Use Variables

Cite this: Environ. Sci. Technol. 2018, 52, 20, 11545–11554
Publication Date (Web):September 24, 2018
https://doi.org/10.1021/acs.est.8b03833
Copyright © 2018 American Chemical Society
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    Abstract

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    Localized primary emissions of carbonaceous aerosol are the major drivers of intracity variability of submicron particulate matter (PM1) concentrations. We investigated spatial variations in PM1 composition with mobile sampling in Pittsburgh, Pennsylvania, United States and performed source-apportionment analysis to attribute primary organic aerosol (OA) to traffic (HOA) and cooking OA (COA). In high-source-impact locations, the PM1 concentration is, on average, 2 μg m–3 (40%) higher than urban background locations. Traffic emissions are the largest source contributing to population-weighted exposures to primary PM. Vehicle-miles traveled (VMT) can be used to reliably predict the concentration of HOA and localized black carbon (BC) in air pollutant spatial models. Restaurant count is a useful but imperfect predictor for COA concentration, likely due to highly variable emissions from individual restaurants. Near-road cooking emissions can be falsely attributed to traffic sources in the absence of PM source apportionment. In Pittsburgh, 28% and 9% of the total population are exposed to >1 μg m–3 of traffic- and cooking-related primary emissions, with some populations impacted by both sources. The source mix in many U.S. cities is similar; thus, we expect similar PM spatial patterns and increased exposure in high-source areas in other cities.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b03833.

    • Additional details, figures, and tables outlining land-use covariates and the spatial-joining processes, discussion of PMF solution, background correction for BC measurement, seasonal PM1 characteristic variations, carbonaceous components, the cumulative distribution of spatially joined average concentrations, residential population vs commuter-adjusted population, and the precision of AMS factor analysis (PDF)

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