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Source Apportionment of Ambient Fine Particulate Matter in Dearborn, Michigan, using Hourly Resolved PM Chemical Composition Data
Citation:
Pancras, P., M. Landis, G. Norris, R. Vedantham, AND T. Dvonch. Source Apportionment of Ambient Fine Particulate Matter in Dearborn, Michigan, using Hourly Resolved PM Chemical Composition Data. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 448(5):2-13, (2013).
Impact/Purpose:
The National Exposure Research Laboratory??s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA??s mission to protect human health and the environment. HEASD??s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA??s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
High time-resolution aerosol sampling was conducted for one month during July–August 2007 in Dearborn, MI, a non-attainment area for fine particulate matter (PM2.5) National Ambient Air Quality Standards (NAAQS). Measurements of more than 30 PM2.5 species were made using a suite of semi-continuous sampling and monitoring instruments. Dynamic variations in the sub-hourly concentrations of source ‘marker’ elements were observed when discrete plumes from local sources impacted the sampling site. Hourly averaged PM2.5 composition data for 639 samples were used to identify and apportion PM2.5 emission sources using the multivariate receptor modeling techniques EPA Positive Matrix Factorization (PMF) v4.2 and EPA Unmix v6.0. Source contribution estimates from PMF and Unmix were then evaluated using the Sustained Wind Instance Method (SWIM), which identified plausible source origins. Ten sources were identified by both PMF and Unmix: (1) secondary sulfate, (2) secondary nitrate characterized by a significant diurnal trend, (3) iron and steel production, (4) a potassium-rich factor attributable to iron/steel slag waste processing, (5) a cadmium-rich factor attributable to incineration, (6) an oil refinery characterized by La/Ce>1 specific to south wind, (7) oil combustion, (8) coal combustion, (9) motor vehicles, and (10) road dust enriched with organic carbon. While both models apportioned secondary sulfate, oil refinery, and oil combustion PM2.5masses closely, the mobile and industrial source apportionments differed. Analyses were also carried out to help infer time-of-day variations in the contributions of local sources.
URLs/Downloads:
DEARBORN_SA_V10 FINAL FINAL.PDF (PDF, NA pp, 2575.212 KB, about PDF)Science of the Total Environment
