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2520278 
Journal Article 
Exploration of the composition and sources of urban fine particulate matter associated with same-day cardiovascular health effects in Dearborn, Michigan 
Morishita, M; Bard, RL; Kaciroti, N; Fitzner, CA; Dvonch, T; Harkema, JR; Rajagopalan, S; Brook, RD 
2015 
Yes 
Journal of Exposure Science and Environmental Epidemiology
ISSN: 1559-0631
EISSN: 1559-064X 
25 
145-152 
English 
is supplemented by 3859198 - Supplementary material
The objective was to explore associations of chemical components and source factors of ambient fine particulate matter (aerodynamic diameter ≤2.5 μm; PM2.5) with cardiovascular (CV) changes following same-day exposure to ambient PM2.5. Twenty-five healthy adults living in rural Michigan were exposed to ambient air in an urban/industrial community for 4 to 5 h daily for five consecutive days. CV health outcomes were measured 1-2 h post exposure. Contributing emission sources were identified via positive matrix factorization. We examined associations between PM2.5 mass, composition and source factors, and same-day changes in CV outcomes using mixed-model analyses. PM2.5 mass (10.8±6.8 μg/m(3)), even at low ambient levels, was significantly associated with increased heart rate (HR). Trace elements as well as secondary aerosol, diesel/urban dust and iron/steel manufacturing factors potentially explained the HR changes. However, trace element analysis demonstrated additional associations with other CV responses including changes in blood pressure (BP), arterial compliance, autonomic balance and trends toward reductions in endothelial function. Two factors were related to BP changes (diesel/urban dust, motor vehicle) and trends toward impaired endothelial function (diesel/urban dust). This study indicates composition of PM2.5 and its sources may contribute to CV health effects independently of PM2.5 mass.Journal of Exposure Science and Environmental Epidemiology advance online publication, 28 May 2014; doi:10.1038/jes.2014.35. 
PM2.5; Dearborn; cardiovascular effects; receptor modeling 
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