Application of mobile sampling to investigate spatial variation in fine particle composition. (October 2016)

• Record Type: Journal Article
• Authors: Li, Hugh Z.; Dallmann, Timothy R.; Gu, Peishi; Presto, Albert A.
• Is Part Of: Atmospheric environment. Volume 142(2016)
• Page Start: 71
• Page End: 82
• Publication Date: 2016-10
• Subjects: Air pollution -- Spatial variation -- Mobile sampling -- Particle composition -- Land use regression -- Traffic; Air -- Pollution -- Periodicals; Air -- Pollution -- Meteorological aspects -- Periodicals; 551.51
• DOI: 10.1016/j.atmosenv.2016.07.042
• Languages: English
• ISSNs: 1352-2310

Abstract

Abstract: Long-term exposure to particulate matter (PM) is a major contributor to air pollution related deaths. Evidence indicates that metals play an important role in harming human health due to their redox potential. We conducted a mobile sampling campaign in 2013 summer and winter in Pittsburgh, PA to characterize spatial variation in PM2.5 mass and composition. Thirty-six sites were chosen based on three stratification variables: traffic density, proximity to point sources, and elevation. We collected filters in three time sessions (morning, afternoon, and overnight) in each season. X-ray fluorescence (XRF) was used to analyze concentrations of 26 elements: Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Cd, Sb, and Pb. Trace elements had a broad range of concentrations from 0 to 300 ng/m 3 . Comparison of data from mobile sampling filters with stationary monitors suggested that the mobile sampling strategy did not lead to a biased dataset. We developed Land Use Regression (LUR) models to describe spatial variation of PM2.5, Si, S, Cl, K, Ca, Ti, Cr, Fe, Cu, and Zn. Using ArcGIS-10.3 (ESRI, Redlands, CA), we extracted different independent variables related to traffic influence, land-use type, and facility emissions based on the National Emission Inventory (NEI). To validate LUR models, we used regression diagnostics such as leave-one-out cross validation (LOOCV), mean studentized prediction residual (MSPR), and root mean square of studentized residuals (RMS). The number of predictors in final LUR models ranged from 1 to 6. Models had an average R 2 of 0.57 (SD = 0.16). Traffic related variables explained the most variability with an average R 2 contribution of 0.20 (SD = 0.20). Overall, these results demonstrated significant intra-urban spatial variability of fine particle composition. Highlights: Fine particle composition displayed significant intra-urban spatial variability. Traffic LUR variables explained the most spatial variability. Zn is a weaker traffic indicator than EC or particle-bound PAH.