AAAR 34th Annual Conference
October 12 - October 16, 2015
Hyatt Regency
Minneapolis, Minnesota, USA
Abstract View
Spatial Variation of PM2.5 Components with Mobile Sampling Strategy in Pittsburgh
ZHONGJU LI, Timothy Dallmann, Albert A. Presto, Carnegie Mellon University
Abstract Number: 136 Working Group: Urban Aerosols
Abstract Long-term exposure to particulate matter (PM) is the major contributor to air pollution related death in the 21st century. Evidence indicates that metals play an important role in harming health with their redox activity. A mobile sampling campaign was conducted in 2014 fall and 2015 winter in Pittsburgh to characterize spatial variations in PM2.5 mass and its components. Thirty-six sites were chosen based on three stratification variables—traffic density, proximity to point source and elevation. Filter samples were collected in three time sessions (morning, afternoon, and night) in each season. X-ray fluorescence was used to analyze concentrations of 16 elements: Na, Al, Si, S, Cl, K, Ca, Ti, Cr, Fe, Co, Ni, Cu, Zn, Se, Sb. Concentrations generally ranged from 0 to 300 ng/m3 and indicated spatial heterogeneity.
Land-use regression (LUR) models were developed for metals and other trace species. Three categories of independent variables were extracted using Arcgis-10.1: traffic factors, land-use zoning, and Toxic Release Inventory (TRI) facility data. Various regression diagnostics were performed to validate LUR models. The number of predictors in final LUR models ranged from 1 to 5, and the models had an average R2 of 0.53(0.10).
Metal source profiles were also derived using positive matrix factorization (PMF) at each of the 36 sites. We will develop LUR models based on the specific emissions sources determined by PMF. The spatial variations informed by the PMF-derived LUR models will be compared to species-specific LUR models for species commonly used as source tracers to determine the effectiveness of each approach to apportioning source-resolved exposures.