Characterization and source apportionment of airborne particulate elements in the Athabasca oil sands region

Abstract The oil sands industries in Alberta, Canada are potential sources of particulate-bound elements in the region. This study explored the ambient concentrations and size distributions, and conducted source apportionment of 48 particulate elements, based on samples collected in 2016–2017 at four air monitoring sites in the Athabasca oil sands region: Fort McKay (AMS1), Buffalo Viewpoint (AMS4), Wapasu Creek (AMS17), and Stoney Mountain (AMS18). Element concentrations in fine and coarse particulate matter (PM2.5 and PM2.5–10 respectively) at the four sites were generally lower than their typical concentrations at other urban and industrial sites in North America. Among all elements, S was the most abundant in PM2.5 with mean concentrations ranging from 189 ng/m3 (AMS18) to 284 ng/m3 (AMS1). Of the trace, toxic elements in PM2.5, Zn was the most abundant with mean concentrations ranging from 3.43 ng/m3 (AMS18) to 5.37 ng/m3 (AMS4). Positive Matrix Factorization (PMF) modeling of the element concentrations in PM2.5 was used for source apportionment for Zone1 (including AMS 1, 4, and 17, situated closer to industrial activities) and for Zone2 (including AMS18, a background site). The sources of elements for Zone1, included crustal dust, bitumen processing, haul road dust, and biomass burning that explained ~33%, ~43%, ~15%, and ~9% of the total resolved elemental mass, respectively. The sources of elements for Zone2, included Pb-rich source, biomass burning, fugitive oil sands, crustal dust, and bitumen processing explaining ~8%, ~7%, ~3%, ~22%, and ~60% of the total resolved elemental mass, respectively. Elemental mass concentrations of the bitumen processing source factor at Zone2 was two-thirds of that in Zone1. Overall, mass proportions of the bitumen processing source factor at all four sites were significant, suggesting that the oil sands industries played a key role in ambient element concentration levels in the region.