Spatial regression modelling of particulate pollution in Calgary, Canada

Abstract

The study presents a spatial analysis of particulate pollution, which includes not only particulate matter, but also black carbon, a pollutant of growing concern for human health. We developed land use regression (LUR) models for two particulate matter size fractions, PM 2.5 and PM 10 , and for δC, an index calculated from black carbon (BC)—a component of PM 2.5 —which indicates the portion of organic versus elemental BC. LUR models were estimated over Calgary (Canada) for summer 2015 and winter 2016. As all samples exhibited significant spatial autocorrelation, spatial autoregressive lag (SARlag) and error (SARerr) models were computed. SARlag models were preferred for all pollutants in both seasons, and yielded goodness of fit aligned with or higher than values reported in the literature. LUR models yielded consistent sets of predictors, representing industrial activities, traffic, and elevation. The obtained model coefficients were then combined with local land use variables to compute fine-scale concentration predictions over the entire city. The predicted concentrations were slightly lower and less dispersed than the observed ones. Consistent with observed pollution records, prediction maps exhibited higher concentration over the road network, industrial areas, and the eastern quadrants of the city. Lastly, results of a corresponding study of PM in summer 2010 and winter 2011 were considered. While the small size of the 2010–2011 sample hampered a multi-temporal analysis, we cautiously note comparable seasonal patterns and consistent association with land use variables for both PM fine fractions over the 5-year interval.