Spatial regression modelling of particulate pollution in Calgary, Canada

• Published in: GeoJournal Vol. 87; no. 3; pp. 2141 - 2157
• Main Authors: Bertazzon, Stefania, Couloigner, Isabelle, Mirzaei, Mojgan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2022; Springer Nature B.V
• Subjects: Air pollution; Autocorrelation; Biomass; Black carbon; Canada; Carbon; Coal-fired power plants; Coefficients; Environmental Management; Estimates; Forest & brush fires; Fossil fuels; Geography; Goodness of fit; Human Geography; human health; Industrial areas; Land use; Organic chemicals; Outdoor air quality; Particulate emissions; Particulate matter; Particulate pollution; particulates; Pollutants; Pollution; prediction; Predictions; Regression analysis; roads; Roads & highways; Seasonal variations; Social Sciences; Spatial analysis; Summer; Suspended particulate matter; traffic; Urban areas; Variables; Winter; Canada; Calgary Alberta Canada; Spatially autoregressive lag and error models; Particulate matter (PM; Air pollution fine-scale prediction maps; and PM; Spatial land use regression (LUR); ); Black carbon (BC, delta-C, organic vs. elemental); Air pollution and human health; Particulate matter (PM2.5 and PM10)

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,...