Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary, Canada. Issue 7 (3rd July 2019)

• Record Type: Journal Article
• Authors: Bertazzon, Stefania; Couloigner, Isabelle; Underwood, Fox E.
• Is Part Of: International journal of geographical information science. Volume 33:Issue 7(2019)
• Page Start: 1335
• Page End: 1354
• Publication Date: 2019-07-03
• Subjects: LUR land use regression -- SAR LUR spatially autoregressive -- multi-temporal LUR -- NO2 nitrogen dioxide -- spatial air pollution; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1578885
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.