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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.
Acknowledgments
We thank the Canadian Institutes for Health Research, CIHR Institute for Population and Public Health, and the O’Brien Institute for Public Health for funding the project “Walk smart, breathe smart”. We acknowledge our colleagues of Health Canada for their contribution to the monitoring campaigns, along with student volunteers, field crews, members of the OIPH Geography of Health study group, and all the individuals and organizations that hosted air monitors. We also acknowledge the University of Calgary’s Spatial and Numeric Data Service, the City of Calgary, Rocky View County, Statistics Canada, Environment Canada, and the Calgary Region Airshed Zone for data used in this study.
Finally, we sincerely thank the anonymous referees and Professor O’Sullivan for their truly insightful comments and constructive criticism.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. However, windrose predictors did not improve any of the models; hence, they are not presented in this paper.
2. Although in some cases the performance of GWR was equivalent to the SARlag specification.
Additional information
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Notes on contributors
Stefania Bertazzon
Stefania Bertazzon is Professor of Geography at the University of Calgary, Canada. A quantitative health geographer, she has developed models of air pollution, ocean pollution, and analyses of the association of health, socio-economic status, and the built environment. She received her Masters degree from the University of Venice, Italy, and her Ph.D. from the University of Calgary, Canada.
Isabelle Couloigner
Isabelle Couloigner is a Research Associate in Spatial Analytics/Modelling both at the Department of Geography and the Department of Ecosystem and Public Health, University of Calgary. She received her engineering degree from Ecole Louis de Broglie, France and completed her PhD at Ecole des Mines de Paris, Sophia Antipolis, France.
Fox E. Underwood
Fox Underwood is a health geographer working in the area of digestive disease epidemiology in the Department of Medicine at the University of Calgary. She has lead data collection for two substantial air pollution monitoring campaigns in Calgary and recently assisted in the launching of a third. She completed her MSc in the Department of Geography at the University of Calgary, Canada.