Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary, Canada

Bertazzon, Stefania; Couloigner, Isabelle; Underwood, Fox E.

doi:10.1080/13658816.2019.1578885

Cited by 3 publications

(13 citation statements)

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“…The combination of these three analyses suggests that AEV exhibits a spatial trend, not only visually discernible but also quantifiable and statistically significant: it consists of a gradient, increasing from the west and southwest to the east and northeast portions of the metropolitan area. This pattern is consistent with pollution patterns previously noted in the literature and is further confirmed by correlation analysis [33,39,43,44]. Further, spatial autocorrelation in AEV increases over the years, suggesting that the gradient is becoming more pronounced, likely indicating a growing disparity across the city.…”

Section: Spatial Analysissupporting

confidence: 90%

“…LUR models compute regression coefficients for a set of predictors, for a given pollutant and for a given sample. Spatiotemporal LUR models of NO 2 are discussed in previously published work [33,45]. Despite differences across monitoring campaigns, and an overall slight downward trend in pollution levels, these studies show that pollutants exhibit a consistent spatial pattern over the 5-year interval between campaigns.…”

Section: Study Region Data and Methodsmentioning

confidence: 83%

“…Previous studies conducted by our group yielded land use regression (LUR) estimates of air quality at fine scale (see Figure 3). Air quality monitoring campaigns were conducted during the summer and winter of 2010-2011 and of 2015-2016, yielding summer and winter models, respectively, for each campaign [33,[42][43][44]. For both the 2010-2011 and 2015-2016 campaigns, LUR models were computed for nitrogen dioxide (NO 2 ) and particulate matter (PM 2.5 and PM 10 ), among other pollutants.…”

Section: Study Region Data and Methodsmentioning

confidence: 99%

“…Spatially detailed air pollution data are rarely available and monitoring networks are sparsely installed, mostly due to the high costs associated with air monitoring. This study relies on fine-scale, i.e., dissemination block level [32], air quality estimates, obtained through land use regression (LUR) models on data from two monitoring campaigns conducted by our group in collaboration with Health Canada over the metropolitan area of Calgary, with deployment of up to 100 monitoring stations [33]. As owners of the 2010-2011 and 2015-2016 LUR models, we can affirm with certainty that no other study conducted has analyzed the association between asthma and air pollution over space in the Calgary metropolitan area.…”

Section: Introductionmentioning

confidence: 99%

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A Preliminary Spatial Analysis of the Association of Asthma and Traffic-Related Air Pollution in the Metropolitan Area of Calgary, Canada

et al. 2020

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We performed a preliminary spatial analysis to assess the association of asthma emergency visits (AEV) with ambient air pollutants (NO2, PM2.5, PM10, Black Carbon, and VOCs) over Calgary, Canada. Descriptive analyses identify spatial patterns across the city. The spatial patterns of AEV and air pollutants were analyzed by descriptive and spatial statistics (Moran’s I and Getis G). The association between AEV, air pollutants, and socioeconomic status was assessed by correlation and regression. A spatial gradient was identified, characterized by increasing AEV incidence from west to east; this pattern has become increasingly pronounced over time. The association of asthma and air pollution is consistent with the location of industrial areas and major traffic corridors. AEV exhibited more significant associations with BTEX and PM10, particularly during the summer. Over time, AEV decreased overall, though with varying temporal patterns throughout Calgary. AEV exhibited significant and seasonal associations with ambient air pollutants. Socioeconomic status is a confounding factor in AEV in Calgary, and the AEV disparities across the city are becoming more pronounced over time. Within the current pandemic, this spatial analysis is relevant and timely, bearing potential to identify hotspots linked to ambient air pollution and populations at greater risk.

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Section: Spatial Analysissupporting

confidence: 90%

Section: Study Region Data and Methodsmentioning

confidence: 83%

Section: Study Region Data and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Preliminary Spatial Analysis of the Association of Asthma and Traffic-Related Air Pollution in the Metropolitan Area of Calgary, Canada

et al. 2020

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“…33 Further, the LUR model used in Calgary was shown to remain stable over a 5-year interval. 34 We used the NO 2 estimates from the air pollution study reported by Bertazzon and colleagues for the study period. 4 Land use regression estimates were assigned to each patient based on the 6-digit postal codes of their residential locations, which we defined as their neighbourhood.…”

Section: Data Sourcesmentioning

confidence: 99%

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study

Liu

Bertazzon

Villeneuve

et al. 2020

cmajo

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S tudies have consistently shown that short-term elevations in air pollution concentrations increase the risk of myocardial infarction (MI). 1,2 Improving our understanding of the effects of short-term exposure to air pollution on MI may inform government policy and facilitate prevention by warning populations at risk. In Calgary, the major contributors to air pollution are transportation for nitrogen dioxide (NO 2) and carbon monoxide (CO); construction for particulate matter less than 10 µm in diameter (PM 10) and particulate matter less than 2.5 µm in diameter (PM 2.5); and industry for sulfur dioxide (SO 2). 3 The relatively higher air pollution regions, from a long-term exposure perspective, are mainly distributed along major traffic corridors and close to industrial areas. 4,5 Air pollution exposure studies that consider an average of air pollution levels overlook the inherent spatial nature of air pollution. 6,7 Historically, temporal analyses exploring the association between short-term exposure to air pollution and health outcomes have assumed that pollutants are spatially homogeneous. 8-11 However, research has shown that spatial distribution patterns differ by pollutant. 12 For example, it is widely recognized that ozone (O 3) is relatively spatially homogenous because of consistent concentration levels and temporal fluctuations, whereas NO 2 is spatially heterogeneous

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Spatial regression modelling of particulate pollution in Calgary, Canada

2021

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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 dC, 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 multitemporal analysis, we cautiously note comparable seasonal patterns and consistent association with land use variables for both PM fine fractions over the 5-year interval.Keywords Spatial land use regression (LUR) Á Spatially autoregressive lag and error models Á Air pollution fine-scale prediction maps Á Air pollution and human health Á Particulate matter (PM 2.5 and PM 10 ) Á Black carbon (BC, delta-C, organic vs. elemental) Introduction and rationaleParticulate matter (PM) is a mixture of small particles: acids, organic chemicals, metals, and dust particles (EPA 2016). Coarse particles (PM 10 ) are 2.5-10 micrometers in diameter; fine particles (PM 2.5 ) are less than 2.5 micrometers in diameter. Black carbon (BC) is a component of PM 2.5 , formed by the incomplete combustion of biomass and fossil fuels,

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Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary, Canada

Cited by 3 publications

References 48 publications

A Preliminary Spatial Analysis of the Association of Asthma and Traffic-Related Air Pollution in the Metropolitan Area of Calgary, Canada

A Preliminary Spatial Analysis of the Association of Asthma and Traffic-Related Air Pollution in the Metropolitan Area of Calgary, Canada

Temporal and spatial effect of air pollution on hospital admissions for myocardial infarction: a case-crossover study

Spatial regression modelling of particulate pollution in Calgary, Canada

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