Canadian air zones represent a complex mixture of urban and rural land-use impacted by diverse emissions sources. The Calgary Spatial and Temporal Exposure Modeling (CSTEM) Study was designed to provide spatial and temporal air quality information for Calgary and surrounding areas to support local air zone management strategies and air pollution health studies. CSTEM collected two-week integrated measurements of nitrogen dioxide (NO2), volatile organic compounds, particulate matter (PM10, PM2.5), black carbon (BC), and PM-components at 125 sites in summer (August 5-19, 2015) and 124 sites in winter (January 20-February 3, 2016). Seasonal trends were assessed by collecting two-week integrated data every two weeks at four temporal sites across the city from March 25, 2015-April 27, 2016. NO2 and VOCs were measured using Ogawa Passive Samplers and Organic Vapor Passive Samplers. Gravimetric PM2.5 and PM1.0 measurements were collected using Harvard Cascade Impactors with 37 mm Teflon filters. PM2.5 samples were analyzed for elemental composition using HF-nitric acid digested inductively coupled plasma mass spectrometry. BC was measured via optical scanning of gravimetric PM2.5 samples using a SootScan Model OT21 Transmissometer. Continuous BC was collected at 40 sites using microAethalometers. Analysis of collocated BC samples showed good agreement (R2>0.70) between the methods. Air pollution data were combined with land-use information to develop land-use regression models. Stepwise selection and regression tree methods were used to identify best predictors. The Getis-Ord Gi statistic and global Moran’s I were applied to display local variations of pollutants. Both ordinary least squares (OLS) regression and geographically weighted regression (GWR) techniques were applied. Summer results follow. As NO2 displayed greater local variation compared with PM, GWR and regional OLS models were only developed for NO2. Global OLS models performed poorly, predicting only 56% of the variability in NO2. Regional OLS models performed slightly better, with R2 ranging from 56 to 60%. Anova tests showed that GWR models provided a statistically significant improvement over OLS with local R2 ranging within the study area from 56% to 87% (Q25=72% and Q75=84%).Industrial zoning, infrastructure and major roads were significant predictors of NO2. Industrial zoning, PM emitting facilities, and local roads were major predictors of PM2.5. CSTEM results provide insight into best approaches for characterizing air pollution in a large, diverse air zone. Future analyses will focus on seasonal and temporal modeling; modeling BC, VOCs, and metals; and integrating data from other sources such as chemical transport models, satellite remote sensing, and continuous regulatory monitoring.

Spatial and Temporal Assessment of Air Pollution in the Calgary, Alberta Air Zone / M. Johnson, I. Couloigner, S. Bertazzon, F. Underwood, K. Van Ryswyk, R. Kulka, H. You. - ELETTRONICO. - (2016), pp. 0-0. (Intervento presentato al convegno International Society of Exposure Science (ISES) 2016 Annual Meeting tenutosi a Utrecht, Olanda nel 9–13 ottobre 2016).

Spatial and Temporal Assessment of Air Pollution in the Calgary, Alberta Air Zone.

S. Bertazzon;
2016

Abstract

Canadian air zones represent a complex mixture of urban and rural land-use impacted by diverse emissions sources. The Calgary Spatial and Temporal Exposure Modeling (CSTEM) Study was designed to provide spatial and temporal air quality information for Calgary and surrounding areas to support local air zone management strategies and air pollution health studies. CSTEM collected two-week integrated measurements of nitrogen dioxide (NO2), volatile organic compounds, particulate matter (PM10, PM2.5), black carbon (BC), and PM-components at 125 sites in summer (August 5-19, 2015) and 124 sites in winter (January 20-February 3, 2016). Seasonal trends were assessed by collecting two-week integrated data every two weeks at four temporal sites across the city from March 25, 2015-April 27, 2016. NO2 and VOCs were measured using Ogawa Passive Samplers and Organic Vapor Passive Samplers. Gravimetric PM2.5 and PM1.0 measurements were collected using Harvard Cascade Impactors with 37 mm Teflon filters. PM2.5 samples were analyzed for elemental composition using HF-nitric acid digested inductively coupled plasma mass spectrometry. BC was measured via optical scanning of gravimetric PM2.5 samples using a SootScan Model OT21 Transmissometer. Continuous BC was collected at 40 sites using microAethalometers. Analysis of collocated BC samples showed good agreement (R2>0.70) between the methods. Air pollution data were combined with land-use information to develop land-use regression models. Stepwise selection and regression tree methods were used to identify best predictors. The Getis-Ord Gi statistic and global Moran’s I were applied to display local variations of pollutants. Both ordinary least squares (OLS) regression and geographically weighted regression (GWR) techniques were applied. Summer results follow. As NO2 displayed greater local variation compared with PM, GWR and regional OLS models were only developed for NO2. Global OLS models performed poorly, predicting only 56% of the variability in NO2. Regional OLS models performed slightly better, with R2 ranging from 56 to 60%. Anova tests showed that GWR models provided a statistically significant improvement over OLS with local R2 ranging within the study area from 56% to 87% (Q25=72% and Q75=84%).Industrial zoning, infrastructure and major roads were significant predictors of NO2. Industrial zoning, PM emitting facilities, and local roads were major predictors of PM2.5. CSTEM results provide insight into best approaches for characterizing air pollution in a large, diverse air zone. Future analyses will focus on seasonal and temporal modeling; modeling BC, VOCs, and metals; and integrating data from other sources such as chemical transport models, satellite remote sensing, and continuous regulatory monitoring.
2016
International Society of Exposure Science (ISES) 2016 Annual Meeting
International Society of Exposure Science (ISES) 2016 Annual Meeting
Utrecht, Olanda
M. Johnson, I. Couloigner, S. Bertazzon, F. Underwood, K. Van Ryswyk, R. Kulka, H. You
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1145592
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