Spatial and Temporal Assessment of Air Pollution in the Calgary, Alberta Air Zone.
Notice bibliographique
Résumé
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. \n \nCSTEM 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. \n \nNO2 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. \n \nAir 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. \n \nAs 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. \nCSTEM 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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».