Spatial and Temporal Assessment of Air Pollution in the Calgary, Alberta Air Zone.
Bibliographic record
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. \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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".