Urban Air Quality: Assessing Traffiffic and Building Architecture Impacts using Portable Measuring Devices in Toronto, Ontario
Bibliographic record
Abstract
Vehicle emissions are one of the largest local contributors to poor urban air quality. High emissions are often associated with traffic congestion, and pollution may also become trapped between tall buildings creating a street canyon effect. The spatial variability of traffic-related air pollutants in microenvironments should be considered in evaluating changes in urban planning. This study focuses on assessing the air quality and commuter exposure in Toronto, Ontario, Canada, specifically focusing on the effect of the King Street Pilot Project on local urban air quality by reducing traffic. Increased vehicular density is expected to contribute to higher urban pollution levels and tall buildings may trap these contaminants. Field measurements were made within the King Street Pilot area during weekday rush hours to capture the best representation of peak activity and pollutant levels when there were similar average wind speeds and directions for the sampling dates. A suite of portable devices was carried along predesigned and timed routes through traffic dense areas to measure vehicle-related air pollutants including black carbon (BC), ultrafine particles (UFP, particles smaller than 0.1 μm), and particulate matter (PM2.5, particles smaller than 2.5 μm). Data was normalized, corrected and analyzed using centralized pollutant while considering meteorological site measurements located about 1.5 km away from the study area. Results indicated higher BC and UFP levels during peak commuting times between 8 am to 10 am and relatively increased pollution levels within the area of tall buildings versus the area with shorter buildings. Strong spatial variations of BC and UFP were found, while PM2.5 levels remained relatively constant in the downtown area. Elevated levels of BC and UFP were observed around nearby construction sites. This study contributes to establishing a baseline to evaluate the King Street Pilot Project’s air quality impact as well as proposing potential methods of detailed data collection within microenvironments to observe the air quality of urban centres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".