Examining Particulate Matter Emissions from Vehicular Traffic in an Urban Environment
Bibliographic record
Abstract
This research investigates the complexities of particulate matter (PM) emissions in urban environments, with a focus on non-exhaust sources like brake and tire wear, primarily caused by vehicular traffic in an urban area. By examining the variance in PM size, specifically particles sizes between 50 nm and 1000 nm, the study assesses the impact of traffic volume and patterns on PM emissions in Toronto, Ontario. The approach used is based on vertical fluxes measured with eddy-covariance, counting vehicles and estimation of the footprint. In this study variations in vehicle traffic were linked to fluctuations in particle concentrations and turbulent fluxes. Higher vehicle rates in weekday evenings did not correspond with increased particle numbers, whereas weekday mornings experienced higher concentrations, possibly due to overnight pollutant accumulation and the rising atmospheric boundary layer. Larger particles were more likely to originate from background sources than the road itself, particularly in areas affected by stop-and-go traffic. Over a specific week in March, however, road emissions significantly contributed to particle concentrations, a deviation from the norm. Weekends presented consistent particle deposition, suggesting that roads act more as sinks than sources, especially during periods with fewer vehicles. The complexities of urban particulate dynamics were highlighted, indicating that larger particles are prone to settle on the road surface, and changes in traffic patterns can transform typical emission sources into particle sinks. This study lays the groundwork for future research, emphasizing the need for detailed traffic data to better understand emission sources and implications for urban air quality and public health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".