Sources of Wintertime Atmospheric Organic Pollutants in a Large Canadian City: Insights from Particle and Gas Phase Measurements
Bibliographic record
Abstract
Although atmospheric organic pollutants have been extensively studied to elucidate summertime urban photochemical air pollution, uncertainties remain concerning the quality of wintertime air in large northern North American cities. Here, we used online mass spectrometric measurements of volatile organic compounds (VOCs) and organic aerosol (OA), combined with positive matrix factorization (PMF), to identify sources of organic pollutants in downtown Toronto, Canada during February–March 2023. In some cases, comparable PMF factors were identified for both VOCs and OA, such as from traffic, cooking, and background oxygenated sources. However, VOC PMF yielded additional information, such as a factor associated with human-related emissions of VOCs. Additionally, VOC PMF yields two traffic factors: one likely related to gasoline and one to diesel use. Despite cold and relatively dark conditions, the OA and VOC oxygenated factors both grow in intensity during the daytime, indicative of photochemical activity, whereas the traffic and cooking factors were enhanced in the morning and late evening due to the timing of vehicle use, cooking, and boundary layer effects. This study illustrates the benefits that arise from the parallel source–receptor analyses of organic gases and aerosol particles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".