Meteorological Influences on Seasonal Variation of Fine Particulate Matter in Cities over Southern Ontario, Canada
Bibliographic record
Abstract
This study examines meteorological impacts on seasonal variation of fine particulate matter (PM2.5) in southern Ontario, Canada. After analyzing PM2.5data at 12 cities in the region in 2006, we found that PM2.5concentrations were 30–40% higher in summer (7–15 μg/m3) than in winter (4–11 μg/m3). High PM2.5episodes occurred more frequently in warmer seasons. Analyses of surface meteorology, weather maps, and airflow trajectories suggest that these PM2.5episodes were often related to synoptic transport of pollutants from highly polluted areas in the United States. The southerly or southwesterly winds associated with midlatitude cyclones play an important role in such transport. A typical weather pattern favoring the transport is suggested. When it was hot, humid, and stagnant with southerly or southwesterly winds, the likelihood of high PM2.5occurrences was high. The Greater Golden Horseshoe and Southwestern Ontario regions had higher PM2.5(6–12 μg/m3annually) than the northern region (4–6 μg/m3), reflecting combined effects of meteorology, regional transport, and local emissions. In the future, PM2.5transport from the United States will likely increase in abundance because of possible prolonged accumulation at the pollution sources as the frequency of the midlatitude cyclones may reduce under climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".