Impacts of Regional Transport and Meteorology on Ground-Level Ozone in Windsor, Canada
Bibliographic record
Abstract
This study investigated impacts of regional transport and meteorology on ground-level ozone (O3) in the smog season (April–September) during 1996–2015 in Windsor, Ontario, Canada. Data from five upwind stations in the US, which are within 310 km (i.e., Allen Park and Lansing in Michigan, Erie, National Trail School, and Delaware in Ohio), were included to assess the regional characteristics of O3. The five US stations showed high degrees of similarity with O3 concentrations in Windsor, with overall strong correlations (r = 0.567–0.876 for hourly O3 and r = 0.587–0.92 for 8 h max O3 concentrations) and a low degree of divergence, indicating that O3 pollution in the study area shares regional characteristics. Meteorological conditions played important roles in O3 levels in Windsor. High O3 concentrations were associated with southerly and southwesterly air mass from which polluted and hot air mass was transported and that enhanced local photochemical O3 production. In contrast, northerly flows brought in clean, cool, and dry air mass, and led to low O3 concentrations. Strong correlations were found between numbers of days with 8 h max O3 concentrations greater than 70 ppb and numbers of days with daily max temperature greater than 30 °C, as well as between daily max temperatures and 8 h max O3 concentrations. Nearly half (45%) of the high O3 days (≥90th percentile) occurred in dry tropical weather during 1996–2015, and the 90th percentile 8 h max O3 was associated with dry tropical weather. Occurrences of both southerly flow hours and dry tropical weather type in the smog season increased during the study period. If there were more hot and dry days in the next few decades due to climate change, the effect of emission control on reducing peak O3 values would be diminished. Therefore, continued regional and international efforts are essential to control precursors’ emissions and to mitigate O3 pollution in Windsor.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".