Two-decade spatiotemporal variations in ground-level ozone over Ontario, Canada
Bibliographic record
Abstract
Introduction Ground-level ozone (O 3 ) remains a persistent air quality concern in Ontario, Canada’s most populous province. Understanding long-term trends and spatially explicit details of O 3 is important for supporting air quality management in Ontario. Method We construct a high-resolution (daily, 10 km) dataset of maximum daily 8-hour average O 3 (MDA8 O 3 ) over Ontario from 2004 to 2023, through a two-step machine learning model. The model has incorporated our hypothesis that accounting for transboundary influences can enhance the accuracy of O 3 estimation. Results Validation against in-situ measurements confirms the hypothesized high accuracy of the dataset (R 2 = 0.82, RMSE = 4.99 ppb), outperforming the traditional model and two existing datasets. The dataset reveals pronounced spatiotemporal heterogeneity in MDA8 O 3 concentrations, which are low in northern Ontario but high in southern Ontario, especially in southwest Ontario. Seasonally, the provincial mean MDA8 O 3 peaks in spring (∼40 ppb) and dips in autumn (∼27 ppb), while spatial MDA8 O 3 in summer is most heterogeneous among all seasons, with a peak in southwestern Ontario. From 2004 to 2023, the provincial mean MDA8 O 3 shows no significant trend, while a significant decreasing trend (−0.1 ppb/year, p < 0.05) appears in southern Ontario, where MDA8 O 3 increases in winter but decreases in summer, both significantly. The number of days exceeding the World Health Organization (WHO) O 3 guideline range from 10 to 80 days in southern Ontario, with a decline of 1–4 days (up to 15%) per year over 2004–2023. Discussion The analysis suggests that O 3 in southern Ontario is impacted by both anthropogenic emissions and meteorology. Reductions in O 3 precursor emissions have effectively lowered summertime O 3 across southern Ontario, partially offsetting the meteorological-driven increase in O 3 . This MDA8 O 3 dataset offers a valuable resource for further research in environmental health, air quality policy, and O 3 impact on agriculture.
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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 | 0.000 | 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 | 0.000 |
| 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 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".