Two-decade spatiotemporal variations in ground-level ozone over Ontario, Canada
Bibliographic record
Abstract
Introduction Ground-level ozone (O3) remains a persistent air quality concern in Ontario, Canada’s most populous province. Understanding long-term trends and spatially explicit details of O3 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 O3 (MDA8 O3) 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 O3 estimation. Results Validation against in-situ measurements confirms the hypothesized high accuracy of the dataset (R2 = 0.82, RMSE = 4.99 ppb), outperforming the traditional model and two existing datasets. The dataset reveals pronounced spatiotemporal heterogeneity in MDA8 O3 concentrations, which are low in northern Ontario but high in southern Ontario, especially in southwest Ontario. Seasonally, the provincial mean MDA8 O3 peaks in spring (∼40 ppb) and dips in autumn (∼27 ppb), while spatial MDA8 O3 in summer is most heterogeneous among all seasons, with a peak in southwestern Ontario. From 2004 to 2023, the provincial mean MDA8 O3 shows no significant trend, while a significant decreasing trend (−0.1 ppb/year, p < 0.05) appears in southern Ontario, where MDA8 O3 increases in winter but decreases in summer, both significantly. The number of days exceeding the World Health Organization (WHO) O3 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 O3 in southern Ontario is impacted by both anthropogenic emissions and meteorology. Reductions in O3 precursor emissions have effectively lowered summertime O3 across southern Ontario, partially offsetting the meteorological-driven increase in O3. This MDA8 O3 dataset offers a valuable resource for further research in environmental health, air quality policy, and O3 impact on agriculture.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".