Temporal variations and trend of ground-level ozone based on long-term measurements in Windsor, Canada
Bibliographic record
Abstract
This study investigates temporal variations and long-term (1996–2015) trends of ground-level O 3 (ozone) and its precursors, NO x (nitrogen oxides), and volatile organic compounds in Windsor, Ontario, Canada. During the 20-year study period, NO x , non-methane hydrocarbon concentrations, and ozone formation potential decreased significantly by 58 %, 61 %, and 73 %, respectively, while O 3 concentrations increased by 33 % (20.3 ppb in 1996 vs. 27 ppb in 2015). Our analysis revealed that the increased annual O 3 concentrations in Windsor were due to (1) decreased O 3 titration (by 50 % between 1996 and 2015) owing to declining nitric oxide concentrations, which is suggested by a slightly decreasing trend of annual mean total O 3 concentrations after the titration effect is removed, (2) reduced local photochemical production of O 3 because of dwindling precursor emissions, and (3) an increased background O 3 level that has a greater impact on the low-to-median concentrations. The net effect of those factors is decreasing peak O 3 levels during the smog season from May to September but an overall increasing trend of annual means. These results indicate that the emission control measures are effective in reducing peak ozone concentrations. However, challenges in lowering annual O 3 levels call for long-term collaborative efforts in the region and around the globe.
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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) | 0.000 | 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 | 0.000 | 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 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".