Bibliographic record
Abstract
Abstract. This study investigates long-term trends of criteria air pollutants, including NO2, CO, SO2, O3 and PM2.5, and (NO2+O3) measured in ten Canadian cities during the last two to three decades and associated driving forces in terms of emission reductions, perturbations from varying weather conditions and large-scale wildfires, and changes in O3 sources and sinks. Two machine-learning methods, including random forest algorithm and boosted regression trees, were used to extract deweathered mixing ratios (or mass concentrations) of the pollutants. The Mann-Kendall analysis of the deweathered and original annual average concentrations of the pollutants showed that, on the time scale of 20 years or longer, the perturbation from varying weather conditions exerted a very minor influence on the decadal trends of original annual averages (within ±2 %) in ~70 % of the cases, and a moderate influence up to 16 % of the original trends in the other 30 % cases. NO2, CO and SO2 showed decreasing trends in the last two to three decades in all the cities except CO in Montreal. O3 showed increasing trends in all the cities, except Halifax, mainly due to weakened titration reaction between O3 and NO. (NO2+O3), however, showed decreasing trends in all the cities, except Victoria, because the increase in O3 is much less than the decrease in NO2. In three of the five eastern Canadian cities, emission reductions dominated the decreasing trends in PM2.5, but no significant trends in PM2.5 were observed in the other two cites. In five western Canadian cities, increasing or no significant trends in PM2.5 were observed, likely due to unpredictable large-scale wildfires overwhelming or balancing the impacts of emission reductions on PM2.5. In addition, despite improving air quality during the last two decades in most cities, air quality health index of above 10 (representing very high-risk condition) still occasionally occurred after 2010 in western Canadian cities because of the increased large-scale wildfires.
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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 | 0.005 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.689 | 0.474 |
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".