Changing Air Quality and the Ozone Weekend Effect during the COVID-19 Pandemic in Toronto, Ontario, Canada
Bibliographic record
Abstract
Air pollutants, NO, NO2, and O3, were examined from April to June 2020 and compared to a 10-year (2010–2019) climatology of these pollutants for two monitoring sites in Toronto, Ontario, Canada, coinciding with local lockdown measures during the first wave of the COVID-19 pandemic. NO and NO2 values were lower than any of the preceding 10 years at the two Toronto sites for both weekdays and weekends. Ozone concentrations did not have a corresponding decrease and in fact increased for weekdays, similar to other parts of the world. The well-documented ozone weekend effect was considerably muted during the morning rush hour throughout this pandemic period. A Fisher exact test on hourly averaged data revealed statistically significant record hourly minimums for NO and NO2, but this was not found for ozone, consistent with the aggregate ranking results. These findings are likely the result of considerably reduced vehicular traffic during this time and ozone chemistry in a NOx-saturated (VOC limited) environment. This has important implications for ozone abatement strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".