Statistical Quantification of COVID-19 Lockdown Effect on Air Quality from Ground-Based Measurements in Ontario, Canada
Bibliographic record
Abstract
Preliminary analysis of satellite measurements from around the world showed drops in nitrogen dioxide (NO 2 ) with lockdowns due to the COVID-19 pandemic. A number of studies have found these drops to be correlated with local decreases in transportation and/or industry. None of these studies, however, has rigorously quantified the statistical significance of these drops relative to natural meteorological variability and other factors that influence pollutant levels during similar time periods in previous years. Here, we develop a novel statistical testing framework that accounts for seasonal variability, transboundary influences, and new factors such as COVID-19 restrictions in explaining trends in several pollutant levels at 16 ground-based measurement sites in Southern Ontario, Canada. We find statistically significant and temporary drops in NO 2 (11 out 16 sites) and CO (all 4 sites) in April-June 2020, with pollutant levels 20% lower than in the previous three years. Much fewer sites (2-3 out of 16) experienced statistically significant drops in O 3 and PM2.5. The statistical testing framework developed here is the first of its kind applied to air quality data, and highlights the need for rigorous assessment of statistical significance, should analyses of pollutant level changes post COVID-19 lockdowns be used to inform policy decisions in Ontario, Canada. See Methods section in the manuscript.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".