The Effects of the COVID-19 Pandemic on Air Pollution: A Systematic Review
Bibliographic record
Abstract
Introduction: The efforts to reduce COVID-19 transmission could significantly affect pollution and weather in most parts of the world due to the reduction of industrial activities and road transport. Hence, this systematic review aimed to assess the effects of the COVID-19 pandemic on air pollution. Methods: The keywords were searched in the online databases of Scopus, PubMed, and Cochrane. We applied the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Results: Generally ambient air pollutants (PM2.5, PM10, NO2, NOX, NO, SO2, CO, black carbon, BTX (benzene, toluene, and Xylene), NH3, HCHO, PAHs, CH4, Solid Waste, UFPs (Ultrafine particle, ≥115.5 nm)) decreased significantly during lockdown period due to restricted human activities. Noteworthy, controversial findings have been reported concerning O3 levels; most studies, especially in East Asia, reported enhancement in the levels of O3, which was mainly attributed to meteorology factors. Although the COVID-19 pandemic caused a global health crisis, the improvement in worldwide air quality status was significant. Conclusion: Generally, pollutants generated by industrial activities were observed to be significantly reduced during lockdowns.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".