Global changes in the hazardous atmospheric NO2 during the COVID-19 lockdown and post-lockdown periods
Bibliographic record
Abstract
• Examination of NO2 in global hotspots, 3000 cities and 86 major urban centres during LD. • A considerable decline in NO2 is found in all major hotspots and urban centres during LD. • After ease of LD, NO2 is reversed back to the previous year level in most areas. • The reversal of pollution in urban areas demands a revision of current vehicular norms. There are several environmental policies such as vehicular emission norms and industrial regulations, yet many countries grapple with poor air quality. In this context, the COVID-19 lockdown (LD, March–April 2020) provided a unique opportunity to examine the anthropogenic and natural sources of air pollution. Here, we observe a notable decrease in NO₂ pollution in its global hotspots such as East China (EC), Indo-Gangetic Plain (IGP), Western Europe (WE), South Africa (SA), the United States of America (USA) and Southeast Asia (SEA), about 5–30% during LD. A similar decrease in NO₂ is also observed in the major urban centres of the world (e.g. New York, Delhi, Beijing, London, Mexico, Toronto, Canberra, Johannesburg and Paris) in the same period. This reduction is owing to the temporary pause of human activities such as industrial operations and transport, which are the major sources of NO₂ there. However, after the ease of LD (i.e. post-lockdown period or PostLD), high NO₂ pollution is observed in most regions and cities (about 10–30%), which is more pronounced in the cities of EC (e.g. Beijing), IGP (e.g. Delhi), WE (e.g. London) and the USA (e.g. New York, Pittsburgh). In addition, some other global cities (e.g. Mumbai, Bangalore, Wuhan, Montreal, Bonn and Jakarta) also show a comparable rise in NO₂ during PostLD, about 5–25%. These results indicate that the decline in NO₂ pollution was primarily due to the strict vehicular regulations during LD. Therefore, this assessment suggests revisiting the existing vehicular policies and enforcing additional measures to reduce air pollution for a healthy and sustainable planet.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".