Air Quality Decrement After Lockdown in Major Cities of Rajasthan, India
Bibliographic record
Abstract
The lockdown restrictions during the COVID-19 pandemic provided a 'path' of reinstatement of the air quality globally. Despite several financial challenges, air quality improvement positively impacted the environment due to lockdown in the worst pandemic situations. The present study assessed the air pollution scenario in the post lockdown phase in the seven major metropolises of Rajasthan, namely, Jodhpur, Alwar, Jaipur, Kota, Pali, Ajmer, and Udaipur, in the recent pandemic year 2020. The air pollution scenario is determined with the help of the Air Quality Index (AQI) and the concentration level of PM2.5, PM10, NO2, and SO2. This study reveals that most cities of Rajasthan are violating India's national ambient air quality standards (NAAQS). It is found that Jodhpur is on rank first in terms of pollution levels, followed by Alwar, Jaipur, Pali, and Udaipur. The pollution level was higher before the lockdown period then reduced to a certain level due to restricted activities in lockdown. The pollution level is not rapidly increased after lockdown due to rainfall from the southwest monsoon. Winter season consists of higher concentration levels of pollutant and higher than before lockdown period. The study shows the significant impact of lockdown in reducing air pollution levels in cities. But imposing lockdown in a city or country is not a permanent solution to curb air pollution. So, regulating agencies and stakeholders should implement better control and reduction technologies for Indian cities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".