Analysis on Air Pollutants in COVID-19 Lockdown Using Satellite Imagery: A Study on Pakistan
Bibliographic record
Abstract
In recent years, due to rapid urbanization and industrialization, there is an increasing trend of air pollution that has brought the most alarming situation for the health of humans in some developing countries. Among various air pollutants, the most dangerous contaminants are CO (Carbon Monoxide), NO2 (Nitrogen Dioxide) as well as O3 (Ozone). This paper conducted a crucial analysis by utilizing the GEE cloud-based platform with Sentinel-5P TROPOMI satellite imagery of multi-temporal range to analyze the changes of NO2, CO, and O3 at a surface level during the nationwide lockdown in Pakistan due to COVID-19. As the whole country "shuts down", the sudden suspension of industrial activities and sparse vehicles on roads greatly reduced the air pollution. Our study found a notable reduction in NO2 (28.88%), CO (15.81%), and O3 (8.41%). The improvement in the air quality helps people who are suffering from respiratory and related diseases. Our study is helpful for the environmental department to design effective policies and take measures to improve and maintain the air quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".