Unveiling air quality shifts during Diwali in Chennai using geospatial insights for pollution assessment
Bibliographic record
Abstract
Air pollution poses a significant challenge to global health, contributing to one in nine fatalities worldwide and causing approximately seven million premature deaths annually, according to the World Health Organization (WHO). In South Asia, air pollution ranks as the sixth leading cause of mortality, exacerbating issues such as poor visibility, respiratory illnesses, and elevated death rates. This study examines the impact of fireworks emissions on air quality in Chennai during the Diwali festival, focusing on particulate matter (PM 2.5 , PM 10 ) and trace gases, including nitrogen oxides (NO 2 ), ammonia (NH 3 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ). Measurements recorded before and after Diwali festival on October 26, 2022, revealed mean 24-h PM 2.5 and PM 10 concentrations of 93 µg/m 3 and 94 µg/m 3 , respectively—exceeding WHO’s emission limits. During the festival, the peak concentrations of NO 2 , NH 3 , SO 2 , CO, and O 3 were 15.83 µg/m 3 , 5.83 µg/m 3 , 25.50 µg/m 3 , 35.67 µg/m 3 , and 13 µg/m 3 , respectively, surpassing limits established by India’s Central Pollution Control Board (CPCB). Comparisons with pre-Diwali data indicated substantial increases in pollutant levels attributed to fireworks. Geographic Information System (GIS) mapping and trace gas correlation analyses highlighted the spatial and temporal distribution of pollutants. Findings suggest that short-term exposure to these elevated pollutant levels can significantly impact vulnerable populations, particularly infants, children, and individuals with respiratory conditions. To mitigate these adverse effects, the study recommends implementing restrictions on fireworks near sensitive areas such as hospitals and schools, alongside public awareness campaigns to promote sustainable practices during festive celebrations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".