Assessment of suspended particulate matter and heavy metal analysis during Diwali festival at Raipur, Chhattisgarh, India
Bibliographic record
Abstract
Diwali is an important festival in India, and a lot of firecracker bursting takes place in every part of the country during festive occasions. The short-term effect of firecrackers on ambient air quality was assessed by monitoring suspended particulate matter (SPM) and heavy metal analysis (iron (Fe), zinc (Zn), lead (Pb), manganese (Mn) and nickel (Ni)) during the Diwali festival at the urban-industrial city of Raipur, India, from 3 to 11 November 2018. The daily average SPM concentrations were found to be about two times higher on 7 November (Diwali; 425.64 µg/m3) and 8 November (next day of Diwali; 417.92 µg/m3) compared with that on 3 November (pre-Diwali day; 247.56 µg/m3). Heavy metal analysis (iron, zinc, lead, manganese and nickel) was carried out using atomic absorption spectroscopy, and the same concentration trend in the order of iron > zinc > lead > manganese > nickel was found during day- and night-time. A significant increment in SPM and heavy metal concentrations was observed post-Diwali. Back-trajectory analysis revealed that the trajectories at surface level (50 and 1000 m above ground level) originated from local sources, showing local anthropogenic activities such as burning of firecrackers and industrial activities as a major pollution source. The wind rose diagram shows a higher SPM concentration when the wind was north-easterly. The present study reveals that pollution levels were considerably increased during Diwali and firecracker bursting played a major role by contributing to air pollution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".