Effect of Biomass Burning, Diwali Fireworks, and Polluted Fog Events on the Oxidative Potential of Fine Ambient Particulate Matter in Delhi, India
Bibliographic record
Abstract
We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM 2.5 ) oxidative potential (OP) during the postmonsoon (PMON) and winter season in Delhi, India. The real-time hourly averaged OP (based on a dithiothreitol assay) and PM 2.5 chemical composition were measured intermittently from October 2019 to January 2020. The peak extrinsic OP (OP v: normalized by the volume of air) was observed during the winter fog (WFOG) (5.23 ± 4.6 nmol·min –1 ·m –3 ), whereas the intrinsic OP (OP m; normalized by the PM 2.5 mass) was the highest during the Diwali firework-influenced period (29.4 ± 18.48 pmol·min –1 ·μg –1 ). Source apportionment analysis using positive matrix factorization revealed that traffic + resuspended dust-related emissions (39%) and secondary sulfate + oxidized organic aerosols (38%) were driving the OP v during the PMON period, whereas BURN aerosols dominated (37%) the OP v during the WFOG period. Firework-related emissions became a significant contributor (∼32%) to the OP v during the Diwali period (4 day period from October 26 to 29), and its contribution peaked (72%) on the night of Diwali. Discerning the influence of seasonal and episodic sources on health-relevant properties of PM 2.5, such as OP, could help better understand the causal relationships between PM 2.5 and health effects in India.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".