PM2.5 and PM10 Airborne Concentrations Resulting from Fireworks During Festivities: A Systematic Review
Bibliographic record
Abstract
The burning of fireworks damages air quality by causing elevated concentrations of particulate matter (PM2.5 and PM10) in short periods of time.In this context, the research aimed to compare airborne concentrations of PM2.5 and PM10 from fireworks with the National Ambient Air Quality Standards (NAAQS) and the World Health Organization (WHO) guideline.The applied methodology involved the use of the PRISMA 2020 statement.The literature review was conducted on digital databases such as Scopus, ScienceDirect, Taylor & Francis, Wiley, and Ebsco.Annual growth in scientific production was calculated using a digital tool (Calcuvio), and data analysis was performed using Microsoft Office Excel and VOSviewer.The annual growth in production (1999 to 2022) was 18.74%.The highest scientific production per year was concentrated in 2019 and 2020, with China being the leading country.The festivities where sound pressure levels were predominantly measured were during the Spring Festival and Diwali.The most frequently mentioned keywords were "fireworks" and "PM2.5".In conclusion, the percentage of studies that exceeded the NAAQS for PM2.5 and PM10 was 2% and 15%, respectively, while for the WHO guideline, it was only 1% for PM10.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".