A Systematic Review of Fireworks Noise and Its Exceedance of WHO Outdoor Limits: Global Trends and Implications
Bibliographic record
Abstract
Firework noise, generated in short bursts, poses health risks.The objective of the research was to compare the sound pressure levels generated by fireworks with the World Health Organization's (WHO) noise guidelines.The methodology employed was the 2020 PRISMA statement.The bibliographic review was conducted using digital databases such as Scopus, ScienceDirect, Taylor & Francis, Wiley, and EBSCO.To determine the annual growth of scientific production, a digital tool was utilized, and data analysis was performed with Microsoft Office Excel and VOS viewer.The annual growth of scientific production between 1975 and 2022 was 6.47%.The geographical distribution of studies by year and country was concentrated in 2013 and in India, with 3 and 13 publications, respectively.The festival where the most sound pressure levels were measured was Diwali, with 8 studies.The author with the highest number of citations was Overall K., with 161 citations, and the keyword with the highest number of occurrences was "fireworks" (18 instances).It is concluded that 100% (19) of the studies exceeded the WHO's desirable upper limit value for outdoor noise.This finding is concerning because it directly affects people's health.Consequently, governments should implement strategies to minimize the negative impacts generated by fireworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".