Review on physicochemical properties of pollutants released from fireworks: environmental and health effects and prevention
Bibliographic record
Abstract
The pollutants released from fireworks may seriously deteriorate air quality and adversely impact human health. To aid in obtaining comprehensive observations and in establishing effective legislation aimed at controlling the short-term effects of fireworks, we systematically reviewed the findings of previous studies of the impact of fireworks. These studies, primarily located in Asia (>70% studies), Europe, and North America considered particle concentrations, size distribution, morphology, noise, and chemical composition (including water-soluble ions, elements, carbonaceous material, organic matter, and trace gases), along with the associated human health effects during a fireworks display. Forty-one percent of the studies suggested that the concentrations of firework particles were reported to be 1–5 times higher than the respective background values, and the mean ratios PM10/TSP, PM2.5/PM10, and PM1.0/PM2.5 were 0.64, 0.72, and 0.65, respectively. During festivals, the concentrations of SO 4 2− and K + were the highest of the water-soluble ions with the highest concentrations of K and S for major elements and CO and SO 2 for gaseous pollutants. The health effects of particles and gaseous pollutants, including metals, emitted from fireworks need further epidemiological study to aid in the prevention of health problems and for the treatment of patients. Fireworks industries should use technical innovation to reduce pollutant emissions. Emissions inventories of fireworks displays should be compiled and used in Eulerian models to forecast the spatiotemporal distribution of pollutants and to further assistant the government in establishing appropriate restriction levels and legislations that balance environmental protection with the festive spirit.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".