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Record W2598616611 · doi:10.5539/ep.v6n1p19

Airborne Particulate Matter from Sparkling Fireworks

2017· article· en· W2598616611 on OpenAlexvenueno aff
Haruki Shimazu

Bibliographic record

VenueEnvironment and Pollution · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersKanazawa UniversityKindai University
KeywordsFireworksParticulatesEnvironmental scienceEnvironmental chemistryChemistry

Abstract

fetched live from OpenAlex

The present study examines the emission levels of particulate matters (PM) from sparkling fireworks and to know the emission characteristics of PM. Particulate matter <2.5 microns (PM2.5) and suspended particulate matter (SPM) were determined while burning six brands of sparkling fireworks. The average PM concentrations before burning were levels of 10 μg/m3, but the average concentrations after burning were 741 μg/m3 for PM2.5 and 810 μg/m3 for SPM. The mean ratio of the concentrations of PM2.5 and SPM after burning in all of the sparkling fireworks was 0.890. The emissions per firework ranged from 6.5 mg to 151 mg for PM2.5, and from 7.1 mg to 160 mg for SPM. The means of the emissions per combustible amount of the firework ranged from 0.017 to 0.066 mg/mg for PM2.5, and from 0.018 to 0.071 mg/mg for SPM. The influences of the burning time, burning rate and combustible amount of the fireworks on the PM emissions were investigated. As a result, PM2.5 and SPM emissions tend to increase with the burning rates. This suggests that the burning rate of firework have an influence on the PM emissions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.264
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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