Analysis and Trends Research Publications on Air Quality (PM 2.5) Management Strategies
Bibliographic record
Abstract
Abstract The study was conducted to analyze the trends in the development of research in the field of air quality management from 1983 to 2022. The study aims to find out the developments of scientific publications from year to year, the authors who dominate, the dominant countries, and the opportunities for innovation in research topics related to air quality strategy. Data collection was done by searching through Scopus with the keywords management OR strategy OR monitoring AND “air pollution” OR “air quality” AND “particulate matter” OR pm OR pm10 OR pm AND 2,5 with the category, article title, abstract, and keywords. The data is analyzed in terms of the number of publications per year, authors, origin of authors, and subjects with Ms. Excel. Whereas trends in publication development are analyzed using software such as biblioshiny and vosviewer. The results of the study show that in the period 1983-2022, research on air quality management strategies has undergone significant progress. The highest publication development began in 2019, with the production of over 100 annual scientific articles. Some of the main authors are Zhang Y, Wang Y, Wang X, Zhang X, Liu Y, and Wang J. The results of this study revealed gaps in spatial distribution where most of the research was conducted in China, the USA, Canada, the United Kingdom, India, and Spain, as well as some European countries. However, it could be an opportunity for researchers from lower income countries like Indonesia to do similar research. Topics that have not been much discussed and studied are topics about mapping, spatial analysis, modelling of air pollution, and dynamic systems for air quality management strategies.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.028 | 0.057 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".