Air quality modelling, simulation, and computational methods: a review
Bibliographic record
Abstract
The objective of this paper is to provide a comprehensive theoretical review with regard to history, existing approaches, recent developments, major research, associated computational methods, and applications of air quality models. A wide range of topics is covered, focusing on sources of air pollution, primary and secondary pollutants, atmospheric chemistry, atmospheric chemical transport models, computer programs for dispersion modelling, online and offline air quality modelling, data assimilation, parallel computing, applications of geographic information system in air quality modelling, air quality index, as well as the use of satellite and remote sensing data in air quality modelling. Each of these elements is comprehensively discussed, covered, and reviewed with respect to various literature and methods related to air quality modelling and applications. Several major commercial and noncommercial dispersion packages are extensively reviewed and detailed advantages and limitations of their applications are highlighted. The paper includes several comparison summaries among various models used in air quality study. Furthermore, the paper provides useful web sites, where readers can obtain further information regarding air quality models and (or) software. Lastly, current generation of air quality models and future directions are also discussed. This paper may serve as a compendium for scientists who work in air quality modelling field. Some topics are generally treated; therefore, the paper may also be used as a reference source by many scientists working with air quality modelling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".