Identifying air pollution characteristics, source apportionment methods, and air quality modeling approaches in transport hub settings: State-of-play and future directions
Bibliographic record
Abstract
This review synthesises the current state-of-the-art in air quality (AQ) research relating to current monitoring and modeling methods focused on transport hub (TH) settings. Air pollution characteristics from monitoring studies revealed that higher concentrations of PM 2 . 5 (12.4-147.8 μg/m 3 ) and SO 2 (54.1-78.3 μg/m 3 ) dominated AQ issues in ports. Train terminals were dominated by NO 2 (52.2-472.5 μg/m 3 ), with VOCs (123-973 ppb) and UFPs (5×10 3 to 4.8×10 6 particles/cm 3 ) considerably higher at airports. Bivariate polar plots, data filtration techniques, and regression models were considered relatively simple, resource-efficient, and effective source apportionment methods to assess AQ (SO 2 , NO 2 and UFP) from sources in and around THs. Speciated receptor modelling is more expensive but is appropriate for complex environments to evaluate multi-pollutant (PM and VOCs) conditions. Gaussian models demonstrated better agreement than Eulerian and Lagrangian models at airports, with Eulerian models slightly outperforming Gaussian models in port settings. Additionally, Eulerian was the most effective methods to model secondary pollutants and over long distances. Limited AQ research focused on small-scale semi-enclosed THs, such as bus and train terminals, with an additional knowledge gap of indoor AQ in port and airport buildings. Improved characterisation of pollutants like VOCs, BC and PAHs in THs would benefit climate and health impact assessments, with the integration of AI offering a means to enhance monitoring and management AQ at THs in the future.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".