Identifying air pollution characteristics, source apportionment methods, and air quality modeling approaches in transport hub settings: State-of-play and future directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 it