Identifying Air Pollution Characteristics, Source Apportionment Methods, and Air Quality Modelling Approaches in Transport Hub Settings: State-of-Play and Future Directions

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 PM2.5 (12.4–147.8 μg/m3) and SO2 (54.1–78.3 μg/m3) dominated AQ issues in ports. Train terminals were impacted by NO2 (52.2–472.5 μg/m3), with VOCs (123–973 ppb) and UFPs (5 × 103 to 4.8 × 106 particles/cm3) 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 (SO2, NO2 and UFP) from sources in and around THs. Speciated receptor modelling was more expensive, but is suitable 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 would benefit climate and health impact assessments at THs, with the integration of AI offering a means to enhance monitoring and management AQ at Tin these settings in the future.