Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Abstract

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.