Computational Fluid and Particle Dynamics Analyses for Prediction of Airborne Infection/Spread Risks in Highway Buses: A Parametric Study

Abstract

Highway buses are used in a wide range of commuting services and in the tourist industry. The demand for highway bus transportation has dramatically increased in the recent post-pandemic world, and airborne transmission risks may increase alongside the demand for highway buses, owing to a higher passenger density in bus cabins. We developed a numerical prediction method for the spatial distribution of airborne transmission risks inside bus cabins. For a computational fluid dynamics analyses, targeting two types of bus cabins, sophisticated geometries of bus cabins with realistic heating, ventilation, and air-conditioning were reproduced. The passengers in bus cabins were reproduced using computer-simulated persons. Airflow, heat, and moisture transfer analysis were conducted based on computational fluid dynamics, to predict the microclimate around passengers and the interaction between the cabin climate and passengers. Finally, droplet dispersion analysis using the Eulerian–Lagrangian method and an investigation of the spatial distribution of infection/spread risks, assuming SARS-CoV-2 infection, were performed. Through parametric analyses of passive and individual countermeasures to reduce airborne infection risks, the effectiveness of countermeasures for airborne infection was discussed. Partition installation as a passive countermeasure had an impact on the human microclimate, which decreased infection risks. The individual countermeasure, mask-wearing, almost completely prevented airborne infection.