Numerical Simulation of Contaminant Transport and Infection Probability in Public Transport Vehicles

In this work, a numerical set-up is built to perform transient numerical simulations of airflow quality, contaminant transport, and risk of infection within enclosed spaces. In particular, the case of an urban bus is proposed by studying the probability of infection from SARS CoV-2 during typical urban travel. Different air supply units are analyzed: an air-conditioning device with partial outside air recirculation and an air purification system with continuous indoor air purification and different air diffuser configurations. The infection probability is evaluated using an original methodology based on the Wells–Riley model. The generation and transport of airborne infections are considered by solving a quanta transport equation that uses empirical values for quanta exhalation and inhalation rates. The flow field is solved once using URANS models. Next, different target positions for infectious and target susceptible people are simulated to build a general infection probability matrix, allowing the quantification of the risk of contagion by running a set of affordable transient simulations. Air age and PM_2.5 concentration are also employed to evaluate general air quality. The numerical model, experimentally validated in past works, is verified here using a mesh convergence analysis. Hence, the different air supply units and configurations are analyzed with the current methodology to quantify the risk of infection, showing a 13% risk reduction when introducing the air purification unit and a 23% reduction when using the same unit but with a more efficient grid configuration.