Risk of SARS-CoV-2 in a car cabin assessed through 3D CFD simulations

Abstract

In this study, the risk of infection from SARS-CoV-2 Delta variant of passengers sharing a car cabin with an infected subject for a 30-min journey is estimated through an integrated approach combining a recently developed predictive emission-to-risk approach and a validated CFD numerical model numerically solved using the open-source OpenFOAM software. Different scenarios were investigated to evaluate the effect of the infected subject position within the car cabin, the airflow rate of the HVAC system, the HVAC ventilation mode, and the expiratory activity (breathing vs. speaking). The numerical simulations here performed reveal that the risk of infection is strongly influenced by several key parameters: As an example, under the same ventilation mode and emitting scenario, the risk of infection ranges from zero to roughly 50% as a function of the HVAC flow rate. The results obtained also demonstrate that (i) simplified zero-dimensional approaches limit proper evaluation of the risk in such confined spaces, conversely, (ii) CFD approaches are needed to investigate the complex fluid dynamics in similar indoor environments, and, thus, (iii) the risk of infection in indoor environments characterized by fixed seats can be in principle controlled by properly designing the flow patterns of the environment.