Evaluation of intervention measures in reducing the driver's exposure to respiratory particles in a taxi with infected passengers

Abstract

In the fifth wave of the COVID-19 epidemic in Hong Kong in early 2022, the large number of infected persons caused a shortage of ambulances and transportation vehicles operated by the government. To solve the problem, taxi drivers were recruited to transport infected persons to hospitals in their taxis. However, many of the drivers were infected after they began to participate in the plan. To tackle this issue, the present study numerically evaluated the effectiveness of several intervention measures in reducing the infection risk for taxi drivers. First, experiments were conducted inside a car to validate the large-eddy simulation (LES)-Lagrangian model for simulation of particle transport in a car. The validated model was then applied to calculate the particle dispersion and deposition in a Hong Kong taxi with intervention measures that included opening windows, installing partitions, and using a far-UVC lamp. The results show that opening the windows can significantly reduce the driver's total exposure by 97.4 %. Installing partitions and using a far-UVC lamp can further reduce the infection risk of driver by 55.9 % and 32.1 %, respectively. The results of this study can be used to support the implementation of effective intervention measures to protect taxi drivers from infection.

Keywords: Bioaerosol dispersion and deposition; Car cabin environment; Computational fluid dynamics (CFD); Far-UVC disinfection; Intervention measures.