Impacts of respiratory activities on infection risk of COVID-19 in a passenger elevator

Abstract

Contaminant transport and flow distribution are very important during an elevator ride, as the reduced social distancing may increase the infection rate of airborne diseases such as COVID-19. This investigation used a computational fluid dynamics (CFD) model based on the RNG k–ϵ turbulence model to predict airflow and particle transport in an elevator-lobby area with moving passengers. The CFD results showed a complex airflow pattern due to the downwash air supply from the ceiling and the upward thermal plumes generated by passengers. This investigation studied different respiratory activities of the index patient, i.e., breathing, coughing with and without a mask, and speaking. The results quantitatively compare the risk of infection among different respiratory activities. During an elevator ride, the infection risk was generally low because of the short duration. However, if the index patient talked in an elevator, the infection risk would be relatively high, as two passengers in the closest proximity to distance would be infected.