Numerical investigation of airborne transmission of respiratory infections on the subway platform

Underground subway platforms are among the world's busiest public transportation systems, but the air-borne transmission mechanism of respiratory infections on these platforms has been rarely studied. Here, computational fluid dynamics (CFD) modeling is used to investigate the airflow patterns and infection risks in an island platform under two common ventilation modes: Mode 1-both sides have air inlets and outlets; Mode 2-air inlets are present at the two sides and outlets are present in the middle. Under the investigated scenario, airflow structure is characterized by the ventilation jet and human ther-mal plumes. Their interaction with the infector's breathing jet imposes the front passenger under the highest exposure risk by short-range airborne route, with intake fractions up to 2.57% (oral breathing) or 0.63% (nasal breathing) under Mode 1; oral breathing of the infector may impose higher risks for the front passenger compared with nasal breathing. Pathogen are efficiently diluted as they travel further, in particular to adjacent crowds. The maximum and median value of intake fractions of passengers in adjacent crowds are respectively 0.093% and 0.016% (oral breathing), and 0.073% and 0.014% (nasal breathing) under Mode 1. Compared with Mode 1, the 2nd mode minimizes the interaction of ventilation jet and breathing jet, where the maximum intake fraction is only 0.34%, and the median value in the same crowd and other crowds are reduced by 23-63%. Combining published quanta generation rate data of COVID-19 and influenza infectors, the predicted maximum and median infection risks for passengers in the same crowds are respectively 1.46%-40.23% and 0.038%-1.67% during the 3-10 min waiting per-iod, which are more sensitive to ventilation rate and exposure time compared with return air. This study can provide practical guidance for the prevention of respiratory infections in subway platforms.(c) 2022 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Automated summary

This study used computational fluid dynamics modeling to investigate the airflow patterns and infection risks in an underground subway platform. The results showed that the interaction between ventilation jet and breathing jet posed the highest exposure risk to front passengers. However, pathogens were efficiently diluted as they traveled further. Different ventilation modes had an impact on infection risks, with the second mode minimizing the risks. Based on data of COVID-19 and influenza infectors, the predicted infection risks for passengers in the same crowd were sensitive to ventilation rate and exposure time.