Effects of purifiers on the airborne transmission of droplets inside a bus

Abstract

During an airborne infectious disease outbreak, bus passengers can be easily infected by the dispersion of exhaled droplets from an infected passenger. Therefore, measures to control the transport of droplets are necessary, such as a mask or purifier. The current research examined aerosol transport in a bus with air-conditioning. To determine the dispersion path, deposition distribution, and droplet escape time, the computational fluid dynamics were used to predict the flow field and the dispersion of droplets considering the effects of droplet size, location of the infected person, and purifier type. In addition, based on the viability and the number of virus particles in a droplet, the total number of virus particles inhaled by passengers over a 4-h journey was obtained by the superposition method. The Wells–Riley equation was then used to assess the infection risk of the passengers in the bus cabin. The results showed that droplets with a size of 1–20 μ m have essentially the same deposition characteristics, and the location of the infected passenger affects the distribution of droplets' transport and the effectiveness of a purifier in removing droplets. A purifier can effectively remove droplets from passengers' coughs and reduce the infection risk of passengers. The performance of the smaller purifiers is not as stable as that of the larger purifiers, and the performance is influenced by the airflow structure where the infected passenger is located.

COVID-19 transmission and control in land public transport: A literature review

“…4 ). Of all surfaces in the vehicle cabin, the droplets deposit most on human body surfaces, especially those of the person adjacent to the infector [36 , 40 , 72] . For the short social distance, high-velocity droplets sprayed by infectors can also directly settle on nearby passengers’ mucous membranes (such as eyes, nose, and mouth) or be inhaled, causing drop spray transmission and aerosol inhalation transmission ( Fig.…”

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

“…In addition, the link between COVID-19 transmission and public transportation has been found to be mixed ( Gartland et al, 2022 ). While using public transportation, measures such as the wearing of masks, physical distancing, air conditioning, and filtering on public transit may contain the spread of COVID-19 effectively (Kriegel, 2022; Miller et al, 2022 ; Muathesis, 2022; Yang et al, 2022 ). Quarantine measures, such as suspension of operations or restrictions to prevent the spread of infectious diseases, may cause unexpectedly high socioeconomic costs, such as increases in mental health conditions and the deepening of socioeconomic inequality ( Fink et al, 2022 ; Koch and Park, 2022 ; Thunström et al, 2020 ).…”

“…They also argued that the routes and main factors of public transit in diffusing COVID-19 were still unclear during the early experiences of the pandemic. Some studies have found that such measures such as mask wearing, physical distancing, air conditioning, and filtering on public transit have a significant impact on reducing exposure to COVID-19 transmission ( Kriegel et al, 2021 ; Matheis et al, 2022 ; Miller et al, 2022 ; Yang et al, 2022 ). Musselwhite et al (2020) argues that restrictions on the operation of overcrowded public transit due to the higher possibility of individual transmission of COVID-19 needs to be well thought because the exposure in households can be higher.…”

Predicting aerosol transmission in airplanes: Benefits of a joint approach using experiments and simulation

“…A lot of work has been done both from the perspective of general modeling of aerosol spread, [6][7][8][9][10] and for making predictions in specific scenarios, ranging from hospital rooms, [11][12][13] classrooms 14 and restaurants, 15,16 to aircrafts, 17 cars 18 and buses. 19,20 We refer to References 21-23 for comprehensive reviews of different computational models for aerosol transmission. It is important to note that a majority of computational models for predicting aerosol transmission are not validated against experiments.…”

“…To make faster predictions, various numerical simulation models have also been proposed. A lot of work has been done both from the perspective of general modeling of aerosol spread, 6–10 and for making predictions in specific scenarios, ranging from hospital rooms, 11–13 classrooms 14 and restaurants, 15,16 to aircrafts, 17 cars 18 and buses 19,20 . We refer to References 21–23 for comprehensive reviews of different computational models for aerosol transmission.…”