To date, the vast majority of simulation efforts to predict exposure to droplet transmission consider computational fluid dynamics (CFD) where the turbulent air flow is solved using Reynolds-averaged Navier–Stokes (RANS) coupled with Lagrangian particle tracking. While it is difficult to directly measure, recent studies 9 indicate that the shedding rate λ is in the range of 1 < λ < 50 s −1. 8 In addition, key factors such as the location of the virus within the respiratory tract and the quantity of virus influence the contagiousness of airborne droplets. 7 The analysis of a superspreading event at a choral rehearsal in the state of Washington in the USA estimates the rate to be around 970 quanta/h. The process depends on the individual’s breathing rate, which varies from person to person, and for an individual depends on the activity level, such as resting, walking, speaking, singing, shouting, coughing, and sneezing.
6 The virus shedding rate is a fundamental quantity that defines the rate at which the virus becomes airborne, yet it is difficult to quantify. The droplets vary in size, from sub-micron to greater than 50 µm. The shedding of virus-laden particles is a complicated biological process by which mucus lining the lungs contains the virus, and as air passes through the respiratory tract, small droplets are formed and pass through the mouth and into the surrounding air. Finally, it was found that well fitted surgical masks, when worn by both infected and susceptible passengers, can nearly eliminate the transmission of the disease.
#Layauto virus windows#
The opening of doors and windows was found to reduce the concentration by approximately one half, albeit its benefit does not uniformly impact all passengers on the bus due to the recirculation of airflow caused by entrainment through windows. The flow that carries these aerosols is predominantly controlled by the ventilation system, which acts to uniformly distribute the aerosol concentration throughout the bus while simultaneously diluting it with fresh air. A risk metric was adopted based on the number of particles exposed to susceptible passengers. Computational fluid dynamics was employed to measure the airflow within the bus and evaluate risk. High-resolution instrumentation was used to measure size distribution and aerosol response time on a campus bus of the University of Michigan under these different conditions. Specific attention is paid to the transport of submicron- and micron-sized particles relevant to typical respiratory droplets. The effects of the ventilation and air-conditioning systems, opening windows and doors, and wearing masks are analyzed. This study presents a combined experimental and numerical analysis to identify transmission mechanisms on an urban bus and assess strategies to reduce risk. Several recent outbreaks of SARS-CoV-2 indicate the high risk of transmission among passengers on public buses if special precautions are not taken.
Airborne respiratory diseases such as COVID-19 pose significant challenges to public transportation.
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