Dispersion of sneeze droplets in a meat facility indoor environment – Without partitions

Kumar, Sunil; Klassen, Mark; Klassen, David; Hardin, Robert G.; King, Maria D.

doi:10.1016/j.envres.2023.116603

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…Yang et al [30] used computational fluid dynamics (CFD) simulation to predict the cough jet dispersion in an airline cabin. To date, studies have evaluated SARS-CoV-2 spread via heating, ventilation, and air conditioning (HVAC) systems in various practical settings, such as underground car parks [31], laboratories [32], hospital wards [33][34][35][36], restaurants [37], restrooms [38,39], elevators [40], public transportation [41][42][43][44], classrooms [45], cafeterias [46], airplanes [47], dental clinics [48], factories [49], etc. These research efforts provide essential references for understanding droplet diffusion in different environments and formulating strategies to prevent cross-infections.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Droplet Dispersion and Passenger Infection Risk Assessment in a Subway Carriage

Wu,

Yu,

et al. 2024

Applied Sciences

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Droplet transmission is a critical pathway for the spread of respiratory infectious viruses. A thorough understanding of the mechanisms of droplet dispersion within subway carriages is crucial to curb the widespread transmission of the virus. This study utilizes computational fluid dynamics (CFD) to establish a full-scale numerical model of a subway carriage. The numerical model and droplet evaporation behavior are validated using experimental data and literature. The impact of primary parameters such as the initial droplet size, release velocity, release position, relative humidity, and passenger density on the droplet diffusion and probability of infection for passengers is investigated. The results indicate that large droplets (100 μm) are deposited on the carriage floor before complete evaporation, while tiny droplets (10 μm) evaporate rapidly, leading to a longer suspension time in the air within the carriage. The infected passenger’s position influences the ventilation system’s efficiency in removing the droplets; removal takes significantly longer when the infected passenger is closer to the carriage end. Additionally, a low relative humidity (35%) and high passenger density (4 p/m2) result in more droplets being trapped by passengers’ bodies. The infection probability for passengers depends on the initial size and quantity of droplets trapped by their bodies. Maintaining higher relative humidity levels and limiting the passenger numbers within the subway carriage can reduce the number of droplets captured by passengers’ bodies, thus helping to reduce the infection probability of fellow passengers.

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