Large eddy simulation of sneeze plumes and particles in a poorly ventilated outdoor air condition: A case study of the University of Houston main campus

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Abstract

Since the outbreak of the COVID-19 pandemic, many previous studies using computational fluid dynamics (CFD) have focused on the dynamics of air masses, which are believed to be the carriers of respiratory diseases, in enclosed indoor environments. Although outdoor air may seem to provide smaller exposure risks, it may not necessarily offer adequate ventilation that varies with different micro-climate settings. To comprehensively assess the fluid dynamics in outdoor environments and the efficiency of outdoor ventilation, we simulated the outdoor transmission of a sneeze plume in “hot spots” or areas in which the air is not quickly ventilated. We began by simulating the airflow over buildings at the University of Houston using an OpenFOAM computational fluid dynamics solver that utilized the 2019 seasonal atmospheric velocity profile from an on-site station. Next, we calculated the length of time an existing fluid is replaced by new fresh air in the domain by defining a new variable and selecting the hot spots. Finally, we conducted a large- eddy simulation of a sneeze in outdoor conditions and then simulated a sneeze plume and particles in a hot spot. The results show that fresh incoming air takes as long as 1000 s to ventilate the hot spot area in some specific regions on campus. We also found that even the slightest upward wind causes a sneeze plume to dissipate almost instantaneously at lower elevations. However, downward wind provides a stable condition for the plume, and forward wind can carry a plume even beyond six feet, the recommended social distance for preventing infection. Additionally, the simulation of sneeze droplets shows that the majority of the particles adhered to the ground or body immediately, and airborne par- ticles can be transported more than six feet, even in a minimal amount of ambient air.

Type
Publication
Science of the Total Environment, Volume 891, 164694
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Supplementary notes can be accessed here.

Hadi Zanganeh Kia
Hadi Zanganeh Kia
Graduate student of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Computational Fluid Dynamics and Numerical Modeling.

Prof. Yunsoo Choi
Prof. Yunsoo Choi
Professor of Atmospheric Chemistry, Air-Quality Modeling, AI (Deep Learning/Machine Learning), Satellite Remote Sensing

My research interests include Atmospheric Chemistry, Air-Quality Modeling and AI (Deep Learning/ Machine Learning).

Delaney Nelson
Delaney Nelson
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Climate Sciences, Air-Quality, Remote Sensing and AI (Deep Learning/ Machine Learning).

Jincheol Park
Jincheol Park
Ph.D. candidate of Atmospheric Sciences at Dept. of Earth & Atmospheric Sciences

My research interests include Atmospheric Sciences, Air-Quality, Sensitivity Analysis and Numerical Modeling.