Airborne transmission of COVID-19 and mitigation using box fan air cleaners in a poorly ventilated classroom

Ruichen He, Wanjiao Liu, John Elson, Rainer Vogt, Clay Maranville, Jiarong Hong

Research output: Contribution to journalArticlepeer-review

Abstract

Many indoor places, including aged classrooms and offices, prisons, homeless shelters, etc., are poorly ventilated but resource-limited to afford expensive ventilation upgrades or commercial air purification systems, raising concerns on the safety of opening activities in these places in the era of the COVID-19 pandemic. To address this challenge, using computational fluid dynamics, we conducted a systematic investigation of airborne transmission in a classroom equipped with a single horizontal unit ventilator (HUV) and evaluate the performance of a low-cost box fan air cleaner for risk mitigation. Our study shows that placing box fan air cleaners in the classroom results in a substantial reduction of airborne transmission risk across the entire space. The air cleaner can achieve optimal performance when placed near the asymptomatic patient. However, without knowing the location of the patient, the performance of the cleaner is optimal near the HUV with the air flowing downwards. In addition, we find that it is more efficient in reducing aerosol concentration and spread in the classroom by adding air cleaners in comparison with raising the flow rate of HUV alone. The number and placement of air cleaners need to be adjusted to maintain their efficacy for larger classrooms and to account for the thermal gradient associated with a human thermal plume and hot ventilation air during cold seasons. Overall, our study shows that box fan air cleaners can serve as an effective low-cost alternative for mitigating airborne transmission risks in poorly ventilated spaces.

Original languageEnglish (US)
Article number057107
JournalPhysics of Fluids
Volume33
Issue number5
DOIs
StatePublished - May 1 2021

Bibliographical note

Funding Information:
We acknowledge the support from Ford Motor Company for this research. We would like to thank Dr. Siyao Shao and Mr. Rafael Grazzini Placucci for conducting the flow visualization experiment. In addition, we also would like to acknowledge the support from Minnesota Supercomputing Institute (MSI) that provides computational resources.

Publisher Copyright:
© 2021 Author(s).

PubMed: MeSH publication types

  • Journal Article

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