Predicting airborne coronavirus inactivation by far-UVC in populated rooms using a high-fidelity coupled radiation-CFD model

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dc.contributor.authorBuchan, Andrew G.
dc.contributor.authorYang, Liang
dc.contributor.authorAtkinson, Kirk D.
dc.date.accessioned2020-11-17T10:58:42Z
dc.date.available2020-11-17T10:58:42Z
dc.date.issued2020-11-12
dc.description.abstractThere are increased risks of contracting COVID-19 in hospitals and long-term care facilities, particularly for vulnerable groups. In these environments aerosolised coronavirus released through breathing increases the chance of spreading the disease. To reduce aerosol transmissions, the use of low dose far-UVC lighting to disinfect in-room air has been proposed. Unlike typical UVC, which has been used to kill microorganisms for decades but is carcinogenic and cataractogenic, recent evidence has shown that far-UVC is safe to use around humans. A high-fidelity, fully-coupled radiation transport and fluid dynamics model has been developed to quantify disinfection rates within a typical ventilated room. The model shows that disinfection rates are increased by a further 50-85% when using far-UVC within currently recommended exposure levels compared to the room’s ventilation alone. With these magnitudes of reduction, far-UVC lighting could be employed to mitigate SARS-CoV-2 transmission before the onset of future waves, or the start of winter when risks of infection are higher. This is particularly significant in poorly-ventilated spaces where other means of reduction are not practical, in addition social distancing can be reduced without increasing the risken_UK
dc.identifier.citationBuchan AG, Yang L, Atkinson KD. (2020) Predicting airborne coronavirus inactivation by far-UVC in populated rooms using a high-fidelity coupled radiation-CFD model. Scientific Reports, Volume 10, 2020, Article number 19659en_UK
dc.identifier.issn2045-2322
dc.identifier.urihttps://doi.org/10.1038/s41598-020-76597-y
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16003
dc.language.isoenen_UK
dc.publisherNature Research (part of Springer Nature)en_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectSARS-CoV-2en_UK
dc.subjectCOVID-19en_UK
dc.subjectfar-UVCen_UK
dc.titlePredicting airborne coronavirus inactivation by far-UVC in populated rooms using a high-fidelity coupled radiation-CFD modelen_UK
dc.typeArticleen_UK

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