Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics.
Aerosol
Airborne pathogen
Buildings
CFD
COVID19
Droplet release
Respiratory events
Journal
Sustainable cities and society
ISSN: 2210-6715
Titre abrégé: Sustain Cities Soc
Pays: Netherlands
ID NLM: 101735304
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
20
05
2021
revised:
13
01
2022
accepted:
15
01
2022
pubmed:
25
1
2022
medline:
25
1
2022
entrez:
24
1
2022
Statut:
ppublish
Résumé
Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and to envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters limitations due to the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets' initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. There is a noticeable inconsistency around these collected data amongst many reported studies. This study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Therefore, this paper briefly reviews the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. The relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures are also reviewed. Eventually, the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis are scurtinized to pave a pathway toward future studies ensuing these identified gaps and limitations.
Identifiants
pubmed: 35070645
doi: 10.1016/j.scs.2022.103704
pii: S2210-6707(22)00037-3
pmc: PMC8767784
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
103704Informations de copyright
© 2022 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
None.
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