Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – a machine learning approach

Mesgarpour, M., Abad, J. M. N., Alizadeh, R., Wongwises, S., Doranehgard, M. H., Jowkar, S. and Karimi, N. (2022) Predicting the effects of environmental parameters on the spatio-temporal distribution of the droplets carrying coronavirus in public transport – a machine learning approach. Chemical Engineering Journal, 430(Part 2), 132761. (doi: 10.1016/j.cej.2021.132761) (PMID:34642569) (PMCID:PMC8495004)

Abstract

Human-generated droplets constitute the main route for the transmission of coronavirus. However, the details of such transmission in enclosed environments are yet to be understood. This is because geometrical and environmental parameters can immensely complicate the problem and turn the conventional analyses inefficient. As a remedy, this work develops a predictive tool based on computational fluid dynamics and machine learning to examine the distribution of sneezing droplets in realistic configurations. The time-dependent effects of environmental parameters, including temperature, humidity and ventilation rate, upon the droplets with diameters between 1 and 250μm are investigated inside a bus. It is shown that humidity can profoundly affect the droplets distribution, such that 10% increase in relative humidity results in 30% increase in the droplets density at the farthest point from a sneezing passenger. Further, ventilation process is found to feature dual effects on the droplets distribution. Simple increases in the ventilation rate may accelerate the droplets transmission. However, carefully tailored injection of fresh air enhances deposition of droplets on the surfaces and thus reduces their concentration in the bus. Finally, the analysis identifies an optimal range of temperature, humidity and ventilation rate to maintain human comfort while minimising the transmission of droplets.

• Item Type: Articles
• Keywords: COVID-19, droplets distribution, droplet suspension, machine learning, computational fluid dynamics, prediction models.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Karimi, Dr Nader

Creator Roles

Karimi, N.: Conceptualization, Supervision, Project administration, Writing – review and editing

• Authors: Mesgarpour, M., Abad, J. M. N., Alizadeh, R., Wongwises, S., Doranehgard, M. H., Jowkar, S., and Karimi, N.
• College/School: College of Science and Engineering > School of Engineering
• Journal Name: Chemical Engineering Journal
• Publisher: Elsevier
• ISSN: 1385-8947
• ISSN (Online): 1873-3212
• Published Online: 07 October 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Chemical Engineering Journal 430(Part 2): 132761
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
311655		Risk EvaLuatIon fAst iNtelligent Tool (RELIANT) for COVID19	Andrea Cammarano	Engineering and Physical Sciences Research Council (EPSRC)	EP/V036777/1	ENG - Aerospace Sciences