Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors
Open accessAbstract
We provide research findings on the physics of aerosol and droplet dispersion relevant to the hypothesized aerosol transmission of SARS-CoV-2 during the current pandemic. We utilize physics-based modeling at different levels of complexity, along with previous literature on coronaviruses, to investigate the possibility of airborne transmission. The previous literature, our 0D-3D simulations by various physics-based models, and theoretical calculations, indicate that the typical size range of speech and cough originated droplets (d⩽20μm) allows lingering in the air for O(1h) so that they could be inhaled. Consistent with the previous literature, numerical evidence on the rapid drying process of even large droplets, up to sizes O(100μm), into droplet nuclei/aerosols is provided. Based on the literature and the public media sources, we provide evidence that the individuals, who have been tested positive on COVID-19, could have been exposed to aerosols/droplet nuclei by inhaling them in significant numbers e.g. O(100). By 3D scale-resolving computational fluid dynamics (CFD) simulations, we give various examples on the transport and dilution of aerosols (d⩽20μm) over distances O(10m) in generic environments. We study susceptible and infected individuals in generic public places by Monte-Carlo modelling. The developed model takes into account the locally varying aerosol concentration levels which the susceptible accumulate via inhalation. The introduced concept, ’exposure time’ to virus containing aerosols is proposed to complement the traditional ’safety distance’ thinking. We show that the exposure time to inhale O(100) aerosols could range from O(1s) to O(1min) or even to O(1h) depending on the situation. The Monte-Carlo simulations, along with the theory, provide clear quantitative insight to the exposure time in different public indoor environments.
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Deng, X., Gong, G., He, X., Shi, X., & Mo, L. (2021) · Journal of Environmental Sciences · doi:10.1016/j.jes.2021.01.025 · Cited by 33 · see citing works
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Quantifying superspreading for COVID-19 using Poisson mixture distributions
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Risk assessment of airborne COVID-19 exposure in social settings
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- Authors
- Vuorinen, V., Aarnio, M., Alava, M., Alopaeus, V., Atanasova, N., Auvinen, M., Balasubramanian, N., Bordbar, H., Erästö, P., Grande, R., Hayward, N., Hellsten, A., Hostikka, S., Hokkanen, J., Kaario, O., Karvinen, A., Kivistö, I., Korhonen, M., Kosonen, R., Kuusela, J., Lestinen, S., Laurila, E., Nieminen, H. J., Peltonen, P., Pokki, J., Puisto, A., Råback, P., Salmenjoki, H., Sironen, T., & Österberg, M.
- Affiliations
- Aalto University (FI)
- Finnish Meteorological Institute (FI)
- CSC - IT Center for Science (Finland) (FI)
- VTT Technical Research Centre of Finland (FI)
- Mikkelin keskussairaala (FI)
- University of Helsinki (FI)
- Corresponding author
- Vuorinen, Ville
- ORCID
- Vuorinen, Ville
- Alava, Mikko
- Alopaeus, Ville
- Atanasova, Nina
- Auvinen, Mikko
- Bordbar, Hadi
- Erästö, Panu
- Grande, Rafael
- Hayward, Nick
- Hellsten, Antti
- Hostikka, Simo
- Kaario, Ossi
- Kivistö, Ilkka
- Korhonen, Marko
- Kosonen, Risto
- Lestinen, Sami
- Laurila, Erkki
- Nieminen, Heikki J.
- Peltonen, Petteri
- Pokki, Juho
- Puisto, Antti
- Råback, Peter
- Salmenjoki, Henri
- Sironen, Tarja
- Österberg, Monika
- Venue
- Safety Science
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 130
- Pages
- 104866
- ISSN
- 0925-7535 (ISSN-L)
- PMC
- PMC7428778
- OpenAlex
- W3029277938
- Cited by
- 472
- References
- 117
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 53 · 2023: 72 · 2022: 100 · 2021: 152 · 2020: 36
- FWCI
- 50.95
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Safety Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Aaltodoc (Aalto University) (submittedVersion, cc-by)
- Työväentutkimus Vuosikirja (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Academy of Finland (314487, 309570)
- Scientific Advisory Board for Defense (VN/627/2020-PLM-9)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Infection Control and Ventilation
- Health Sciences › Dentistry › General Dentistry › Dental Research and COVID-19
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- SARS-CoV-2, airborne transmission, aerosol transport, droplet dispersion, droplet nuclei, droplet drying, indoor transmission, exposure time, aerosol dilution, computational fluid dynamics, Monte Carlo simulation, COVID-19 pandemic, safety distance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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