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Modeling and experimental study of dispersion and deposition of respiratory emissions with implications for disease transmission.
Coldrick, Simon; Kelsey, Adrian; Ivings, Matthew J; Foat, Timothy G; Parker, Simon T; Noakes, Catherine J; Bennett, Allan; Rickard, Helen; Moore, Ginny.
  • Coldrick S; Health and Safety Executive, Buxton, Derbyshire, UK.
  • Kelsey A; Health and Safety Executive, Buxton, Derbyshire, UK.
  • Ivings MJ; Health and Safety Executive, Buxton, Derbyshire, UK.
  • Foat TG; Defence Science and Technology Laboratory, Salisbury, UK.
  • Parker ST; Defence Science and Technology Laboratory, Salisbury, UK.
  • Noakes CJ; Leeds Institute for Fluid Dynamics, School of Civil Engineering, University of Leeds, Leeds, UK.
  • Bennett A; National Infection Service, UKHSA, Salisbury, UK.
  • Rickard H; National Infection Service, UKHSA, Salisbury, UK.
  • Moore G; National Infection Service, UKHSA, Salisbury, UK.
Indoor Air ; 32(2): e13000, 2022 02.
Article in English | MEDLINE | ID: covidwho-1714194
ABSTRACT
The ability to model the dispersion of pathogens in exhaled breath is important for characterizing transmission of the SARS-CoV-2 virus and other respiratory pathogens. A Computational Fluid Dynamics (CFD) model of droplet and aerosol emission during exhalations has been developed and for the first time compared directly with experimental data for the dispersion of respiratory and oral bacteria from ten subjects coughing, speaking, and singing in a small unventilated room. The modeled exhalations consist of a warm, humid, gaseous carrier flow and droplets represented by a discrete Lagrangian particle phase which incorporates saliva composition. The simulations and experiments both showed greater deposition of bacteria within 1 m of the subject, and the potential for a substantial number of bacteria to remain airborne, with no clear difference in airborne concentration of small bioaerosols (<10 µm diameter) between 1 and 2 m. The agreement between the model and the experimental data for bacterial deposition directly in front of the subjects was encouraging given the uncertainties in model input parameters and the inherent variability within and between subjects. The ability to predict airborne microbial dispersion and deposition gives confidence in the ability to model the consequences of an exhalation and hence the airborne transmission of respiratory pathogens such as SARS-CoV-2.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollution, Indoor / Air Microbiology / COVID-19 / Respiratory Aerosols and Droplets Type of study: Prognostic study Limits: Humans Language: English Journal: Indoor Air Journal subject: Environmental Health Year: 2022 Document Type: Article Affiliation country: Ina.13000

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollution, Indoor / Air Microbiology / COVID-19 / Respiratory Aerosols and Droplets Type of study: Prognostic study Limits: Humans Language: English Journal: Indoor Air Journal subject: Environmental Health Year: 2022 Document Type: Article Affiliation country: Ina.13000