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Modeling and experimental study of dispersion and deposition of respiratory emissions with implications for disease transmission
Author(s) -
Coldrick Simon,
Kelsey Adrian,
Ivings Matthew J.,
Foat Timothy G.,
Parker Simon T.,
Noakes Catherine J.,
Bennett Allan,
Rickard Helen,
Moore Ginny
Publication year - 2022
Publication title -
indoor air
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.387
H-Index - 99
eISSN - 1600-0668
pISSN - 0905-6947
DOI - 10.1111/ina.13000
Subject(s) - indoor bioaerosol , airborne transmission , deposition (geology) , exhalation , dispersion (optics) , aerosol , environmental science , transmission (telecommunications) , particle deposition , meteorology , atmospheric sciences , atmospheric dispersion modeling , airflow , computational fluid dynamics , covid-19 , medicine , mechanics , biology , air pollution , disease , physics , computer science , ecology , optics , thermodynamics , pathology , paleontology , telecommunications , radiology , sediment , infectious disease (medical specialty)
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.