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A platform for research: civil engineering, architecture and urbanism
Developing a survival probability-based Lagrangian model for predicting ultraviolet disinfection of bioaerosols in enclosed environments
https://orcid.org/0000-0002-1175-7709
https://orcid.org/0000-0002-9279-8077
Abstract The transmission of airborne infectious diseases in enclosed environments has become a significant public health issue. Far-ultraviolet C (UVC) lights can be used safely and effectively to disinfect bioaerosols and reduce the risk of airborne infectious disease transmission. To ensure proper use of far-UVC systems, an improved Lagrangian method for predicting the dispersion, deposition, and far-UVC disinfection of bioaerosols was proposed. First, an improved Lagrangian model considering the probability of bioaerosol survival was developed for predicting the UVC disinfection of airborne bioaerosols in enclosed environments. The UVC disinfection of surfaces with deposited bioaerosols was implemented into the deposition module of the Lagrangian model by introducing the concept of equivalent deposition number. Experimental data on far-UVC disinfection of E. Coli in the air and on the surfaces in a ventilated chamber were used to validate the proposed model. The validation results show that the proposed model can predict the bioaerosol dispersion, deposition, and far-UVC disinfection reasonably well. The validated model was then employed to calculate the far-UVC disinfection of airborne and surface SARS-CoV-2 bioaerosols in a Mass Transit Railway (MTR) train compartment in Hong Kong. According to the calculation results, the far-UVC lamps are effective in reducing the passengers’ exposure to SARS-CoV-2 bioaerosols.
Highlights A novel Lagrangian model was developed for predicting UV disinfection of bioaerosols. The proposed Lagrangian model was validated by experimental data. Far-UVC disinfection of SARS-CoV-2 bioaerosols in a train compartment was investigated.
Developing a survival probability-based Lagrangian model for predicting ultraviolet disinfection of bioaerosols in enclosed environments
https://orcid.org/0000-0002-1175-7709
https://orcid.org/0000-0002-9279-8077
Abstract The transmission of airborne infectious diseases in enclosed environments has become a significant public health issue. Far-ultraviolet C (UVC) lights can be used safely and effectively to disinfect bioaerosols and reduce the risk of airborne infectious disease transmission. To ensure proper use of far-UVC systems, an improved Lagrangian method for predicting the dispersion, deposition, and far-UVC disinfection of bioaerosols was proposed. First, an improved Lagrangian model considering the probability of bioaerosol survival was developed for predicting the UVC disinfection of airborne bioaerosols in enclosed environments. The UVC disinfection of surfaces with deposited bioaerosols was implemented into the deposition module of the Lagrangian model by introducing the concept of equivalent deposition number. Experimental data on far-UVC disinfection of E. Coli in the air and on the surfaces in a ventilated chamber were used to validate the proposed model. The validation results show that the proposed model can predict the bioaerosol dispersion, deposition, and far-UVC disinfection reasonably well. The validated model was then employed to calculate the far-UVC disinfection of airborne and surface SARS-CoV-2 bioaerosols in a Mass Transit Railway (MTR) train compartment in Hong Kong. According to the calculation results, the far-UVC lamps are effective in reducing the passengers’ exposure to SARS-CoV-2 bioaerosols.
Highlights A novel Lagrangian model was developed for predicting UV disinfection of bioaerosols. The proposed Lagrangian model was validated by experimental data. Far-UVC disinfection of SARS-CoV-2 bioaerosols in a train compartment was investigated.
Developing a survival probability-based Lagrangian model for predicting ultraviolet disinfection of bioaerosols in enclosed environments
https://orcid.org/0000-0002-1175-7709
https://orcid.org/0000-0002-9279-8077
Abstract The transmission of airborne infectious diseases in enclosed environments has become a significant public health issue. Far-ultraviolet C (UVC) lights can be used safely and effectively to disinfect bioaerosols and reduce the risk of airborne infectious disease transmission. To ensure proper use of far-UVC systems, an improved Lagrangian method for predicting the dispersion, deposition, and far-UVC disinfection of bioaerosols was proposed. First, an improved Lagrangian model considering the probability of bioaerosol survival was developed for predicting the UVC disinfection of airborne bioaerosols in enclosed environments. The UVC disinfection of surfaces with deposited bioaerosols was implemented into the deposition module of the Lagrangian model by introducing the concept of equivalent deposition number. Experimental data on far-UVC disinfection of E. Coli in the air and on the surfaces in a ventilated chamber were used to validate the proposed model. The validation results show that the proposed model can predict the bioaerosol dispersion, deposition, and far-UVC disinfection reasonably well. The validated model was then employed to calculate the far-UVC disinfection of airborne and surface SARS-CoV-2 bioaerosols in a Mass Transit Railway (MTR) train compartment in Hong Kong. According to the calculation results, the far-UVC lamps are effective in reducing the passengers’ exposure to SARS-CoV-2 bioaerosols.
Highlights A novel Lagrangian model was developed for predicting UV disinfection of bioaerosols. The proposed Lagrangian model was validated by experimental data. Far-UVC disinfection of SARS-CoV-2 bioaerosols in a train compartment was investigated.
Developing a survival probability-based Lagrangian model for predicting ultraviolet disinfection of bioaerosols in enclosed environments
Pan, Yue (author) / Guo, Kangqi (author) / Lin, Chao-Hsin (author) / Wei, Daniel (author) / Ho, Kin-Fai (author) / Chen, Chun (author)
Building and Environment ; 244
2023-08-21
Article (Journal)
Electronic Resource
English
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