Your browser doesn't support javascript.
Non-uniform risk assessment methods for personalized ventilation on prevention and control of COVID-19
Chinese Science Bulletin-Chinese ; 66(4-5):465-474, 2021.
Article in Chinese | Web of Science | ID: covidwho-1172858
ABSTRACT
Ventilation is an important measure to prevent the transmission of COVID-19 in indoor environment. The protection effect of personalized ventilation in disease control has been preliminarily verified in previous studies. It is considered as one of the promising measures to control airborne transmission indoors due to its high ventilation efficiency with direct delivery of fresh and clean air to the occupant's breathing zone. Especially for vehicles like aircrafts and coaches, in which the personalized ventilation has been used for years, its applications in COVID-19 epidemic control should be further explored. However, most of the present airborne infection risk prediction models are based on the assumption that the exhaled pathogens are evenly distributed in the room. The non-uniform distribution of pathogens in indoor environment cannot be accurately predicted with these models when applying localized ventilation or considering the short-ranged transmission of airborne pathogens. In this study, five different non-uniform risk assessment models were verified by the tracer particle experiments with thermal manikins. The assessment methods included exposure risk index (epsilon(bz)), personal exposure effectiveness (PEE), intake fraction (IF), dose-response model (P(t)) and infection risk reduction ratio (eta). Two breathing thermal manikins were used to simulate two seated passengers in public transportation placed side by side. The nebulized aerosols with similar size distribution to human breathing were released from the source manikin's mouth. The concentration of the received particles at the receptor's breathing zone was sampled with an aerodynamic particle sizer (APS). The receptor's exposure level of droplets released by the exhalation of the infector was predicted accordingly considering the usage patterns of the personalized ventilation. The applicability of each model for predicting the risk of airborne transmission of SARS-CoV-2 was discussed. The results show that the five assessment methods are all based on the measurement results of the droplet concentration in the breathing zone of the exposed occupant. These models can predict the intervention effect of personalized ventilation on the airborne exposure or infection risk, and the trends of the predicted results are basically consistent. The relative exposure level can be predicted by exposure risk index epsilon(bz), PEE and IF, which can be used to simply assess the exposure risk of SARS-CoV-2. The dose-response model can directly assess the infection risk of specific airborne disease such as COVID-19, but only when the viability and infectivity of the virus are accurately determined. And infection risk index. can evaluate the infection risk reduction ratio due to the use of personalized ventilation. These assessment methods can all reflect the problems of risk increase caused by lateral spread and accelerated diffusion of the droplet-laden air due to the application of personalized ventilation to the infector. The protective effect for the exposed occupant can also be evaluated by these models when PV is used for the receptor. The exposure risk of the receptor can be lowered by using the personalized ventilation and the protection effect is increased by using higher volume of clean air. This study can provide support and reference for the evaluation and development of novel ventilation methods for airborne disease control in indoor environment and provide basis for assessment of the transmission risk of COVID-19 under non-uniform conditions.

Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: Chinese Journal: Chinese Science Bulletin-Chinese Year: 2021 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Prognostic study Language: Chinese Journal: Chinese Science Bulletin-Chinese Year: 2021 Document Type: Article