Study on Ventilation Efficiency and Infection Probability in the Outbreak at Restaurant

Abstract

With the spread of the coronavirus infection, ventilation efficiency, new design styles, air conditioning and ventilation operation methods, existing building improvement plans, and other building infection risk reduction methods need to be systematized. This study used computational fluid dynamics (CFD) to recreate a series of cases in which nine out of 89 people were infected at a restaurant in Guangzhou, China. Although the importance of ventilation has been reaffirmed, when making a general ventilation plan, ignore the pollutant concentration distribution in the room and calculate the required ventilation volume assuming complete ventilation in the room. In a real space, the generation of pollution sources are local, and the airflow properties in the room, arrival/distribution of fresh air, and discharge properties of the generated pollutants differ greatly; thus, non-uniform concentration fields, ventilation efficiency distributions, and infection probability distributions also occur. A series of cases infected at a restaurant in Guangzhou was evaluated by Scale for Ventilation Efficiency 3,4 (SVE3, SVE4). SVE3 is corresponding to the traveling time of air from the supply outlet to each point. SVE4 indicates the contribution ratio of a supply opening to air at a point in a room. In addition, the Wells–Riley model (WRM) is a typical model for quantitatively evaluating the risk of airborne infections, and cases of numerical analysis have been reported in various countries worldwide.
However, WRM assumes that the distribution of indoor droplets is uniform and the droplet concentration is stable, and that floating fine particles with a nonuniform concentration field from the active state of the virus, gravity sedimentation, and a non-uniformly distributed pollution source. A diffusion phenomenon is possible, but there is a problem that has not been addressed. Previous studies have excluded diffusion phenomena from their evaluation because gravity sedimentation is significantly less than inactivation, and the cause of local cluster formation is inadequate.This study aims to establish a predictive flow for infection control using
CFD.