Performance of the new spatiotemporal airborne infection risk model across varied indoor air flowrates: An experimental study

Abstract

Understanding the complex dynamics of indoor airflows is crucial for mitigating airborne infection risks in ventilated spaces. These airflows can be simplified into two populations: Recirculating air that spreads contaminants and outgoing air that evacuates them. Quantifying these populations involves analyzing mass transfer between zones in the room/building. This study builds on the newly proposed model that enhances the Wells-Riley model by incorporating indoor airflow interaction mechanisms. The study explores the transfer probability between zones and the recirculation and purging flowrate at the target location and its impact on the risk of infection in a ventilated room. Our contributions include: (i) Performance evaluation of the revised model that accounts for transfer probabilities between zones and purging flowrates; (ii) a novel tracer-gas measurement method to determine local purging flowrates; and (iii) an analysis of how different ventilation systems interact with internal room flow. We validated the proposed model through experimental measurements in a climate chamber, examining contaminant source locations under varying ventilation rates using mixing ventilation (MV) and displacement ventilation (DV). Results reveal significant spatial and temporal heterogeneities in contaminant distribution, with MV showing pronounced temporal variability and DV exhibiting significant spatial variations. Under MV, purging flowrates increase with higher ventilation rates, whereas DV shows no such change. Our findings underscore the importance of considering airflow dynamics in ventilation design to effectively reduce contaminant transfer and/or airborne infection transmission.