American Association for Aerosol Research - Abstract Submission

AAAR 39th Annual Conference
October 18 - October 22, 2021

Virtual Conference

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Estimating SARS-CoV-2 Infection Risk in University Residence Halls Using CO2 Pulse Injections

Daniel Amparo, RYAN MORAVEC, Barbara Turpin, Glenn Morrison, UNC-Chapel Hill

     Abstract Number: 211
     Working Group: Bioaerosols

Abstract
Exploring the dynamics air movement in buildings can provide insight into SARS-CoV-2 transmission risk. This study focused on better understanding SARS-CoV-2 transmission risk within three university residence halls that had experienced outbreaks of COVID-19. Each residence hall differed in room and suite layout, HVAC system, and volume. We repeatedly released pulses of CO2 into a source (infectious person) room and measured the dynamic CO2 concentration in the source room and receptor rooms which were above, below, and adjacent to the source room. Air change rates in the source room were calculated from the decay rate of CO2. The proportion of shared air was determined by comparing the integrated dynamic CO2 concentration in the source and receptor rooms. The mean transport time between rooms was determined by applying a residence time distribution analysis to the same data. The middle 80% of results ranged from 1.1/h to 7.8/h for source room air change rates, 0.02 to 0.3 proportion of shared air and 0.3 to 1.9h mean transport time. Applying a Wells-Riley analysis using these results, the risk of SARS-CoV-2 infection in adjacent rooms ranged from 0.02 to 0.5 assuming an average quanta emission rate of 5 quanta per hour and exposure duration of 3.5 days. Door position (e.g. ajar or closed) both increased and decreased risk, depending on the location of the receptor room. Strategies to reduce risk of transmission in an occupied residence hall include improved filtration, increased ventilation, and reduced contact time. The magnitude of room-to-room deposition losses, SARS-CoV-2 deactivation rates, and quanta emission rates introduce the greatest variability into our estimates.