The Detection of Respiratory Viruses in Heating, Ventilation, and Air Conditioning Units of University Buildings
ZHOUYUAN WANG, Yangyang Zou, Irene Xagoraraki, Kaisen Lin,
Michigan State University Abstract Number: 403
Working Group: Aerosol Science of Infectious Diseases: Lessons and Open Questions on Models, Transmission and Mitigation
AbstractRespiratory viruses, such as SARS-CoV-2 and influenza virus, are predominantly transmitted through the air and can cause human infections through inhalation. Therefore, it is imperative to closely and accurately monitor airborne respiratory viruses in indoor environments to protect public health. During the pandemic, numerous studies have reported detecting airborne SARS-CoV-2 in healthcare settings and quarantine rooms through active sampling. However, less effort has been made in surveilling other indoor spaces, such as commercial buildings and schools, where respiratory viruses may linger longer due to lower ventilation rates, leading to disease transmission. The goal of this study is to develop a novel approach to monitoring the outbreak and spread of respiratory diseases in public buildings. We collected 6 air filters from 3 heating, ventilation, and air conditioning units, including 3 MERV 8 pre-filters and 3 MERV 13 final filters, at Michigan State University's campus. These filters were installed in Nov 2022 and collected in Jan 2023. We recovered dust from air filters and extracted viral RNA from the dust. Qubit assays confirmed that viral RNA was successfully extracted from filter samples with as little as 50 mg dust with a concentration ranged from 0.74 to 12.31 μg RNA/ g dust. SARS-CoV-2 virus was detected on 4 out of 6 filter samples using droplet digital PCR. The concentrations were determined to be 753.30- 1829.57 genomic copies/ g dust. The results indicated the prevalence of airborne SARS-CoV-2 virus in lecture buildings on the university campus. It also demonstrated that HVAC systems can be used as a powerful sampling tool to monitor respiratory viruses. This approach will enable high temporal and spatial resolution sample collection, and results can supplement existing wastewater-based surveillance to monitor infection trends in communities.