Linear predictive model of viral load from air samples in simulated conditions

Airborne viruses persist and propagate in indoor settings, increasing the likelihood of outbreaks and pandemics such as the SARS-CoV-2 Pandemic. Robust air sampling methods capable of detecting, identifying, and quantifying airborne viral particles can provide critical information for mitigating these risks. However, there is still no scientific consensus on standardized protocols for recovering airborne viruses indoors. This study aimed to formulate and validate a standardized protocol for air sampling and analysis tailored to accurately detect and quantify viral aerosols in controlled indoor environments. The research utilized three concentrations of MS2 bacteriophage (1 × 10⁶, 1 × 10⁹, and 1 × 10¹² genomic copies per liter, gc/L), a common surrogate for airborne viruses, aerosolized as particles ranging from 5 to 50 µm in an aerobiological chamber of 11,230 L. Experiments were performed under controlled conditions at 24 °C and 84% relative humidity. Air sampling employed the MD8 Airport (Sartorius) with gelatin filters, varying sampling flow rates (20, 30, and 50 L/min) and durations (5, 15, and 25 min), resulting in a recovery efficiency of 80 ± 26.5%. A linear regression model was developed to predict environmental viral particle numbers from qPCR recovery data. The results indicate that lower sampling flows (20 L/min) achieve more precise measurements for low viral concentrations, while their efficiency diminishes for extremely high concentrations over extended sampling times. The proposed methodology, combining gelatin filter samplers and qPCR, proved highly effective, detecting airborne viral loads as low as 1 × 10⁶ gc/L during short sampling periods, providing consistent results for rapid monitoring of viral particles at low concentrations in closed environments.