Indoor air sampling for detection of viral nucleic acids

Detecting and monitoring airborne viruses is critical for mitigating the spread of infectious diseases and safeguarding public health. Indoor air sampling has emerged as a promising but underexplored approach to the surveillance of viral pathogens, complementing more established methods such as clinical diagnostics and wastewater-based epidemiology. Indoor air contains viral particles originating from human occupants through exhaled respiratory droplets, skin shedding, and resuspension of contaminated dust, making it a valuable target for viral biosurveillance. Although viruses typically account for less than 1% of total sequences in metagenomic data from air samples, a wide range of human-infecting viruses, including respiratory and skin-associated pathogens, can be detected using various capture mechanisms. This review provides a comprehensive examination of indoor air sampling for viral nucleic acid detection, focusing on the sources and composition of viruses indoors; sampling technologies; and strategies for effective implementation. We examine three complementary approaches to viral collection: active air sampling using dedicated collection devices; passive sampling primarily through settled dust analysis; and monitoring of building heating, ventilation, and air conditioning (HVAC) systems. For active sampling, we assess previously published results on the efficacy of filtration, impaction, impingement, cyclonic separation, electrostatic precipitation, and condensation for viral collection, highlighting challenges in optimizing sampling efficiency for submicrometer particles and recovering sufficient viral nucleic acids for analysis. For passive and HVAC-based approaches, we evaluate their potential for providing temporally-integrated, building-wide viral surveillance data while requiring minimal additional infrastructure. We examine potential strategies for implementing these approaches, particularly in airports and healthcare facilities. Future studies should focus on addressing such knowledge gaps, optimizing sampling methodologies, and developing best practices for integrating air sampling into public health surveillance systems.