The Aerosol and Particulate Research Laboratory (APRL) is at the forefront of research exploring how viruses and genetic materials travel through the air and impact human health and the environment. Our bioaerosol transmission studies focus on understanding and improving how we detect, collect, and analyze airborne biological agents.
Instrument Design and Development for Liquid Stabilization and Viability Preservation:
This ongoing work focuses on advancing the BioCascade Impactor (BC) by addressing challenges associated with evaporation-driven volume loss and reduced microbial viability during extended sampling, particularly under low relative humidity conditions. To tackle this, a modified BC system is being developed that integrates temperature control with a programmable, stage-specific water-injection module designed to stabilize the collection medium over time. Current efforts involve quantifying evaporation dynamics across varying environmental conditions and refining injection profiles to maintain consistent liquid volumes without disrupting the sampler’s aerodynamic performance. Experimental studies using bacteriophage aerosols are being conducted to evaluate how these modifications influence viable virus recovery across different humidity levels and sampling durations, with particular attention to maintaining size-resolved collection integrity.
Field Deployment and Genomic Characterization of Airborne Pathogens in Clinical Settings:
In parallel, the enhanced BioCascade platform is being deployed in clinical environments to investigate airborne pathogen dynamics using a size-resolved sampling approach. This work involves collecting bioaerosols in high-risk hospital settings and analyzing them using Oxford Nanopore sequencing to enable real-time, untargeted characterization of microbial communities present in the air. Ongoing efforts are focused on linking particle size distributions with genomic data to better understand the presence and behavior of airborne pathogens, including multi-drug resistant organisms (MDROs). Field sampling is being conducted alongside environmental monitoring to assess how factors such as ventilation, occupancy, and humidity influence pathogen occurrence. Complementary culture-based analyses are also being performed to support genomic findings and to further explore the viability and transmission potential of clinically relevant microorganisms in aerosol form.
Machine Learning–Driven Optimization of VIVAS Performance:
This ongoing research focuses on developing a data-driven framework to improve the performance and reliability of the Viable Virus Aerosol Sampler (VIVAS) under varying environmental conditions. Current efforts involve building an extendable machine learning pipeline to predict whether specific combinations of environmental parameters and instrument settings will produce optimal liquid collection levels, which are critical for preserving virus viability. Using an experimental dataset, models are being trained and evaluated to identify key factors influencing sampler performance, with particular emphasis on temperature control and ambient particle conditions. Advanced algorithms such as XGBoost are being explored and refined through cross-validation and hyperparameter tuning to enhance predictive accuracy. In parallel, a user-friendly web-based application is under development to enable real-time prediction of optimal operating conditions, allowing researchers and practitioners to adapt VIVAS settings dynamically based on environmental inputs. This work aims to establish a flexible and continuously improving framework for intelligent aerosol sampling system optimization.
