Large Eddy Simulation-Based Optimization of Occupant Configurations in Pre-Existing Indoor Spaces for Mitigating Airborne Pathogen Transmission

The transmission of indoor pollutants, particularly in the context of airborne diseases, has seen extensive research, with a renewed focus fueled by the SARS-CoV-2 pandemic. Current models consider both spatial and temporal variations in pollutant concentrations, but guidelines often focus mainly on temporal factors, neglecting spatial configurations. This oversight is particularly relevant in environments like restaurants and classrooms, where both safety and operational efficiency are crucial. While most studies advocate for changes in ventilation systems, such modifications are not always feasible in pre-existing spaces. Our study addresses this gap by utilizing a statistical overloading framework and high-fidelity Large Eddy Simulations (LES) to determine optimal spatial configurations for minimizing infection risk. We simulate airflow in a canonical room, tracking over 20 million droplet nuclei of various sizes to analyze how different occupant configurations and densities affect pathogen dispersion. We apply these insights to a classroom setting, maintaining a minimum separation distance of 1.6 meters between the occupants and analyze the adherence to these constraints with various occupant population densities while modeling different exposure intervals to determine an optimal occupancy layout. The findings reveal that while social distancing is crucial in mitigating transmission risk, localized airflow dynamics and zonal analysis can also be equally important. Therefore, developing a model to generate evidence-based, spatially informed guidelines for various environments is imperative for advancing indoor air quality and infection control strategies. This groundwork aims to deliver a coherent paradigm that shifts from the simplistic two-meter separation rule by accounting for diverse spatial configurations.