Minimizing pathogen transmission through indoor environment optimization using central composite design of experiment

The human respiratory emission mechanism generates virus particles such as the COVID-19 virus that exists in both aerosols and water droplets form. Trajectories of airborne pathogens transmitted from coughing are influenced by the air flow pattern surrounding the transmission source, making it challenging to predict. ANSYS Fluent 2022 R1 is employed in this study to visualize the airborne pathogen transmission in indoor space of different environmental conditions with steady-state and periodic wind velocity. The effect of five different indoor parameters on airborne transmission is studied, which include wind velocity, air temperature, relative humidity of the air, particle residence time after emission, and the physical distancing of 3 feet, 6 feet, and 10 feet between two humans. The characteristics between airborne transmission and varying indoor conditions have motivated this study to correlate their dynamic relationship. Due to the limitation in controlling indoor conditions, obtaining optimum indoor conditions with minimal airborne transmission remains challenging. Simulation-based design of experiment of central composite design type yields optimum indoor condition with a wind velocity of 4.337 m/s, air temperature of 24.26 degrees C, and relative humidity of 40.14% showed 58.14% reduction in deposited particle mass relative to the base condition at a similar physical distancing of 3 feet. Surrounding air with higher relative humidity leads to a larger mean diameter of particles due to the hygroscopic growth effect. In comparison, the evaporation effect dominates in lower air humidity, leading to particle shrinkage. Particle deposition from human to human is lower with longer distances between humans, with comparable behaviour of particle transmission at each successive time step, suggesting prolonged exposure risk of the healthy individual as time progresses. This research focuses on adopting engineering simulation to find optimum indoor conditions with minimal human-to-human airborne transmission to strengthen safety and health judgement during a pandemic.

Automated summary

This study employs ANSYS Fluent 2022 R1 to visualize the airborne transmission of pathogens in indoor spaces under different environmental conditions. Five indoor parameters are studied, including wind velocity, air temperature, relative humidity, particle residence time, and physical distancing. Simulation-based experiments reveal the optimum indoor condition for reducing human-to-human airborne transmission. Higher relative humidity results in larger particle diameter, while lower humidity leads to particle shrinkage. Furthermore, longer distances between individuals result in lower particle deposition and prolonged exposure risk. This research is significant for enhancing safety and health judgement during a pandemic by identifying the optimal indoor conditions to minimize airborne transmission between individuals.