Airborne respiratory aerosol transport and deposition in a two-person office using a novel diffusion-based numerical...

The COVID-19 pandemic was caused by the SARS-CoV-2 coronaviruses transmitted mainly through exposure to airborne respiratory droplets and aerosols carrying the virus.

To assess the transport and dispersion of respiratory aerosols containing the SARS-CoV-2 virus and other viruses in a small office space using a diffusion-based computational modeling approach.

A 3-D computational model was used to simulate the airflow inside the 70.2 m³ ventilated office. A novel diffusion model accounting for turbulence dispersion and gravitational sedimentation was utilized to predict droplet concentration transport and deposition. The numerical model was validated and used to investigate the influences of partition height and different ventilation rates on the concentration of respiratory aerosols of various sizes (1, 10, 20, and 50 µm) emitted by continuous speaking.

An increase in the hourly air change rate (ACH) from 2.0 to 5.6 decreased the 1 μm droplet concentration inside the office by a factor of 2.8 and in the breathing zone of the receptor occupant by a factor of 3.2. The concentration at the receptor breathing zone is estimated by the area-weighted average of a 1 m diameter circular disk, with its centroid at the center of the receptor mannequin mouth. While all aerosols were dispersed by airflow turbulence, the gravitational sedimentation significantly influenced the transport of larger aerosols in the room. The 1 and 10 μm aerosols remained suspended in the air and dispersed throughout the room. In contrast, the larger 20 and 50 μm aerosols deposited on the floor quickly due to the gravitational sedimentation. Increasing the partition between cubicles by 0.254 m (10") has little effect on the smaller aerosols and overall exposure.

This paper provides an efficient computational model for analyzing the concentration of different respiratory droplets and aerosols in an indoor environment. Thus, the approach could be used for assessing the influence of the spatial concentration variations on exposure for which the fully mixed model cannot be used.