Airborne transmission plays a significant role in cross infection in healthcare facilities, such as COVID-19. The concentration of infectious aerosols in these areas should be controlled to reduce exposure risks to susceptibles. Air distribution (ventilation) is an effective engineering approach to controlling airborne infection risks, particularly during the COVID-19 pandemic. Stratum ventilation (SV), a novel air distribution solution, has shown promise in reducing airborne infection risks by directly supplying fresh air to the breathing zone. However, its recognition in mitigating airborne infection risks in patient wards is limited.

This study establishes an experimentally validated computational fluid dynamics (CFD) model to investigate the contaminant distribution in a two-bed patient ward under SV, mixing ventilation (MV), downward ventilation (DWV), and displacement ventilation (DV). Tracer gas (CO₂) is used as a biomarker to simulate the exhaled and coughed contaminants by patients in various positions. The results show that SV had lower contaminant concentrations in the breathing zone and higher contaminant removal effectiveness. The coughed contaminant is diluted quickly under stratum ventilation, and the high-concentration spot is substantially reduced.

The interaction between SV airflow and coughed droplets is further investigated numerically using the Lagrangian approach. The size of a droplet is a crucial factor influencing its dispersion pattern. Droplets of the initial size of 10 μm, 20 μm, 50 μm, and 100 μm are investigated, and the evaporation process is considered to simulate the size decreasing during the dispersion process. The horizontal airflow of SV leads to an intense droplet deposition at the initial dispersion stage, decreasing droplet concentration in the air. With the infector in the supine and sitting positions, 68% and 49% of the coughed droplets are deposited on indoor surfaces in 10 seconds, respectively. Compared with DV, the cough flow dissipates faster under SV because the relatively high airflow velocity in the breathing zone suppresses the cough flow, which prevents the spread of contaminants in the breathing zone. For DV, the cough flow dominates the droplet dispersion in the breathing zone because of the low momentum of the ambient airflow.

Compared with experiments and CFD simulations, zonal models are convenient to implement. A zonal model is proposed to predict dynamic non-uniform contaminant distribution in stratum-ventilated rooms. The zoning method is based on the unique airflow pattern under SV, and the room is divided into the jet zone, entrainment zone, and mixing zone. The proposed zonal model shows better accuracy for non-uniform air distribution under stratum ventilation than the conventional zonal model.

Two ventilation indices, air utilization effectiveness (AUE) and contaminant dispersion index (CDI), are proposed. A three-step correlation analysis based on Spearman’s rank correlation coefficient, Pearson correlation coefficient, and goodness of fit and a min-max normalization-based error analysis is developed to test the validity of ventilation indices qualitatively and quantitatively. The results recommend the integrated index of AUE and CDI to indicate the overall airborne infection risk and CDI to indicate the local airborne infection risk, respectively, regardless of the effects of air distribution, supply airflow rate, infectivity intensity, room configuration and occupant distribution.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Taguchi methods are employed to optimize the design parameters of stratum ventilation for patient wards under cooling and heating conditions. The energy efficiency and airborne infection control performance with different supply and return air outlet layouts with supply airflow rates of 6 -12 ACH are compared. A return air grille at the ceiling and a supply diffuser height of 1.5 m is recommended. For cooling conditions, a supply airflow rate of 6 ACH is recommended. A supply vane angle of 60º and a supply airflow rate of 12 ACH are preferred for heating conditions.

The findings of this study have significant implications for designing and implementing effective air distribution systems in healthcare facilities to control airborne infection risks.