Influence of human breathing modes on airborne cross infection risk

Abstract

CFD simulation is an accurate and reliable method to predict the risk of airborne cross-infection in a room. This paper focuses on the validation of a 3-D transient CFD model used to predict personal exposure to airborne pathogens and infection risk in a displacement ventilated room. The model provides spatial and temporal solutions of the airflow pattern in the room (temperature, velocity and turbulence), as well as contaminant concentration in a room where two thermal manikins simulate two standing people, one of whom exhales a tracer gas N2O simulating airborne contaminants. Numerical results are validated with experimental data and the model shows a high accuracy when predicting the transient cases studied. Once the model is validated, the CFD model is used to simulate different airborne cross-infection risk scenarios. Four different combinations of the manikins’ breathing modes and four different separation distances between the two manikins are studied. The results show that exhaling through the nose or mouth disperses exhaled contaminants in a completely different way and also means that exhaled contaminants are received differently. For short separation distances between breathing sources the interaction between breaths is a key factor in the airborne cross-infection for all the breathing mode combinations studied. However, for long distances the general airflow conditions in the room prove to be more important.