Fast prediction and real-time visualization of indoor particle distribution based on a Markov chain model integrated graphics user interface

Rapid access to data on the dispersion and transmission of airborne particulate pollutants is crucial for controlling indoor environment and preventing respiratory infectious diseases. In this study, a modified Markov chain model was introduced and the reliability of the model was validated by experimental data. Then a Markov chain model integrated with graphics-user interface (GUI) was developed for fast prediction and real-time visualization of indoor airborne particle dispersion. Simulations and analyses of aerosol transmission in a lift were conducted using the GUI. Factors such as the infected occupant's location and mask-wearing behaviour influencing infection risk were assessed. The results showed that the exposure degree and infection risk of susceptible occupants were affected by the infected occupant's location and mask-wearing behaviour. The closer the infected occupant to released virus particles from the air inlet, the greater the impact on the susceptible occupants. The developed Markov chain model integrated with the GUI, has enabled quick and precise presentation of real-time dynamic transport of airborne contaminants under a known steady-state flow field. This would provide strong support for assessing and controlling the spread of aerosol contaminants and have a potential for application to minimize risk of accidental release of hazardous pollutants.