Infection probability under different air distribution patterns

Wei Su, Bin Yang, Arsen Melikov, Chenjiyu Liang, Yalin Lu, Faming Wang*, Angui Li, Zhang Lin, Xianting Li, Guangyu Cao, Risto Kosonen

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review


    Infectious diseases have caused significant physical harm to humans as well as enormous economic losses over the years. Effective ventilation and distribution of fresh air could help to reduce indoor cross-infection. The computational fluid dynamics (CFD) method was used in this paper to investigate airborne transmission with seven different air distribution methods. The revised Wells-Riley model, which took into account the non-uniform air distribution generated with the methods, was used to calculate the infection probability in an office room shared by ten occupants for 4 h. One of the occupants was an infector. The significance of the infector's location was studied. The obtained infection probability was compared to that obtained in the case of complete air mixing, which is uncommon in practice. Under specified conditions of this study, personalized ventilation (PV) performed the best in terms of preventing cross-infection, followed by displacement ventilation (DV), impinging jet ventilation (IJV), stratum ventilation (SV) and wall attachment ventilation (WAV). The number of infected occupants was reduced below the number obtained under the complete mixing assumption by using these air distribution methods. Mixing ventilation (MV) and diffuse ceiling ventilation (DCV) exhibited the worst performance. In comparison to the case of complete mixing the infection probability for seven out of nine susceptible occupants was higher with MV and for all occupants in the case of DCV. In SV, the position of the infector had a clear impact on the infection probability of susceptible individuals. WAV may perform better in practice if the system is well designed. The location of the exhaust outlets had a significant impact on the infection probability for DCV.

    Original languageEnglish
    Article number108555
    JournalBuilding and Environment
    Number of pages16
    Publication statusPublished - 2022


    • Air distribution
    • Cross-infection
    • Infection probability
    • Revised Wells-Riley model


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