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A negative pressure isolation ward prevents the outflow of airborne microorganisms from inside the ward, minimizing the spread of airborne contamination causing respiratory infection. In response to recent outbreaks of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), Korea has increased the number of these facilities. However, airborne contaminants that flow into the ward from adjacent areas may cause secondary harm to patients. In this study, the sterilization effect of upper-room ultraviolet germicidal irradiation (UR-UVGI) on microorganisms generated within the negative pressure isolation ward and those flowing inward from adjacent areas was evaluated through field experiments and computational fluid dynamics (CFD) analysis, to assess the potential of this approach as a supplementary measure to control such microorganisms. The sterilization effect was found to be not high because of high-level ventilation. CFD analysis under various conditions shows that the sterilization effect for indoor-generated microorganisms varies with the level of UV radiation, the source locations of the indoor-generated microorganisms, air supplies and exhausts, the UVGI system, and the airflow formed under the specified conditions. Our results show that when the UVGI system is installed in the upper part of the ward entrance, contaminated air from adjacent area is strongly sterilized.

Infectious diseases such as the COVID-19 pandemic have necessitated preventive measures against the spread of indoor infections. There has been increasing interest in indoor air quality (IAQ) management. Air quality can be managed simply by alleviating the source of infection or pollution, but the person within a space can be the source of infection or pollution, thus necessitating an estimation of the exact number of people occupying the space. Generally, management plans for mitigating the spread of infections and maintaining the IAQ, such as ventilation, are based on the number of people occupying the space. In this study, carbon dioxide (CO2)-based machine learning was used to estimate the number of people occupying a space. For machine learning, the CO2 concentration, ventilation system operation status, and indoor–outdoor and indoor–corridor differential pressure data were used. In the random forest (RF) and artificial neural network (ANN) models, where the CO2 concentration and ventilation system operation modes were input, the accuracy was highest at 0.9102 and 0.9180, respectively. When the CO2 concentration and differential pressure data were included, the accuracy was lowest at 0.8916 and 0.8936, respectively. Future differential pressure data will be associated with the change in the CO2 concentration to increase the accuracy of occupancy estimation.