Estimation of Occupancy Using IoT Sensors and a Carbon Dioxide-Based Machine Learning Model with Ventilation System and Differential Pressure Data.

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.

The Wavelength-Based Inactivation Effects of a Light-Emitting Diode Module on Indoor Microorganisms.

With the increased incidence of infectious disease outbreaks in recent years such as the COVID-19 pandemic, related research is being conducted on the need to prevent their spread; it is also necessary to develop more general physical-chemical control methods to manage them. Consequently, research has been carried out on light-emitting diodes (LEDs) as an effective means of light sterilization. In this study, the sterilization effects on four types of representative bacteria and mold that occur indoors, Bacillus subtilis, Escherichia coli, Penicillium chrysogenum, and Cladosporium cladosporidides, were confirmed using LED modules (with wavelengths of 275, 370, 385, and 405 nm). Additionally, power consumption was compared by calculating the time required for 99.9% sterilization of each microorganism. The results showed that the sterilization effect was high, in the order 275, 370, 385, and 405 nm. The sterilization effects at 385 and 405 nm were observed to be similar. Furthermore, when comparing the power consumption required for 99.9% sterilization of each microorganism, the 275 nm LED module required significantly less power than those of other wavelengths. However, at 405 nm, the power consumption required for 99.9% sterilization was less than that at 370 nm; that is, it was more efficient and similar to or less than that at 385 nm. Additionally, because 405 nm can be applied as general lighting, it was considered to have wider applicability and utility compared with UV wavelengths. Consequently, it should be possible to respond to infectious diseases in the environment using LEDs with visible light wavelengths.