Effectiveness of HSE Procedures Based on IAQ Data to Reduce COVID-19 Contagion Risk Inside School Classrooms

During the peak of the pandemic, many countries closed schools to prevent the spread of SARS-CoV-2 among teaching staff and students. The late reopening has been managed according to some exceptional health safety and environment (HSE) procedures or protocols to mitigate infection risk rates. As a result, several schools installed IoT sensors that can be used to gauge Indoor Air Quality and the danger of COVID-19 transmission. For most health and safety procedures, stationary CO2 thresholds were considered as limits to ensure a low COVID-19 infection risk. This study aims to test the effectiveness of such static approach by evaluating the risk of infection inside a school building in Milan. The findings show that an alert and warning system based on the CO2 concentration may not suffice to cut out the infection risk, i.e., to reduce the virus concentration in the case of an infected person in a classroom. Moreover, although effective in reducing biological risk, excessive ventilation may cause discomfort and increase energy consumption above an acceptable limit in winter. In order to be effective, IoT data need to be accompanied by forecasting tools (powered by Artificial Intelligence) capable of anticipating peaks in pollutant concentration as in a Digital Twin approach.