A Monte Carlo Assessment of the Effect of Different Ventilation Strategies to Mitigate the COVID-19 Contagion Risk in Educational Buildings

Abstract

The COVID-19 pandemic outbreak has increased the general awareness of the importance of proper ventilation in the indoor environment to reduce the contagion risk. In particular, attention has been paid to specific categories of buildings, such as schools, due to two factors: (1) high occupancy density and (2) the presence of young and sometimes more susceptible people. Despite the high level of alertness towards the ventilation of classrooms, robust analyses of the effectiveness of the different strategies to mitigate the contagion risk have been difficult to perform. Indeed, the COVID-19 pandemic is still ongoing, and many factors, such as the presence of multiple viral strains, use of facial masks, progression in vaccination, and installation of air purifiers and other sanitization devices, make it difficult to fully quantify the impact of room ventilation by simply analysing available monitoring data. Moreover, mitigation strategies related to ventilation are often dynamic, increasing the complexity of the problem to assess. In this framework, this work proposes a new Monte Carlo method integrated with building performance simulation to evaluate the number of infected occupants under different scenarios, considering also the dynamic boundary conditions. The described approach has been applied to a case study classroom at the Free University of Bozen-Bolzano, Italy, analysing almost 100 different scenarios and discussing the effectiveness of different ventilation strategies traditionally adopted to ensure suitable IAQ according to CO2 concentration limits. Results highlight the importance of combining different solutions (e.g., mixed-mode ventilation and facial masks) to limit the risk for both students and lecturers.