## 3 Results
### Discussion
Nonetheless, it was observed that sufficient ventilation mostly occurs during short time windows. Consequently, the weighted averaged ventilation rate is below the reference values specified in ASHRAE's standard [30] and by WHO [31]. Furthermore, the risk of airborne infection for the CO\({}_{2}\) measurement months was computed in a similar way than the one presented in [12; 15]. The results are lower than the ones obtained by using the Gammaitoni-Nucci model for a ventilation rate close to the weighted averaged previously mentioned. This could be related to the effect of intermittent ventilation. To assess this hypothesis, artificial quanta curves were obtained for different dynamic (intermittent) ventilation strategies and the infection risk was computed via the Gammaitoni-Nucci model. The results show that a ventilation scheme comprising intervals of low and high ventilation rates can drastically reduce the airborne transmission risk and is therefore encouraged. This is in line with the manual airing of naturally ventilated classrooms proposed in [44]. Another dynamic procedure for risk reduction is presented in [45], although this study focuses on controlling the pathogen source in buildings with mechanical ventilation rather than the ventilation rate. The intermittent ventilation of naturally ventilated classrooms is probably mostly needed during the cold season, when high ventilation rates can preclude thermal comfort and contagion risk is therefore higher. This is supported by the results of the measurement campaign that were presented, which exhibit seasonal trends similar to the ones observed in [12; 17]. A limitation of the present study is that the methods used for both computing the ventilation rate and the airborne transmission risk rely on the perfect mix hypothesis. This is a common approach in the research community, as it has been previously mentioned. In this work, a first assessment of the hypothesis has been done. The results show, particularly regarding the airborne transmission risk, that it is a reasonable assumption. Furthermore, the family of Wells-Riley models has been shown to provide reasonable and informative results when compared with methods that do not rely on the perfect mix hypothesis, like the ones based on computational fluid dynamics simulations [46; 47]. Furthermore, the Rudnick-Milton approach poses an additional limitation to the results. As it has been mentioned, said model relies on using CO\({}_{2}\) as a proxy for the studied pathogen. Nonetheless, in reality, the SARS-CoV-2 virus is transmitted via small droplets of fluid. The movement and dispersion of these droplets is more complex than that of gaseous CO\({}_{2}\) and is highly dependent on the droplet size, which in turn depends on environmental conditions, such as temperature and humidity [48]. This limitation is inherent to the Rudnick-Milton model. Nonetheless, the authors wish to highlight that it remains useful model for risk estimation, particularly for naturally-ventilated environments, as it avoids the problem of explicitly estimating the ventilation rate. In light of the above-mentioned aspects, it can be concluded that, despite some limitations, the Wells-Riley models are useful tools to quickly estimate the advantages and disadvantages of different ventilation and risk reduction strategies.