## 2 Materials and Methods
### Relative Airborne Pathogen Risk Transmission  
Given that the analyzed spaces are continuously occupied by sensitive individuals (children aged 0 to 2 years), the relative risk of airborne disease transmission is assessed, using SARS-CoV-2 as a representative infectious pathogen. To this end, the methodology outlined by [PERSON] et al. [12] is applied, utilizing the _Covid Risk airborne_ tool ([[https://www.covidairbornerisk.com/](https://www.covidairbornerisk.com/)]([https://www.covidairbornerisk.com/](https://www.covidairbornerisk.com/)), accessed on 13 June 2024) developed by [PERSON] et al. [72]. This tool is based on the probabilistic model adapted from Wells-Riley [10; 27; 28; 29], which  
Figure 2: (**a**) Classrooms A and B’s floor plan; (**b**) Section of classrooms A and B. AWAIR Omni sensor (Await Inc., San Francisco, CA, USA) marked in blue. statistically predicts the airborne transmission of pathogens among individuals during a given event, transmitted via aerosols (both medium and long distances) [73]. This method employs the concept of a \"quantum\", defined as the \"dose of airborne droplet nuclei required to cause infection in 63% of susceptible occupants\" [27]. Thus, the risk of transmission during a given event depends on the number of quanta inhaled by susceptible individuals. Since the emission of infectious particles occurs simultaneously with the respiratory process, it is possible to correlate this emission with the monitoring of the average excess CO2 levels relative to outdoor concentrations. To estimate the average emission rate of infectious particles from a potentially infectious subject, the concept of the Quanta Emission Rate (_ERq_) is utilized. The emission rate established by [PERSON] et al. for SARS-CoV-2 [28; 74] is adopted, incorporating the Monte Carlo Method [27] with an enhancement factor of 3.3 for the \"Omicron\" BA2 variant relative to the original virus [75; 76]. Additionally, it is assumed that the infectious individual has an emission capacity at the 85 th percentile. Consequently, the basic quanta exhalation rate (_EP\({}_{0}\)_) is set at 18.6 q.h\({}^{-1}\). To assess the relative infection risk, three scenarios were analyzed based on varying levels of vocalization and metabolic activities by one-year-old occupants (Table 1). * Case 1: normal breathing, representing periods of minimal vocalization and relative quiet. * Case 2: regular speaking, simulating typical days when infants intermittently cry, babble, or laugh. * Case 3: loud speaking, representing scenarios with increased vocalization, which are less common given the age of the children. The relative risk of contagion is evaluated using the Attack Rate (AR) indicator, which is epidemiologically defined as the \"ratio of infection cases (C) to the total number of susceptible individuals (S) exposed to a quantifiable concentration of infectious quanta\", considering that n is the \"infectious dose inhaled by a susceptible person present in the premise during the event (quanta)\". This metric can also represent the individual infection risk (R) during a specific event, as expressed in Equation (1). \[AR=\frac{C}{S}=R\equiv 100\cdot(1-e^{n}) \tag{1}\]  
The calculation of \(n\), detailed extensively in [12; 77], is influenced by various factors. These include the Quanta Emission Rate (ERq), which varies depending on the infectious agent and specific characteristics of the infectious individual, such as age, metabolic activity, and vocalization intensity. Additional determinants are the event duration, the room volume, the infectious agent's airborne decay rate (affected by air velocity, \(\mathfrak{t}_{\text{a}}\), and RH), and the aerosol deposition rate on surfaces. Air-cleaning measures, such as filtration, UV light, ventilation efficiency, and potential mask usage, are also integral to this calculation.