## 3 Results
### Ventilation Rate
#### 3.1.2 Daily Measurements  
Daily measurements during normal operation were fitted in the same way as controlled test data, but the upper restriction to \(\mathrm{C_{eq}}\) was set at 3000 ppm, which is well above the maximum concentration registered. It should be noticed that this restriction will be mainly relevant when \(Q\) is very small and the solution for Equation (2) degenerates into a linear function. In these cases, allowing \(\mathrm{C_{eq}}\) to reach higher values reduces the adjusted ACH value. It was observed that setting a restriction for \(\mathrm{C_{eq}}\) above 3000 ppm produced insignificant changes in ACH (e.g., the cases bounded by this upper restriction changed their adjusted ACH from 0.1 h\({}^{-1}\) to 0.05 h\({}^{-1}\)). All measurement series at the front of the classroom were sectioned in adjustment windows according to what was described in Section 2.2. An example is shown in Figure 4. With this method, around 50% of the measurements performed during classroom occupation were adjusted with a box model. A first analysis of the adjusted box models showed that a significant proportion of the adjustment windows during occupation hours had high ventilation rates, with median values of 6.6 h\({}^{-1}\) and 6.4 h\({}^{-1}\) for the morning and afternoon shifts, respectively. These values comply with both the ASHRAE 62.1 standard and WHO's recommendation. Nonetheless,  
Figure 4: Example of the sectioning into adjustment windows for a particular day. The grey curve corresponds to the \(\mathrm{CO_{2}}\) concentrations at the front of the classroom with monitor AQ1. Orange and blue areas identify adjustment windows with build-up and decay of \(\mathrm{CO_{2}}\), respectively. Orange and blue curves depict the adjusted box model for each window. The grey shaded background identifies occupation periods for the classroom. Figure 3: Example of results for controlled tests. (a) depicts test 1, with unilateral ventilation. (b) depicts test 2, with crossed ventilation. Each colour identifies a monitor at a specific measurement position. The jagged curves correspond to experimental data and the smooth curves represent the adjusted box model. they can be misleading if not carefully interpreted. In particular, it is important to notice that adjustment windows are of variable duration. Consequently, these results could not be quite representative of what usually happens in the classroom. For example, adjustment windows of short duration with high ACH results could inflate results. This is, in fact, what was observed: there is a correlation between ACH and the adjustment windows' duration, with longer duration corresponding to lower ventilation rates and vice versa. Figure 5 illustrates these results for the afternoon shift. Similar results were obtained for the morning shift. In order to compensate for the variable duration of adjustment windows, a daily temporally weighted ACH average was computed. This average is defined for each shift of each measurement day as follows:  
\[\overline{\text{ACH}}_{\text{p}}=\frac{\sum_{i=1}^{v}\text{ACH}_{i}\Delta t_{i} }{\sum_{i=1}^{v}\Delta t_{i}} \tag{14}\]  
where \(v\) is the number of adjusted windows and \(\text{ACH}_{i}\) and \(\Delta t_{i}\) are the adjusted ventilation rate and the duration of the \(i\)-th window. Figure 6 shows the monthly weighted average results and compares them with the distribution directly computed from the adjustment results. The contrast is striking and clearly illustrates that while the classroom is capable of reaching very high ventilation rates, as it was also shown by the controlled tests, during normal operation this happens only for reduced periods. This is probably related to thermal comfort restrictions. With such high ventilation rates and air coming from outside, it would be almost impossible to maintain a comfortable temperature inside the classroom. Consequently, occupants only ventilate the classroom for short periods.