## 1 Introduction
It was furthermore applied to the air flow field investigation, MAA and CO\({}_{2}\) distribution, and air-change efficiency when inside a bedroom the heights of the supply air outlet is varied [23], or to particular building ventilation devices such as wind towers and wind-catchers [24; 25]. Different applications of similar CFD models, far from the aim of the present paper, were proposed in the chemical and biopharmaceutical fields [26; 27] or in the evaluation of the MAA in the outside city ventilation [28]. In natural ventilated buildings, the experimental approach is necessary in order to evaluate the indoor air quality performance; further useful information could be also obtained by questionnaire surveys [29; 30]. In a previous study [31], the authors investigated the wind-forced natural ventilation rates of an office both with an experimental and a numerical procedure, by using the CFD code Fluent, and MAA was calculated. Experimental data were used to validate the numerical method. The simulation results were in a good agreement with the experimental ones only for the lower values of the MAA, in a configuration with both door and window opened; for higher values (especially in the configurations with closed window), the numerical solution was not convergent, therefore an experimental approach is necessary. In these cases, the environmental parameters could have an important role in determining the air exchange performance of a room, such as reported in [32], where the influence of the supply air temperature on the mean local age of air was investigated considering a stratum ventilated office. In the literature, other different approaches are also studied and applied in the engineering field, which can be useful for this kind of investigation. Artificial neural networks (ANN), are the most used in prediction problems, i.e., when an output is calculated starting from known parameters [33; 34; 35; 36]. The authors have also studied the ANN in different kinds of applications, such as energy demand [37], thermal comfort [38], and indoor air temperature [39] prediction; in each one, the strengths and weakness of ANN were highlighted. According to these works, artificial neural networks could be a very useful tool for CO\({}_{2}\) concentration prediction within the room, starting from a few input parameters. It is worth noting that in all the papers, a feedforward network, and in particular, a fitting neural network, was always used for predicting the energy demand, the PMV (Predicted Mean Vote) index, or the air temperature. In these studies, the same pattern of ANN was used, i.e., one input, one hidden, and one output layer. Only in [36; 39], two hidden layers were needed for obtaining a good reliability of the ANN. According to the state of the art, in the present paper, two main topics are investigated: the effect of the windows airtightness and of the environmental conditions (such as temperature and pressure) on the ventilation rate and the application of ANN for the CO\({}_{2}\) prediction within the room. Both topics represent a significant novelty, because they are a first tentative to:  
1. Establish a correlation of the mean age of air (MAA) with the environmental conditions. As far as the authors' awareness, only a few data are available in the scientific literature about this topic;
2. Simulate the CO\({}_{2}\) concentration by using artificial neural network. No applications of ANNs in this field were found in the literature, therefore, it can lead an innovative contribution to the scientific literature. Two offices located in two cities of the Umbria region--central Italy were chosen as case studies. They mainly differ by geometric characteristics (volume differs for about 30%) and by the airtightness class of the window (considered as Class 1-2 and Class 4, respectively). Thirty-three experimental campaigns were overall carried out in different seasons, environmental conditions, and with different room configurations.