_Applied_  
_sciences_  
Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO\({}_{2}\) Prediction by Artificial Neural Networks  
**[PERSON] 1,*\(\copyright\) and [PERSON] 2**  
1 Department of Engineering, University of Perugia, Via [PERSON] 67, 06125 Perugia, Italy  
2 ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development of Engineering, Via Anguillarese, 301, 00123 Rome, Italy; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +39-075-585-3993 check for updates  
Received: 10 February 2020; Accepted: 27 February 2020; Published: 3 March 2020  
The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO\({}_{2}\) monitoring within the environment; however, other methods can be found in the literature, but they have not always led to satisfactory results. In this context, the present paper is focused on two main topics: the effect of the windows airtightness and of the environmental conditions on MAA and the application of artificial neural network (ANN) for the CO\({}_{2}\) prediction within the room. Two case studies (case study 1 located in Temi and case study 2 located in Perugia) were investigated, which differ in geometric dimensions (useful area, volume, window area) and in airtightness of windows. The indoor and outdoor environmental conditions (air temperature, pressure, relative humidity, air velocity, and indoor CO\({}_{2}\) concentration) were monitored in 33 experimental campaigns, in four room configurations: open door-open window (OD-OW); closed door-open window (CD-OW); open door-closed window (OD-CW); closed door-closed window (CD-CW). Tracer decay methodology, according to ISO 16000-8:2007 standard, was compiled during all the experimental campaigns. A feedforward ANN, able to simulate the indoor CO\({}_{2}\) concentration within the rooms, was then implemented; the monitored environmental conditions (air temperature, pressure, relative humidity, and air velocity), the geometric dimensions (useful area, volume, window area), and the airtightness of windows were provided as input data, while the CO\({}_{2}\) concentration was used as target. In particular, data of 19 experimental campaigns were provided for the training process of the network, while 14 were only used for testing the reliability of ANN. The CO\({}_{2}\) concentration predicted by ANN was then used for the MAA calculation in the four room configurations. Experimental results show that MAA of case study 2 is always higher, in all the examined configurations, due to the higher airtightness characteristics of the window and to the higher volume of the room. When the difference between indoor and outdoor temperature increases, the MAA increases too, in almost all the investigated configurations. Finally, the CO\({}_{2}\) concentration predicted by ANN was compared with experimental data; results show a good accuracy of the network both in CO\({}_{2}\) prediction and in the MAA calculation. The predicted CO\({}_{2}\) concentration at the beginning of experimental campaigns (time step 0) always differs less than 2% from experimental data, while a mean percentage difference of \(-\)18.8% was found considering the maximum CO\({}_{2}\) concentration. The MAA calculated using the predicted CO\({}_{2}\) of ANN was greater than the one obtained from experimental data, with a difference in the 0.5-1.3 min range, depending on the configuration. According to the results, the developed ANN can be considered an alternative and valuable tool for a preliminary evaluation of MAA.  
mean age of air; environmental conditions effect; windows air tightness; artificial neural network; CO\({}_{2}\) prediction and MAA calculation