This study deals with ventilation effects on measured and perceived indoor air quality (IAQ) in a demonstrator building where IAQ problems can occur. Unlike outdoor air, indoor air is usually recycled continuously, which makes it trapping pollutants. Indoor air quality (IAQ) is characterized by a pollutants' concentration, as well as air temperature and humidity. The study's aim is to implement an efficient and smart ventilation system while leaning on continuous measurements of indoor air pollutants in a demonstrator building via a smart sensor based on a Raspberry Pi 3 model B+ card. Such a monitoring system measures atmospheric pollutants (CO2, CO, VOCs, formaldehyde, PM2.5 and benzene) and also comfort parameters (temperature and humidity). It is intended to locate the source influencing the IAQ and, thereby, it will certainly be very helpful to users and to obtain interactive cartography of IAQ. To achieve this, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. It has been developed through a series of measures. Measurements showed that the system is able to measure the air quality level and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, human activity, etc. It turned out that the CO2 level was higher during the occupation periods. Note that classrooms were excessively confined during occupancy periods by calculating the ICONE air containment index. In terms of hygrothermal comfort, the air was dry and very hot, especially in winter. However, the current ventilation system regulates the airflow according to the CO2-concentration only and does not consider the classrooms' hygrometry. This upsets occupants comfort and influences their productivity.