This research focuses on developing a low-cost, real-time monitoring system integrated with a Building Information Modeling (BIM) ecosystem to improve indoor air quality and comfort in polluted urban environments. The system uses a 3D parametric BIM model, sensor arrays, and microprocessors, communicating via the Arduino Cloud. It collects environmental data, including temperature, humidity, pressure, illumination, and carbon dioxide levels. When CO2 concentrations exceed comfort thresholds, the system assesses external pollution levels using data from the IQAir database before opening windows. A servomotor device controls window openings, adapting the duration based on pollution and CO2 data to minimise indoor pollutants.<br />The procedure involves two key processes: detailed 3D scanning using the Leica BLK2GO laser scanner and real-time environmental monitoring via the Arduino platform. The integration of BIM, Python code, and Dynamo enables the system to adapt the building's ventilation in response to fluctuating conditions. Data collected from this system is compared with factory-calibrated sensors like AirThings Wave Plus, showing similar results over a 7-day period. This project is part of the MUSA-Multilayered Urban Sustainability Action and aims to further explore natural methods for CO2 absorption within indoor environments.