Towards Smart Building: Visualization of Indoor CO2 Concentration. Adapting Modern Computational Tools for Informing Design Building Decisions

Abstract

Carbon dioxide (CO2) is part of the indoor air. According to the American Environmental Agency (EPA), one of the world's worst polluted places are indoor spaces where we spend more than 90% of our time (U.S.EPA, 1989). It has been shown that excessive CO2 indoor concentration can cause different health problems, including allergies, lung cancer, induced asthma, and respiratory infections like Covid-19 virus. Health problems often occur in poorly ventilated space that allows CO2 to increase beyond acceptable levels due to inefficient air circulation. To better understand the dynamics of this condition requires a fine grain model of how CO2 builds up and moves through space. In our study we developed a CO2 sensor network to record CO2 data and to visualize CO2 spread through a typical classroom to monitor air quality and to inform engineering and design building decisions to eliminate health risks. The CO2 sensor network is deployed inside 16 equally divided parts of the classroom. Each part is equipped with one sensor node for CO2 concentration monitoring. Collected data is visualized using modern computational tools and AI data-driven techniques. The results show that the increase in the quantity of classroom occupants as well as the time which they spend indoors directly impacts the level of CO2. Higher occupancy in the room triggers a higher value of CO2 concentration. Sensors in close proximity to people have higher CO2 readings. One of the data visualization charts shows readings from sensors installed in the different sections of the classroom. It visualizes approximately 5000 seconds of the readings done every second and shows the minimum and maximum indoor CO2 value. Another visualization is a three-dimensional model that spatially represents different CO2 concentrations in the equally divided parts of the classroom. Additionally, the airflow circulation analysis conducted in the classroom sheds light on how to adjust ventilation rates, to change the ventilation setup, or to adjust the building geometry. Personalized knowledge-based recommendation systems can be built to monitor indoor air quality inside the various classrooms at the university.

Keywords: CO2 Sensor Network, Air Quality, Data Visualisation, Airflow analysis

DOI: 10.54941/ahfe1004011