The EuroCC2 team collaborated with the company EMBIO Diagnostics in advancing air quality interpretability, monitoring and analysis. The goal of the project was to consult EMBIO Diagnostics in developing a comprehensive solution encompassing a web-based visualization tool for floorplan annotation, advanced measurement prediction techniques, and innovative anomaly detection methods in air quality data.
Using machine learning methods, we could successfully identify unusual patterns in air quality data, thus improving the overall reliability of the monitoring system and providing a reporting toolkit.
The outcomes of the project enabled Embio Diagnostics to make predictions in previously unmonitored locations, playing a crucial role in the strategic placement of sensors. By identifying areas with high prediction uncertainties, it allowed for a more informed deployment of sensors, thus enhancing the overall effectiveness of the air quality monitoring system.
