Real-Time Air Quality Monitoring: Integrating Firebase, Mobile Dashboard, and GRU Prediction

Building upon a portable air quality monitoring device, this project extends its capabilities by integrating real-time data upload and advanced predictive modelling. The ESP32S3 microcontroller is utilized not only for data logging onto an SD card but also for uploading data to Firebase in real time. A user-friendly Android app is developed to provide a dashboard for monitoring air quality metrics remotely. Additionally, a graphical user interface is implemented on the device’s TFT screen to allow on-device data visualization. The collected data is further leveraged to implement a Gated Recurrent Unit (GRU) model, enabling accurate air quality predictions. This paper demonstrates the successful integration of cloud-based data management, front-end user interaction, and machine learning within a cohesive air quality monitoring system.