Iot-Based Real-Time Air Quality Index Monitoring And Predictive Modelling

Abstract

This research presents the design and implementation of an IoT-based system for real-time air quality monitoring and predictive modeling. The increasing concern over air pollution necessitates the development of efficient, scalable solutions for continuous monitoring of air quality parameters such as particulate matter (PM), carbon dioxide (CO2), and other pollutants. The proposed system utilizes low-cost IoT sensors, coupled with cloud-based data processing, to collect and transmit real-time air quality data. A predictive model, based on machine learning algorithms, is applied to forecast air quality trends and provide early warnings of potential pollution events. The system's effectiveness is demonstrated through a prototype, showcasing its ability to track air quality in real time and predict future air quality index (AQI) levels. The results highlight the system’s potential to assist in proactive air quality management and inform public health interventions. Challenges such as sensor calibration, data accuracy, and predictive model optimization are also discussed.