IoT-Driven Air Quality Monitoring and Predictive Analysis with Explainable AI

Air pollution is a critical concern in urban environments due to its impact on public health and quality of life. A real-time air quality monitoring and prediction model is introduced that integrates Internet of Things (IoT) sensors with Machine Learning (ML) models to detect and predict pollutant concentrations. The system collects data on nitrogen dioxide (NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>), sulfur dioxide (SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>), carbon monoxide (CO), and ozone (O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>) using MQ-7, MQ-136, MQ-9, and MQ-131 sensors, along with DHT11 for temperature and humidity. The data is preprocessed to train ML models, including Decision Tree, Random Forest, Gradient Boosting, and Support Vector Classifier. The Decision Tree model achieved the highest accuracy of 99.68% in predicting pollutant levels. Explainable AI (XAI) techniques, such as LIME, are employed to interpret prediction results enhancing model transparency and building user trust. A web-based dashboard visualizes real-time data and prediction outputs, enabling policymakers, environmental agencies, and the public to make informed decisions. The proposed system offers a scalable and interpretable solution for proactive urban air quality management.