Machine Learning-Based Air Quality Monitoring Using IoT Sensors

Abstract

Air pollution is a significant global concern, and machine learning is helping analyze real-time data from IoT-enabled air quality sensors. This paper explores ML techniques such as clustering, deep learning, and time-series forecasting for air pollution prediction and early warning systems. It discusses challenges like sensor calibration, data inconsistency, and computational limitations. Case studies highlight successful ML-driven air quality monitoring implementations, demonstrating their effectiveness in pollution control, public health improvement, and environmental sustainability.