Smart AQI Prediction: A Scalable IoT- based Environmental Monitoring Framework for Urban Air Quality Management

Environmental degradation, particularly air pollution, poses significant challenges to public health and sustainable urban development. This paper presents a low-cost, scalable, and real-time IoT-based environmental monitoring system designed to track and analyze key air quality indicators such as PM 2.5, PM10, temperature, and humidity. Leveraging GP2Y1010AU0F and PM1025 particulate matter sensors integrated with Arduino Uno and cloud platforms like Thing Speak, the system collects, processes, and visualizes environmental data. Predictive modeling is applied to forecast Air Quality Index (AQI) fluctuations, enabling proactive urban management. This research demonstrates how embedded sensor network s, cloud analytics, and machine learning can synergize to deliver actionable insights and real-time alerts, significantly enhancing environmental governance.