Air Pollution Monitoring and Prediction System using Internet of Things

Abstract

Air pollution has become a major environmental concern affecting human health and climate worldwide. To address this issue, this paper proposes an Air Pollution Monitoring and Prediction System based on the Internet of Things (IoT). The system employs smart sensors to measure key air quality parameters such as CO?, CO, NO?, SO?, and particulate matter (PM2.5 and PM10) in real time. The collected data is transmitted to a cloud platform via wireless communication modules for storage, visualization, and analysis. Machine learning algorithms are then applied to predict future pollution levels based on historical and environmental data trends. The system provides real-time alerts and air quality index (AQI) updates through web and mobile interfaces, enabling authorities and citizens to take timely actions. By integrating IoT with predictive analytics, the proposed framework offers a scalable, low-cost, and efficient solution for continuous environmental monitoring and sustainable urban management.