Smart Ai Based Air Pollution Monitoring and Prediction System

Abstract

Air pollution has become a major environmental and public health concern due to rapid urbanization, industrialization, and increasing vehicle emissions. Continuous monitoring and accurate prediction of air quality are essential for minimizing health risks and supporting effective environmental management. This paper presents a Smart AI-Based Air Pollution Monitoring and Prediction System that integrates Internet of Things (IoT) and Artificial Intelligence (AI) technologies for real-time air quality monitoring and forecasting. The system collects environmental data, including CO, CO₂, PM2.5, PM10, temperature, and humidity, using IoT-enabled sensors. The collected data is transmitted to a centralized platform for storage and analysis. Machine learning algorithms process historical and real-time data to predict future Air Quality Index (AQI) values with improved accuracy. A userfriendly dashboard displays current air quality, historical trends, and predicted pollution levels through graphical visualizations. The system also generates alerts when pollution exceeds predefined safety limits, enabling timely preventive actions. By combining IoT-based sensing, cloud data management, and AI-driven predictive analytics, the proposed system provides an efficient, scalable, and intelligent solution for air quality monitoring. It supports proactive environmental management, enhances public awareness. KEYWORDS: Artificial Intelligence (AI), Internet of Things (IoT), Air Pollution Monitoring, Air Quality Index (AQI), Machine Learning, Environmental Monitoring, Predictive Analytics, Smart Sensors, Real-Time Monitoring, Pollution Prediction.