Ai Based Real-Time Air Quality Monitoring System

Abstract

Air pollution has become one of the most critical environmental and public health issues globally, affecting quality of life and contributing to various health problems. With increasing urbanization and industrialization, the need for effective air quality monitoring has never been more urgent. This study presents a Real-Time Air Quality Monitoring System that delivers real-time air quality data, predictive analytics, and health recommendations. The system integrates continuous data collection from monitoring stations, a Bi-Directional Long Short-Term Memory (Bi-LSTM) model for air quality forecasting, and a visualization dashboard for data interpretation.

To enhance predictive accuracy, Particle Swarm Optimization (PSO) is used to optimize model performance, while a generative AI-based chatbot improves user interaction by offering personalized insights and preventive recommendations. Experimental results confirm the system’s effectiveness in forecasting pollution trends, identifying hotspots, and supporting informed decision-making for both individuals and policymakers. Additionally, an agent-based simulation module enables policymakers to evaluate the impact of interventions on air quality under various real-world scenarios. This research contributes to environmental monitoring and public health management, providing a scalable, intelligent approach to mitigating air pollution.