AQI-Based Air Pollution Exposure and Health Intelligence System

This research presented an intelligent Air Pollution Analysis and Health Impact Assessment System that integrates machine learning and data-driven techniques to provide meaningful environmental insights. By utilizing pollutant data such as PM2.5, PM10, NO₂, and SO₂, the system effectively estimates air quality levels and identifies major pollution sources using rule- based analysis. Unlike traditional air quality monitoring systems that only display AQI values, the proposed approach enhances interpretability by explaining causes and assessing health impacts. The system successfully combines AQI estimation, cause detection, health risk assessment, and personalized exposure modeling within a unified web-based interface. The inclusion of user-specific inputs and actionable recommendations improves user awareness and supports informed decision-making. The project demonstrates the practical application of machine learning and analytical methods in environmental monitoring, providing a strong foundation for future advancements in intelligent air quality and health advisory systems.

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