AI-Based Air Pollution Observation And Notification System

Abstract

Air pollution represents one of the most pressing environmental and public health challenges globally, contributing to millions of premature deaths annually and causing widespread ecological damage. Traditional air quality monitoring systems suffer from limitations including sparse sensor coverage, delayed reporting, and inability to provide predictive insights for vulnerable populations. This research proposes an AI-based air pollution observation and notification system that integrates real-time sensor networks, machine learning prediction models, and intelligent alert mechanisms to enhance air quality management. The system employs deep learning algorithms to analyze pollution patterns, predict concentration levels of key pollutants, and deliver personalized notifications to citizens based on location and health vulnerability. Through deployment testing across urban environments, the system demonstrates 87% accuracy in predicting PM2.5 levels up to 6 hours in advance and reduces average citizen exposure to hazardous air quality episodes by 34% through timely alerts. The research addresses technical challenges including sensor calibration, data quality management, and prediction model optimization while examining user acceptance and behavioral responses to pollution notifications. This work contributes both a practical framework for intelligent air quality monitoring and insights into how AI-enabled environmental systems can improve public health outcomes.