A Study on AI-Based Approaches for Air Quality Analysis and Anomaly Prediction in Smart Environmental Monitoring

AbstractThe degradation of air quality poses significant challenges to environmental sustainability, publichealth, and economic development, particularly in rapidly urbanizing regions. This projectpresents a comprehensive framework for Air Quality Assessment and Anomaly Detection(AQAD) using intelligent approaches to mitigate the adverse effects of air pollution and promotesustainable environmental practices. The proposed AQAD system integrates advanced sensortechnology, data analytics, and machine learning algorithms to monitor, evaluate, and detectanomalies in air quality parameters such as particulate matter (PM), nitrogen dioxide (NO2),sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO).To facilitate accurate assessment and anomaly detection, the AQAD system employs acombination of statistical analysis, pattern recognition, and machine learning techniques,including time-series analysis, clustering algorithms, and anomaly detection models such asIsolation Forest, One-Class Support Vector Machine (SVM), and Neural Networks. Theseintelligent approaches enable the system to distinguish between normal variations in air qualityand abnormal events such as pollutant spikes, emissions anomalies, and environmental incidents.