AI-Powered Anomaly Detection in Air Pollution for Smart Environmental Monitoring

Air pollution is a growing concern due to its adverse effects on human health and the environment. Traditional air quality monitoring stations provide accurate data but are expensive to maintain and limited in coverage. This research explores an AI-based anomaly detection framework to enhance air quality assessment and support the development of virtual monitoring stations. The study utilizes four machine learning techniques—Z-score, Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks—to analyse pollution data. The Z-score method detects extreme pollution values by measuring statistical deviations, while Isolation Forest identifies outliers by isolating anomalies in the dataset. Autoencoders, a deep learning approach, learn typical pollution patterns and highlight deviations, and LSTM networks forecast air quality trends while identifying unexpected pollution spikes. By integrating these techniques, the proposed system improves pollution monitoring, allowing for real-time detection of anomalies and better forecasting of pollution levels. The findings suggest that AI-driven virtual monitoring stations can provide a scalable, cost-effective alternative to traditional sensor based systems. This approach has the potential to enhance environmental monitoring, support proactive pollution control measures, and contribute to data-driven policymaking for air quality management.