AI-Powered Anomaly Detection in Air Pollution for Smart Environmental Monitoring
Raghav Abrol
Raghav Abrol, Researcher, Department of CSAI, NSUT, New Delhi, India.
Manuscript received on 30 March 2025 | First Revised Manuscript received on 08 April 2025 | Second Revised Manuscript received on 12 April 2025 | Manuscript Accepted on 15 April 2025 | Manuscript published on 30 April 2025 | PP: 1-5 | Volume-5 Issue-3, April 2025 | Retrieval Number: 100.1/ijainn.C109805030425 | DOI: 10.54105/ijainn.C1098.05030425
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Air pollution is a growing concern due to its adverse effects on human health and the environment [1]. Traditional air quality monitoring stations provide accurate data but are expensive to maintain and limited in coverage [2]. This research explores an AI-based anomaly detection framework to enhance air quality assessment and support the development of virtual monitoring stations [3]. The study utilizes four machine learning techniques—Z-score, Isolation Forest, Autoencoders, and Long Short-Term Memory (LSTM) networks—to analyse pollution data [4]. The Z-score method detects extreme pollution values by measuring statistical deviations [5], while Isolation Forest identifies outliers by isolating anomalies in the dataset [6]. Autoencoders, a deep learning approach, learn typical pollution patterns and highlight deviations [7], and LSTM networks forecast air quality trends while identifying unexpected pollution spikes [8]. By integrating these techniques, the proposed system improves pollution monitoring, allowing for real-time detection of anomalies and better forecasting of pollution levels [9]. The findings suggest that AI-driven virtual monitoring stations can provide a scalable, cost-effective alternative to traditional sensorbased systems [10]. This approach has the potential to enhance environmental monitoring, support proactive pollution control measures, and contribute to data-driven policymaking for air quality management [11].
Keywords: LSTM, Isolation Forest, Carbon Monoxide, Long Short-Term Memory.
Scope of the Article: AI Applications