Published April 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

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

Authors/Creators

  • 1. Researcher, Department of CSAI, NSUT, New Delhi, India.

Description

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].

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Additional details

Identifiers

DOI
10.54105/ijainn.B1098.05030425
EISSN
2582-7626

Dates

Accepted
2025-04-15
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.

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