Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Principal Component Analysis (PCA) for Particulate Matter (PM) Anomaly Detection

Abstract

This research addresses a critical issue in industrial environments: air quality, specifically regarding PM 1.0 and PM 2.5. High concentrations of these particles pose significant health risks. The study measures temperature, humidity, pressure, altitude, PM 1.0, and PM 2.5 and shows the effectiveness of using AIOT-Particle devices to analyze these features with Principal Component Analysis (PCA). Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to detect anomalies during the observation period. Anomalies occur when the altitude ranges from 65 to 70 units, according to PM 1.0 and PM 2.5 values. The positions where anomalies occur are illustrated based on altitude, temperature, pressure, and concentration. The results demonstrate that altitude dominates as the first feature. Finally, the research concludes that altitude, PM 1.0, and PM 2.5 are the dominant features. The study confirms the effectiveness of PCA and recommends using these three features for anomaly detection in DBSCAN. Overall, the research highlights the novelty and success of AIOT-Particle in industrial environments.