Improving Air Quality Data Analysis by Injecting and Detecting Contextual Anomalies

In this study, the critical issue of air pollution and its impact on quality of life is addressed by developing technologies for monitoring air quality and identifying areas of concern. A notable challenge in this domain is the collection of air pollution data, which often lacks differentiation between normal and abnormal quality levels. Recognizing the importance of detecting anomalies in air pollution data, a novel methodology is introduced that not only safeguards human health but also enhances the overall data quality. The proposed approach involves the injection of abnormal events into air pollution datasets by leveraging the temporal distribution characteristics of the data. To assess the validity of these generated anomalies, the Kolmogorov-Smirnoff test is employed. Furthermore, the developed method is evaluated by exploiting SoA deep learning based models for anomalies detection. Finally, a deep attention-based autencoder method is designed, namely AT-MCRAAD that demonstrates superior performance over existing traditional and contemporary algorithms in identifying these anomalous events.