The complexity of ozone (O3) formation mechanisms in the troposphere make the fast and accurate modeling of ozone very challenging. In the absence of a process model, principal component analysis (PCA) has been extensively used as a data-based monitoring technique for highly correlated process variables; however conventional PCA-based detection indices often fail to detect small or moderate anomalies. In this work, we propose an innovative method for detecting small anomalies in highly correlated multivariate data. The developed method combine the multivariate exponentially weighted moving average (MEWMA) monitoring scheme with PCA modelling in order to enhance anomaly detection performance. Such a choice is mainly motivated by the greater ability of the MEWMA monitoring scheme to detect small changes in the process mean. The proposed PCA-based MEWMA monitoring scheme is successfully applied to ozone measurements data collected from Upper Normandy region, France, via the network of air quality monitoring stations. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.
ASJC Scopus subject areas
- Environmental Chemistry
- Chemical Engineering(all)
- Environmental Engineering
- Safety, Risk, Reliability and Quality