Statistical detection of abnormal ozone measurements based on Constrained Generalized Likelihood Ratio test
IEEE 52nd Annual Conference on Decision and Control (CDC)
Monitoring ozone concentrations levels is an essential requirement due to the adverse environmental and health effects of abnormal ozone pollution. The objective of this paper is twofold: first, to model ground level ozone concentrations, and second, to detect abnormal ozone measurements. Towards this end, a seasonal autoregressive moving average (SARMA) multidimensional models with nuisance parameters has been developed to describe ground level ozone concentrations. The database used to fit the models consists of two data sets collected from Upper Normandy region, France, via the network of air quality monitoring stations. A good description of the ambient ozone pollution may be a tool for facilitating detection of abnormalities in ozone measurements. The overarching goal of this paper is to detect abnormal pollution measurements caused by air pollution anomalies or malfunctioning sensors in the framework of regional ozone surveillance network. The proposed constrained generalized likelihood ratio (CGLR) anomaly detection scheme is successfully applied to collected data. The detection results of the proposed method are compared to that declared by Air Normand air monitoring association.