La rilevazione di cambiamenti in processi dinamici: un'applicazione ad un analizzatore di ozono

Abstract

The quality of data collected by air pollution monitoring networks is often affected by inaccuracies and missing data problems, mainly due to breakdowns and/or biases of the measurement instruments.In this paper we propose a statistical method to detect, as soon as possible, biases in the measurement devices, in order to improve the quality of collected data on line. The technique is based on the joint use of stochastic modeling and statistical process control algorithms. This methodology is applied to the mean hourly ozone concentrations recorded from one monitoring site of the Bologna urban area network. We set up the monitoring algorithm(through a simulation experiment) in such a way to detect anomalies in the data within 24 hours. The results show several out of control signals that may be caused by problems in the measurement device.