Spatial outlier detection in the PM10 monitoring network of Normandy (France)

Abstract We consider hourly PM 10 measurements from 22 monitoring stations located in Basse–Normandie and Haute–Normandie regions (France) and also in the neighboring regions. All considered monitoring stations are either urban background stations or rural ones. The paper focuses on the statistical detection of outliers of the hourly PM 10 concentrations from a spatial point of view. The general strategy uses a jackknife type approach and is based on the comparison of the actual measurement with some robust spatial prediction. Two spatial predictions are considered: the first one is based on the median of the concentrations of the closest neighboring stations which directly consider weighted concentrations while the second one is based on kriging increments, instead of more traditional pseudo–innovations. The two methods are applied to the PM 10 monitoring network in Normandy and are fully implemented by Air Normand (the official association for air quality monitoring in Haute–Normandie) in the Measurements Quality Control process. Some numerical results are provided on recent data from January 1, 2013 to May 31, 2013 to illustrate and compare the two methods.