First evaluation of a novel screening tool for outlier detection in large scale ambient air quality datasets

Systematic collection of long term meso- to large-scale datasets of ambient air quality provides an indispensible means for air pollution monitoring. However, the quality of these monitoring data depends on the chosen method of measurements and the QA/QC procedures applied. We present the first version of a prototyped screening tool for the automatic detection of outliers in large data volume air quality monitoring records. The method is based on an adaption of the existing “Smooth Spatial Attribute Method”, which considers both attribute values and spatio-temporal relationships. An application example of the method is demonstrated by computing warnings on abnormal records in the 2006 / 2007 time series of PM10 daily values reported in the European air quality database AirBase.JRC.H.2-Air and Climat