Screening Tools for Data Quality and Outlier Detection Applied to the Airbase Ambient Air Pollution Database

Systematic collection of long term mesoto 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 a consolidated 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 spatiotemporal relationships. The method is demonstrated with application examples of warnings on abnormal values in time series of PM10 datasets reported in the European air quality database AirBase.