A Tool for the spatio-temporal screening of AirBase Datasets for abnormal values

In the Air Quality Database named AirBase, measurements of ambient air pollution are collected at more than 6000 monitoring stations from over 30 countries. The quality of these data depends on the chosen method of measurements and QA/QC procedures applied by each country. We present a novel methodology to automatically screen the AirBase records for internal consistency and to detect spatio-temporal outliers nested in the data. We implemented a spatio-temporal toolset for screening abnormal values which considers both attribute values and spatial relationships. The method relies on the definition of a neighbourhood for each air measurement, corresponding to a spatio-temporal domain limited in time and distance. It is assumed that within a given spatio-temporal domain in which the attribute values of neighbours have a relationship due to the emission, transport and reaction of air pollutants, abnormal values can be detected by extreme values of their attributes compared to the attribute values of their neighbours. The implemented method can be of interest as the basis of a data quality screening system when countries report their measurements to the European Environment Agency.