Real-time assessment of the ambient air quality has gained an increased interest in recent years. To give support to this
evolution, the statistical air pollution interpolation model RIO is developed. Due to the very low computational cost this
interpolation model is an efficient tool for an environment agency when performing real-time air quality assessments. Beside this, a
reliable interpolation model can be used to produce analysed maps of historical data records as well. RIO is an interpolation model
that can be classified as a detrended Kriging model. In a first step the local character of the air pollution sampling values is removed
in a detrending procedure. Subsequently, the site-independent data is interpolated by an Ordinary Kriging scheme. Finally, in a retrending
step a local bias is added to the Kriging interpolation results. As spatially resolved driving force in the detrending process, a
land use indicator is developed based on the CORINE land cover data set. The indicator is optimized independently for the three
pollutants O3, NO2 and PM10. As a result, the RIO model is able to account for the local character of the air pollution phenomenon at
locations where no monitoring stations are available. Through a cross-validation procedure the superiority of the RIO model over
standard interpolation techniques, such as the Ordinary Kriging is demonstrated. Air quality maps are presented for the three
pollutants mentioned and compared to maps based on standard interpolation techniques
