Abstract:We analyse the temporal variations which can be observed within time series of variogram parameters (nugget, sill and range) of daily air quality data (PM 10 ) over a ten years time frame. Datasets have been obtained from previous geostatistical analysis of country wide datasets from the AirBase ambient air quality database. Applying the Kolmogorov-Zurbenko filtering method, the time series are first decomposed into their short-, mid-, and longterm components. Based on this, we then evaluate the magnitude of the individual spectral signal contributions. Furthermore, the significance of a long term trend component is investigated by a block-bootstrap-based approach combined with linear regression. It is discussed if within these datasets the times series of nugget variance can provide information about the evolution of the measurement uncertainty of the related air pollutant, whereas the sill and the range parameters could contain information about the spatial representativeness of the monitoring stations.