Spatial representativeness of air quality monitoring stations: A grid model based approach

Abstract A methodology for quantifying areas of spatial representativeness of air quality monitoring station is here proposed, exploiting the wide spatial and temporal coverage of chemical transport models results. The method is based on the analysis of time series of model concentrations, extracted at monitoring sites and around, by means of a Concentration Similarity Function (CSF). The method was tested on AMS-MINNI model results, covering Italy and three reference years (2003, 2005, 2007), for assessing the spatial representativeness of PM2.5 and O 3 rural background monitoring stations. The CSF methodology shows good performances in describing both the extension and the shape of representativeness areas, taking into account the difference between pollutants and the dependence on averaging time and temporal interval of concentration data. Results show a large variability in the size and shape of the selected stations in Italy, ranging from 220 to 4500 km 2 . This confirms the importance of carrying out ad-hoc analyses on monitoring stations, as general a priori classifications and qualitative assessments of spatial representativeness are not able to fully capture the complexity of different territorial contexts.