Comparing Different Modeling Approaches in Obtaining Regional Scale Concentration Maps
We studied and compared different operational modeling techniques that are used to generate regional scale concentration maps for PM10, PM2,5, NO2 and O3 over Belgium. The various techniques and resulting maps were analyzed, validated and compared aiming at identifying the best possible regional scale concentration map for each pollutant. A distinction was made between a temporal and a spatial validation. The temporal analysis revealed that an intelligent interpolation technique based on land use characteristics in general performs best in capturing the temporal aspects of air quality in Belgium for the investigated pollutants. For PM10 and PM2.5 this technique also performs best in generating the spatial pattern of the observed annually averaged concentrations. A deterministic model combined with a corrective ‘Optimal Interpolation’ data assimilation technique performs best in reproducing the spatial pattern of O3. For NO2 the interpolation technique manages best in explaining the spatial pattern of the observed annually averaged concentrations in Belgium, but when restricted to the region of Flanders, it competes with a thoroughly calibrated Lagrangian type of modeling.
KeywordsBias Correction Ordinary Kriging Optimal Interpolation Orthogonal Regression Data Assimilation Technique
This research has been carried out in the framework of the Flanders Environmental Report and was financially supported by the Flemish Environmental Agency.
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