A comparative analysis of two highly spatially resolved European atmospheric emission inventories

• Published in: Atmospheric environment (1994) 2013-08, Vol.75, p.43-57
• Main Authors: Ferreira, J., Guevara, M., Baldasano, J.M., Tchepel, O., Schaap, M., Miranda, A.I., Borrego, C.
• Format: Article
• Language: English
• Subjects: Air quality; Aire; Analysis methods; Applied sciences; Atmospheric pollution; Contaminació atmosfèrica; Degradació ambiental; Desenvolupament humà i sostenible; Disaggregation methods; Enginyeria ambiental; European emission inventories; Exact sciences and technology; Inter-comparative analysis; Mathematical models; Measurement; Mesurament; Models matemàtics; Pollution; Qualitat; Spatial variability; Tractament d'emissions i olors; Àrees temàtiques de la UPC
• ISSN: 1352-2310; 1873-2844
• DOI: 10.1016/j.atmosenv.2013.03.052

A reliable emissions inventory is highly important for air quality modelling applications, especially at regional or local scales, which require high resolutions. Consequently, higher resolution emission inventories have been developed that are suitable for regional air quality modelling. This research performs an inter-comparative analysis of different spatial disaggregation methodologies of atmospheric emission inventories. This study is based on two different European emission inventories with different spatial resolutions: 1) the EMEP (European Monitoring and Evaluation Programme) inventory and 2) an emission inventory developed by the TNO (Netherlands Organisation for Applied Scientific Research). These two emission inventories were converted into three distinct gridded emission datasets as follows: (i) the EMEP emission inventory was disaggregated by area (EMEParea) and (ii) following a more complex methodology (HERMES-DIS – High-Elective Resolution Modelling Emissions System – DISaggregation module) to understand and evaluate the influence of different disaggregation methods; and (iii) the TNO gridded emissions, which are based on different emission data sources and different disaggregation methods. A predefined common grid with a spatial resolution of 12 × 12 km2 was used to compare the three datasets spatially. The inter-comparative analysis was performed by source sector (SNAP – Selected Nomenclature for Air Pollution) with emission totals for selected pollutants. It included the computation of difference maps (to focus on the spatial variability of emission differences) and a linear regression analysis to calculate the coefficients of determination and to quantitatively measure differences. From the spatial analysis, greater differences were found for residential/commercial combustion (SNAP02), solvent use (SNAP06) and road transport (SNAP07). These findings were related to the different spatial disaggregation that was conducted by the TNO and HERMES-DIS for the first two sectors and to the distinct data sources that were used by the TNO and HERMES-DIS for road transport. Regarding the regression analysis, the greatest correlation occurred between the EMEParea and HERMES-DIS because the latter is derived from the first, which does not occur for the TNO emissions. The greatest correlations were encountered for agriculture NH3 emissions, due to the common use of the CORINE Land Cover database for disaggregation. The point source emissions (energy