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Volume 10, issue 22
Atmos. Chem. Phys., 10, 10849-10865, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: EMEP – an integrated system of models and observations...

Atmos. Chem. Phys., 10, 10849-10865, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 18 Nov 2010

Research article | 18 Nov 2010

A refinement of the emission data for Kola Peninsula based on inverse dispersion modelling

M. Prank1, M. Sofiev1, H. A. C. Denier van der Gon2, M. Kaasik3, T. M. Ruuskanen4, and J. Kukkonen1 M. Prank et al.
  • 1Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
  • 2TNO, Princetonlaan 6, 3584 CB Utrecht, The Netherlands
  • 3University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
  • 4University of Helsinki, Department of Physics, P.O. Box 68, 00014 Helsinki, Finland

Abstract. The study reviews the emission estimates of sulphur oxides (SOx) and primary particulate matter (PM) from the major industrial sources of Kola Peninsula. Analysis of the disagreements between the existing emission inventories for the Kola region combined with forward and inverse ensemble dispersion modelling, analysis of observation time-series and model-measurement comparison showed that the emission of the Nikel metallurgy plant was missing or strongly under-estimated in the major European emission inventories, such as EMEP, EDGAR, TNO-GEMS, and PAREST-MEGAPOLI. In some cases it was misplaced or mis-attributed to other sources of the region. A more consistent inventory of the anthropogenic emissions of SOx and PM has been compiled for the Peninsula, compared with the existing estimates and verified by means of dispersion modelling. In particular, the SILAM model simulations for 2003 and 2006 with the revised emission data showed much smaller under-estimation of SO2 concentrations at 8 Finnish and Norwegian observational stations. For the nearest site to the plant the 10-fold underestimation turned to a 1.5-fold over-prediction. Temporal correlation improved more moderately (up to 45% for concentrations, up to 3 times for deposition). The study demonstrates the value of a combined usage of forward and inverse ensemble modelling for source apportionment in case of limited observational data.

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