ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-10-10849-2010 A refinement of the emission data for Kola Peninsula based on inverse dispersion modelling Prank M. 1 Sofiev M. 1 Denier van der Gon H. A. C. 2 Kaasik M. 3 Ruuskanen T. M. 4 Kukkonen J. 1 Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland TNO, Princetonlaan 6, 3584 CB Utrecht, The Netherlands University of Tartu, Ülikooli 18, 50090 Tartu, Estonia University of Helsinki, Department of Physics, P.O. Box 68, 00014 Helsinki, Finland 18 11 2010 10 22 10849 10865 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/10/10849/2010/acp-10-10849-2010.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/10/10849/2010/acp-10-10849-2010.pdf

The study reviews the emission estimates of sulphur oxides (SO<sub>x</sub>) 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 SO<sub>x</sub> 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 SO<sub>2</sub> 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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