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

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...

Full description

Bibliographic Details
Main Authors: Prank, M., Sofiev, M., Denier Van Der Gon, H.A.C., Kaasik, M., Ruuskanen, T.M., Kukkonen, J.
Format: Article in Journal/Newspaper
Language:English
Published: 2010
Subjects:
Online Access:http://resolver.tudelft.nl/uuid:2d51e109-552a-4a9f-b146-7c0f7fe02502
Description
Summary: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. © 2010 Author(s).