Atmospheric Chemistry and Physics Discussions www.atmos-chem-phys-discuss.net 1680-7367 1680-7375 10 4 2010 10.5194/acpd-10-10925-2010 http://www.atmos-chem-phys-discuss.net/10/10925/2010/ http://www.atmos-chem-phys-discuss.net/10/10925/2010/acpd-10-10925-2010.html http://www.atmos-chem-phys-discuss.net/10/10925/2010/acpd-10-10925-2010.pdf 10925 10968 2010-04-23 Multi-annual changes of NO_x emissions in megacity regions: nonlinear trend analysis of satellite measurement based estimates I. B. Konovalov konov@appl.sci-nnov.ru M. Beekmann A. Richter J. P. Burrows A. Hilboll Institute of Applied Physics, Russian Academy of Sciences, Nizhniy Novgorod, Russia Laboratoire Inter-Universitaire de Systèmes Atmosphériques, CNRS, UMR7583, Université Paris-Est and Université Paris 7, Créteil, France Institute of Environmental Physics and Remote Sensing, IUP/IFE, University of Bremen, Bremen, Germany Center for Ecology and Hydrology, Wallingford, UK Hazardous impact of air pollutant emissions from megacities on atmospheric composition on regional and global scales is currently an important issue in atmospheric research. However, the quantification of emissions and related effects is frequently a difficult task, especially in the case of developing countries, due to the lack of reliable data and information. This study examines possibilities to retrieve multi-annual NO_x emissions changes in megacity regions from satellite measurements of nitrogen dioxide and to quantify them in terms of linear and nonlinear trends. By combining the retrievals of the GOME and SCIAMACHY satellite instrument data with simulations performed by the CHIMERE chemistry transport model, we obtain the time series of NO_x emission estimates for the 12 largest urban agglomerations in Europe and the Middle East in the period from 1996 to 2008. We employ then a novel method allowing estimation of a nonlinear trend in a noisy time series of an observed variable. The method is based on the probabilistic approach and the use of artificial neural networks; it does not involve any quantitative a priori assumptions. As a result, statistically significant nonlinearities in the estimated NO_x emission trends are detected in 5 megacities (Bagdad, Madrid, Milan, Moscow and Paris). Statistically significant upward linear trends are detected in Istanbul and Tehran, while downward linear trends are revealed in Berlin, London and the Ruhr agglomeration. 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