ACPD Atmospheric Chemistry and Physics Discussions ACPD 1680-7375 Copernicus GmbH Göttingen, Germany 10.5194/acpd-10-10925-2010 Multi-annual changes of NO<sub>x</sub> emissions in megacity regions: nonlinear trend analysis of satellite measurement based estimates Konovalov I. B. 1 2 Beekmann M. 2 Richter A. 3 Burrows J. P. 3 4 Hilboll A. 3 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 23 04 2010 10 4 10925 10968 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-discuss.net/10/10925/2010/acpd-10-10925-2010.html The full text article is available as a PDF file from http://www.atmos-chem-phys-discuss.net/10/10925/2010/acpd-10-10925-2010.pdf

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<sub>x</sub> 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<sub>x</sub> 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<sub>x</sub> 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. The presence of nonlinearities in NO<sub>x</sub> emission changes in Milan, Paris and Madrid is confirmed by comparison of simulated NO<sub>x</sub> concentrations with independent air quality monitoring data. A good quantitative agreement between the linear trends in the simulated and measured near surface NO<sub>x</sub> concentrations is found in London.

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