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Volume 6, issue 7
Atmos. Chem. Phys., 6, 1747–1770, 2006
https://doi.org/10.5194/acp-6-1747-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Atmos. Chem. Phys., 6, 1747–1770, 2006
https://doi.org/10.5194/acp-6-1747-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  24 May 2006

24 May 2006

Inverse modelling of the spatial distribution of NOx emissions on a continental scale using satellite data

I. B. Konovalov1,2, M. Beekmann2, A. Richter3, and J. P. Burrows3 I. B. Konovalov et al.
  • 1Institute of Applied Physics, Russian Academy of Sciences, Nizhniy Novgorod, Russia
  • 2Laboratoire Inter-Universitaire de Systèmes Atmosphériques, CNRS, Créteil, France
  • 3Institute of Environmental Physics and Remote Sensing, IUP/IFE, University of Bremen, Bremen, Germany

Abstract. The recent important developments in satellite measurements of the composition of the lower atmosphere open the challenging perspective to use such measurements as independent information on sources and sinks of atmospheric pollutants. This study explores the possibility to improve estimates of gridded NOx emissions used in a continental scale chemistry transport model (CTM), CHIMERE, by employing measurements performed by the GOME and SCIAMACHY instruments. We set-up an original inverse modelling scheme that not only enables a computationally efficient optimisation of the spatial distribution of seasonally averaged NOx emissions (during summertime), but also allows estimating uncertainties in input data and a priori emissions. The key features of our method are (i) replacement of the CTM by a set of empirical models describing the relationships between tropospheric NO2 columns and NOx emissions with sufficient accuracy, (ii) combination of satellite data for tropospheric NO2 columns with ground based measurements of near surface NO2 concentrations, and (iii) evaluation of uncertainties in a posteriori emissions by means of a special Bayesian Monte-Carlo experiment which is based on random sampling of errors of both NO2 columns and emission rates. We have estimated the uncertainty in a priori emissions based on the EMEP emission inventory to be about 1.9 (in terms of geometric standard deviation) and found the uncertainty in a posteriori emissions obtained from our inverse modelling scheme to be significantly lower (about 1.4). It is found also that a priori NOx emission estimates are probable to be persistently biased in many regions of Western Europe, and that the use of a posteriori emissions in the CTM improves the agreement between the modelled and measured data.

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