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I . B. Konovalov

THE USE OF SATELLITE MEASUREMENTS OF TROPOSPHERIC NO 2 FOR ESTIMATION OF MULTI-ANNUAL CHANGES IN NO x EMISSIONS. I . B. Konovalov Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia М. Beekmann

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I . B. Konovalov

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  1. THE USE OF SATELLITE MEASUREMENTS OF TROPOSPHERIC NO2 FOR ESTIMATION OF MULTI-ANNUAL CHANGES IN NOx EMISSIONS I.B. Konovalov Institute of Applied Physics, Russian Academy of Sciences, Nizhny Novgorod, Russia М. Beekmann Laboratoire Inter-Universitaire de Systèmes Atmosphériques / CNRS, Créteil J.P. BurrowsA. Richter Institute of Environmental Physics and Remote Sensing IUP/IFE, University of Bremen, Bremen, Germany Tropospheric NO2 measured by satellites 10-12 September 2007 KNMI, De Bilt, Netherlands

  2. MOTIVATION The good knowledge of long-term changes in NOx emissions is needed for : • evaluating the efficiency of air pollution control measures • elucidating the reasons for the observed changes in atmospheric composition • advancing the abilities of chemistry transport models to reproduce past and predict future changes in both atmospheric compositionand climate

  3. THE MAIN GOALS OF THIS STUDY • To develop an efficient method for estimating inter-annual changes in NOx emissions on a regular grid using satellite measurements of tropospheric NO2 • To estimate decadal changes of NOx emissionsinEurope, Middle East and the Mediterranean • To verify expert estimates used in the EMEP emission inventory

  4. MEASUREMENT DATA: TROPOSPHERIC NO2 COLUMNS GOME (1996-2002) Evaluation of tropospheric NO2 columns: The airmass factors have been estimated based on predictions of the global CTM MOZART for each day and model pixel; surface albedo and cloud parameters are retrieved from GOME measurements; stratospheric columns from SLIMCAT CTM SCIAMACHY (2003-2005) Evaluation of tropospheric NO2 columns: The method is similar to that used in the case of GOME, except that the tropospheric excess method is used : PREPROCESSING STAGE: The daily data have been gridded with resolution of 10x10 and averaged over the period June-August summer months of each year + deconvolution of the GOME data

  5. TROPOSPHERIC NO2 COLUMNS : DECONVOLUTION OF THE GOME DATA Spatial structure of SCIAMACHY data for one year is superimposed over spatial GOME data for another year (Konovalov et al., 2006) SCIAMACHY (ch) W E GOME (cg ) W E GOME (cgdc) deconvoluted

  6. A TEST OF CONSISTENCY OF GOME AND SCIAMACHY DATA where: Cin is a magnitude of NO2 column in the grid cell i and the year n, N is the total number of grid cells GOME SCIAMACHY

  7. MEASUREMENT DATA: NEAR-SURFACE CONCENTRATIONS United Kingdom Automatic Urban and Rural Network (AURN) (www.airquality.co.uk): The data for daily-average concentrations NOxfrom 21 stations which provided the measurements for at least 90% of the days of each summer season in the period from 1996 to 2005 EMEP network: Routine hourly measurements of ozone concentrations from 36 monitors which provided the measurements for at least 90% of the days of each summer seasonfor the period from 1996 to 2004 (no measurements for 2005 were available)

  8. CHIMERE CHEMISTRY-TRANSPORT MODEL : BRIEF DESCRIPTION Developed by:IPSL / CNRS, LISA /CNRS, INERIS [http://euler.lmd.polytechnique.fr/chimere] Model domain (this study):120W-650E, 290-620N; 2541 grid cells Horizontal resolution (these studies):10x10 Vertical resolution:12 layers in hybrid pressure coordinates Pk=akptop + bkpsurf; ptop=200 mbar Chemical mechanism:reduced MELCHIOR ( 44 species, 120 reactions) Meteorology (this study):MM5 driven by NCEP Reanalysis-2 6-hr data Deep convection scheme is included (Tiedke scheme implemented by KNMI) Anthropogenic emissions:EMEP NOx,VOC,CO,SO2, 10 SNAP sectors; resolution 0.50 x0.50; two versions of the expert emissions are considered that were available on the EMEP website before and after 1 December 2006 Biogenic emissionsof isoprene, pinene and NO [Simpson et al., 1999; Stohl et al. 1996] Dry deposition:the resistance analogy [Wesely, 1989]

  9. DECADAL TRENDS IN TROPOSPHERIC NO2 COLUMNS Decadal trends (1996-2005) in NO2 columns from GOME and SCIAMACHY (in 1014cm-2yr-1) Tropospheric NO2 columns from SCIAMACHY summer 2003 (in 1015molec/cm2) Decadal trends in NO2 columns from CHIMERE (in 1014cm-2yr-1) “new” EMEP data “old” (2006) EMEP data

  10. DECADAL TRENDS IN TROPOSPHERIC NO2COLUMNS Trends in % /yr Centering of the simulated time series: where CminandCoin are the modeled and observed NO2 columns in the grid cell i and year n

  11. INVERSE MODELING METHOD: BASIC IDEAS An idea is to fit emission parameters of the model such that they provide better agreement of simulations with measurements • A chemistry transport model used in the framework of an inverse modeling scheme is expected to provide information on • the relationship between NOx emissions and NO2 columns • variability of NO2 columns which is not caused by changes in NOx emissions

  12. INVERSE MODELING METHOD: PRELIMINARY REMARKS A traditional inverse modeling approach which usually involves subjective judgments on the above uncertainties may yield strongly biased a posteriori estimates of NOx emission changes The factors that hinder the measurement-based estimation of NOx emission changes: • Insufficient information on uncertainties in NO2 columns derived from satellite measurements and calculated with the model: systematic versus random uncertainties in space and time, error covariances • The lack of information on uncertainties in “bottom-up” emission inventory data that could be used as a priori estimates: the expert data on NOx emission changes may be uncertain to different degree in different countries and regions

  13. INVERSE MODELING METHOD: PRELIMINARY REMARKS We assume that decadal trends in total NOx emissions are predominantly due to changes in anthropogenic NOx emissions Available data on consumption of fertilizers in the considered regions suggest that the trends in biogenic NOx emissions should have been rather small during the last decade Adopted from: FAO (Food and Agriculture organization of United Nations). Current world fertilizer trends and outlook 20 2008/09, Rome, 2004; EFMA (European Fertilizer Manufacturers Association), Forecast of food, farming and fertilizer use in European Union 2005-2015, Brussels, 2005

  14. INVERSE MODELING METHOD: BASIC DESCRIPTION Replacement: E=exp(e) Nw(=100)isthenumber of grid cells in a “window” with similar values of Co. Notations: E : {E1, E2, …, EN}: NOx emissions E0:the base case emissions (2001 EMEP data) Co: {Co1, Co2, …, CoN}: observed NO2 columns , Cm:modelled NO2 columns N : <=the number of grid cells n: an index of an year : operator of inter-annual change (E= En+1- En) Evaluation of the emission trends: (Co-Cm(E0)) [(Co-Cm(E0))]t; pa=const when -7 <[e]t< 11 (% per year);pa=0 otherwise

  15. INVERSE MODELING METHOD: BASIC DESCRIPTION Notations: E : {E1, E2, …, EN}: NOx emissions E0:the base case emissions (2001 EMEP data) Co: {Co1, Co2, …, CoN}: observed NO2 columns , Cm:modelled NO2 columns N : <=the number of grid cells n: an index of an year : operator of inter-annual change (E= En+1- En) Replacement: E=exp(e) II. (C0-Cm) [(C0-Cm)]d; pa=exp(-[e]d2/(2e2)), e (=0.14)is defined to provide the equality of the variance [e]dT[e]d calculated for Great Britain to the similar variance in the EMEP data Nw(=100)isthenumber of grid cells in a “window” with similar values of Co.

  16. INVERSE MODELING METHOD: EVALUATION OF SENSITIVITIES OF NO2 COLUMNS TO CHANGES OF NOX EMISSIONS Statistical models of relationships between perturbations of NOx emissions and NO2 columns (Konovalov et al., 2006): i=1, …., N The main steps of the method: • To perform a number of model runs with randomly perturbed emissions • To construct statistical regression models describing the relationship between perturbation of NO2 column in the given grid cell and perturbation of emissions in the limited number (25, M=2) of surrounding grid cells. M=2 M=1 M=0

  17. ESTIMATION OF UNCERTAINTIES IN RESULTS • We estimated the uncertainties of the method (e.g., due to the effects of the long-range transport, nonlinearities and deconvolution of the GOME data) byperforming inversions with surrogate NO2 columns and comparing the results with the “exact” solutions • BUT: The potential systematic drifts in the satellite data could not taken into account; they are assumed to be small… • We performed a Monte-Carlo experiment in order to assess how deviations from the trend in the columns, [(C0-Cm)]d, can influence the estimates of the trends [e]t.

  18. RESULTS:SPATIAL DISTRIBUTION OF NOx EMISSION TRENDS Trends in the a posteriori NOx emissions (%/yr) Trends in the (new) EMEP emissions (%/yr) Magnitudes of the NOx emissions specified in CHIMERE (108 cm-2s-1yr-1) Trends in the (old) EMEP emissions (%/yr)

  19. COMPARISON WITH INDEPENDENT MESUREMENTS NOx (UK NAQN): weighed and centered t.s. O3 (EMEP): average of 90th percentile of daily max

  20. RESULTS:TRENDS OF NOx EMISSIONS FOR DIFFERENT COUNTRIES Trends in percent per year Values in [ ] are unceratinty of a linear fit

  21. RESULTS:TRENDS OF NOx EMISSIONS FOR DIFFERENT COUNTRIES Trends in percent per yearValues in [ ] are uncertainty of a linear fit

  22. MAIN CONCLUSIONS • It is shown that available satellite data combined with modeling results can help in obtaining realistic estimates of multi-annual NOx emission changes • It is argued that the obtained independent estimates of decadal changes in NOx emissions are, at least, not less accurate than available emission inventory data • The inverse modeling results confirm predominantly negative NOx emission trends in Western Europe. However some considerable differences between our results and EMEP data are revealed, especially outside of Western Europe. • The long-term satellite measurements together with modern inverse modeling methods open a promising perspective for global monitoring of emission changes and provide the basis for improving quality of data of traditional emission inventories THANK YOU!

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