1. NO x emission estimates from space Ronald van der A Bas Mijling Jieying Ding

2. Overview NOx inversion: from concentrations to emissions – algorithm description of DECSO Results of NOx inversion for specific regions: – China, Korea, South Africa, Middle East, Europe

3. From concentrations to emissions Kalman Filter applied to emissions: State vector (x): gridded emissions NOx emission model (M): persistency Observations (y): NO 2 concentrations of OMI or GOME-2 Model for NO 2 concentrations (H): Chimere + Averaging Kernel + gridding Sensitivity of concentrations to emission changes (H): isobaric trajectory analysis

4. Properties of DECSO* Regional emissions estimates at a resolution of 25  25 km 2 Relatively fast: Only one run of the CTM needed per time step Emission updates by addition instead of scaling: enables detection new hotspots / relocation existing hotspots Update of NOx-correlated pollutants (CO, PM, SO2) Full error estimation (Kalman filter) * Daily Emission estimates Constrained by Satellite Observations

5. Overview DECSO (4) Emission update (3) Inverse with Kalman Filter (1) Forward model run (2) Sensitivity calculation Mijling and Van der A (2012), Daily emission estimates from space, J. Geophys. Res.

6. Current projects on emission estimates from space

7. MarcoPolo: Monitoring and Assessment of Regional air quality in China using space Observations MarcoPolo project funded by the EU. Objectives: Monitoring air quality from space Improved air quality modelling and forecasting by: – New emission inventory Emission estimates from satellites GIS info, MEIC experience Validation Air quality studies Dissemination of MarcoPolo end products

8. Scope of GlobEmission Within the GlobEmission project emission estimates derived from satellite observations are developed. Main advantages: spatial consistency and high temporal resolution pointing out/identifying flaws in bottom-up inventories monitoring of emission changes, trends & new spots rapid availability to users They provide complimentary information to bottom-up emission inventories DUE project in 2011-2016

9. Committed end users European Environmental Agency LATMOS, France Satellite Environment Center of the Chinese Ministry of Environmental Protection Indian Institute of Tropical Meteorology South African National Space Agency + South African Weather Service National Institute for Environmental Studies, Japan Qatar Environmental & Energy Research Institute Specific user requirements: Species: NOx, CH 4, CO, NMVOC, SO 2, PM, O 3 Accuracy: better than 30% - 80 % Spatial resolution: 1 km - 50 km Time resolution: daily – annual Regional and Global

10. Regional emissions

11. Emissions for East Asia

12. Changes w.r.t. to apriori NOx Emissions

13. North vs South Korea

14. NOx emission trends in East Asia Mijling et al., Atmos. Chem. Phys., 2013

15. NOx emission estimates by DECSO: The monthly NO x emission estimates by DECSO in Nanjing for 2013 and 2014, and the monthly NO x emission of the MEIC inventory of 2010. The shade areas show the natural variability (rms) of the mean NO­ x­ emission estimates from DECSO. Ding et al., Atmos. Chem. Phys., 2015 Decrease 25%

16. Emissions for South Africa

17. Emission hot spots in South Africa

18. EDGAR v4.2 2008 DECSO + OMI 2009-2010 Highveld NO x emissions

20. Majuba power plant

21. Matimba power plant

22. Sasol company oil from coal

23. Emissions for the Middle East

24. NO 2 hot spots in the Middle East

28. Emissions for the Europe

29. NO x emissions in Europe Preliminary! MACC-II Emissions December 2009 DECSO v3b + OMI, December 2009

30. Thank you for your attention! Emission data available on www.globemission.eu