1. Overview of Techniques for Deriving Emission Inventories from Satellite Observations Frascati, 26-27 November 2009 Bas Mijling Ronald van der A

2. GOAL Obtaining an up-to-date emission inventory for air pollutants on a high resolution by using satellite observations. Here:air pollutant= NO 2 high resolution= 0.25º × 0.25º satellite instrument= OMI, GOME-2

3. Drawbacks of bottom-up inventories Depend on the availability and reliability of the statistical information. Depend on historic information: easily out- dated. Uncertainties in spatial resolution if only area totals are available. Examples of emission inventories Edgar (global) INTEX-B (regional - Asia)

4. Satellite minus (CHIMERE + INTEX-B) May – November 2008 GOME-2 OMI -20 0 20 10 15 molecules/cm 2

5. Constraining emissions with satellite observations Satellites have world-wide, homogeneous coverage. Correcting inventory for emission trends Detecting new (unknown) emission sources Emission trend analysis reveals effectiveness of air pollution policy Up-to-date emission inventories improve air quality forecasting

6. Complicating factors: Transport of pollutant away from the source Lifetime of pollutant Variability in lifetime (temperature, chemical composition…) Satellite can only observe pollutant con- centrations. These should be backtracked to their underlying emissions: THIS IS AN INVERSE PROBLEM

7. Lifetime example (1): CO 2

8. Lifetime example (2): NO 2 tropospheric NO 2 in summer: ~4h, in winter: ~10h OMI 2005-2008

9. Concentrations  Emissions (INTEXB inventory)

10. 1.Local, linear relation concentration and emission Martin et al. (2006) Space-based constraints on NOx emission, J. Geophys. Res. Jaeglé et al. (2005) Global partitioning of NOx sources using (…), Faraday Discuss. Ω E Top-down and bottom-up emission weighted by their relative uncertainty: Assume linear relation between NO x emission and NO 2 concentration: With corresponding relative error:

11. Advantages Fast, no inverse modeling needed Emission update gives also new error estimates Disadvantages Transport to neighbouring grid cells neglected Only one emission update possible No new sources detected if a priori emission is 0 1.Local, linear relation concentration and emission

12. 2.Local, linear relation applied iteratively Van der A (2006), Anthropogenic NOx emission estimates for China, KNMI Technical Report Ω E Assume linear relation between NO x emission and NO 2 concentration: Iterate until convergence criteria are met.

13. Advantages Fast, no inverse modeling needed Iteration compensates for transport to neighbouring grid cells Disadvantages No error estimates of inventory No new sources detected if a priori emission is 0 2.Local, linear relation applied iteratively Van der A (2006), Anthropogenic NOx emission estimates for China, KNMI Technical Report

14. More realistic inversion: Sensitivities When transport is taken into account, emissions in all grid cells can contribute to the observed concentration: j i

15. 3. Monte Carlo method Konovalov et al. (2006), Inverse modeling of NOx emission on a continental scale (2006), ACP Perform model runs with random perturbations on the a priori emissions to get a set of linear equations from which the sensitivities can be solved: j i … The optimal number of random model runs depends on the desired accuracy. For two next neighbours: N = 100

16. Advantages Takes transport of nearest neighbours into account Disadvantages Time consuming calculations: ~100 model runs needed to solve transport from 2 nearest neighbours. 3. Monte Carlo method

17. 4.Adjoint inverse modeling + 4D VAR Kurokawa et al. (2008), Adjoint inv. modeling of NOx emissions over eastern China, Atmos. Env. Stavrakou and Müller (2008), Grid-based inversion of CO emissions, J. Geophys. Res.

18. Advantages Adjoint modeling allows to compute sensitivities for long-lived gases Disadvantages Time consuming computations Adjoint code not always available 4.Adjoint inverse modeling + 4D VAR

19. 5.Data assimilation using Kalman filter Napelenok et al. (2008), Inverse modeling method for spatially-resolved NOx emissions, ACP Emission updates by the Kalman filter equations. Sensitivities of emission sources calculated by the Decoupled Direct Method (DDM): transported through adapted transport equations from the model.

20. Kalman filter State vector forecast x f (t i+1 ) = M i [x a (t i )] Error covariance forecast P f (t i+1 ) = M i P a (t i )M i T + Q(t i ) Kalman gain matrix K i = P f (t i )H i T [H i P f (t i )H i T + R i ] -1 State vector analysis x a (t i ) = x f (t i ) + K i (y i o – H i [x f (t i )]) Error covariance analysis P a (t i ) = (I – K i H i ) P f (t i ) x = state vector, describing the emission inventory H = chemical transport model, calculating concentrations from emissions y = observation of concentrations, e.g. by satellite M = emission evolution model

21. Advantages Data assimilation allows for time evolution of emission inventories with correct error estimates Disadvantages Calculation of sensitivities expensive Large matrix inversions in Kalman equations 5.Data assimilation using Kalman filter

22. Flatland simulation “Toy” transport model in two dimension Simplified advection model allows analytic calculation of sensitivities

23. concentrations emissions (1/20) Local, linear

24. concentrations emissions (2/20) Local, linear

25. concentrations emissions (3/20) Local, linear

26. concentrations emissions (4/20) Local, linear

27. concentrations emissions (5/20) Local, linear

28. concentrations emissions (6/20) Local, linear

29. concentrations emissions (7/20) Local, linear

30. concentrations emissions (8/20) Local, linear

31. concentrations emissions (9/20) Local, linear

32. concentrations emissions (10/20) Local, linear

33. concentrations emissions (11/20) Local, linear

34. concentrations emissions (12/20) Local, linear

35. concentrations emissions (13/20) Local, linear

36. concentrations emissions (14/20) Local, linear

37. concentrations emissions (15/20) Local, linear

38. concentrations emissions (16/20) Local, linear

39. concentrations emissions (17/20) Local, linear

40. concentrations emissions (18/20) Local, linear

41. concentrations emissions (19/20) Local, linear

42. concentrations emissions (20/20) Local, linear

43. concentrations emissions (1/20) Kalman

44. concentrations emissions (2/20) Kalman

45. concentrations emissions (3/20) Kalman

46. concentrations emissions (4/20) Kalman

47. concentrations emissions (5/20) Kalman

48. concentrations emissions (6/20) Kalman

49. concentrations emissions (7/20) Kalman

50. concentrations emissions (8/20) Kalman

51. concentrations emissions (9/20) Kalman

52. concentrations emissions (10/20) Kalman

53. concentrations emissions (11/20) Kalman

54. concentrations emissions (12/20) Kalman

55. concentrations emissions (13/20) Kalman

56. concentrations emissions (14/20) Kalman

57. concentrations emissions (15/20) Kalman

58. concentrations emissions (16/20) Kalman

59. concentrations emissions (17/20) Kalman

60. concentrations emissions (18/20) Kalman

61. concentrations emissions (19/20) Kalman

62. concentrations emissions (20/20) Kalman

63. Convergence behaviour KalmanLocal, linear

64. Conclusions 14 Johan Cruijff Every advantage has its disadvantage on the different techniques for deriving emission inventories from satellite observations:

66. 6. Lin et al. (2009)

67. Advantages - Disadvantages - 6. Lin et al. (2009)