1. Atmospheric composition from space: its role in environmental assessment Robert Koelemeijer Netherlands Environmental Assessment Agency (MNP)

2. 2 MNP: bridge between science and policy How does air quality develop, and do we understand this? Do we meet (future) policy goals? (national emission ceilings, EU air quality limit values) What measures can be taken to meet the goals, what are the costs and benefits of measures? Our “Clients”: national government, but also international bodies (European Topic Center Air and Climate Change EEA, CLRTAP convention (EMEP), European Commission, European Parliament, OECD, UNEP)

3. 3 How do we work? Main tools: Emission inventories –Large point sources: individually registered –Other sources: emission = activity * emission_factor Assessment of future developments (activities, policy measures)  emission projections  assessment of compliance with national emission ceilings Chemical transport models + ground based measurements  assessment of concentration levels and compliance with (future) air quality limit values. “Options document”: compilation of all technically feasible emission reduction measures and costs  cost-curves

4. 4 Role of satellite data Validation of (combination of) models/emissions (source- strengths and locations)

5. 5 Jonson et al., 2007. NO2 EMEP-GOME

6. 6 Jonson et al., 2006. HCHO, EMEP-GOME

7. 7 Role of satellite data Validation of (combination of) models/emissions (source- strengths and locations) Short-term air quality forecast (US-studies, ECMWF-GEMS, in NL: SMOGPROG)

8. 8 Role of satellite data Validation of (combination of) models/emissions (source- strengths and locations) Short-term air quality forecast Observe global trends and illustrating the large-scale picture

9. 9 Richter et al., 2005. Global NO2-trend GOME

10. 10 Sciamachy NO2-column

11. 11 Project: Mapping PM2.5 in Europe (MNP, TNO, RIVM) Source: Airbase

12. 12 Can we improve mapping of PM using satellite information (in addition to ground network and models)? Ingredients: MODIS data of Aerosol Optical Thickness (AOD), and “fine fraction” of the AOD (AOD F ) Year: 2003, domain: Europe Ground-based measurements: AERONET (AOD) + AirBase (PM) Model: Lotos-Euros (TNO / RIVM / MNP) Approach: Validation against AERONET Mapping of PM, using ground-based and satellite measurements and models

13. 13 Validation against AERONET

14. 14 Findings of validation against AERONET MODIS AOD (collection 4) shows a seasonal bias over Europe

15. 15 Findings of validation against AERONET Relative difference more or less constant “AOD_modis * 0.7 = AOD_AERONET” (collection 4-data) “AOD F _modis * 0.9 = AOD_AERONET” (collection 4-data)

16. 16 Findings of validation against AERONET MODIS AOD and AOD F agree within error-bounds quoted in literature after removal of bias (±0.05 ± 20%, 1σ-error)

17. 17 Findings of validation against AERONET MODIS correlates fairly good in time and space with AERONET –Average time-correlation:0.72 (AOD) and 0.66 (AOD F ) (34 stations, whole 2003) –Spatial correlation yearly averages: 0.64 (AOD) and 0.72 (AOD F )

18. 18 Cloud contamination? PCCD = Potentially Cloud Contaminated Data PCCD-matrix for MODIS and AATSR 1/3 rd of MODIS AOD data may suffer from cloud-contamination Half of AATSR AOD data may suffer from cloud contamination

19. 19 MODIS AOD, 2003

20. 20 MODIS AOD F, 2003

21. 21 Top right: modeled PM2.5 Top left: MODIS AOD F Bottom: merged PM2.5 PM2.5_merged = a 1 AOD F + a 2 PM2.5_model a 1 and a 2 found by least- squares fitting to measured PM2.5 (AirBase)

22. 22 Assimilation experiment Lotos/Euros Chemical Transport Model Ensemble Kalman Filtering Example: AOD distribution at 26 march 2003

23. 23 Modeled, measured and assimilated AOD

24. 24 Differences between fitting and assimilation method

25. 25 Findings of PM2.5 mapping study MODIS AOD (collection 4) shows a large bias over Europe, but agrees within error-bounds quoted in literature after removal of the bias (note: for clear-sky situations) MODIS data can still suffer from residual cloud-contamination (perhaps about 1/3 rd of all data) First attempts were made to use MODIS data for mapping PM2.5 distributions in Europe Satellite measurements of AOD can be used to improve mapping of PM2.5 in Europe, but more extensive validation and further improvement of retrieval algorithms will be necessary Koelemeijer et al., Atm. Env. 40, 5304-5315, 2006 Schaap et al.,, Atm. Env. 42, 2187-2197, 2008

26. 26 Correlation PM2.5 – AOD at Cabauw-NL

27. 27 Summary Application of satellite data: –Validation of models and emissions –Get information about countries / areas where ground-based measurements are not (made) available Strong points: –Method independent of country borders –Global coverage –High communicative value Weaknesses: –Only data under (near) cloud-free conditions –Sensitive to assumptions on atmospheric state / surface Use in policy oriented reports could benefit from: –Easy access to yearly-averaged data + published

28. 28 Fijn stof (PM10) in 2006

29. 29 Air quality (NO2) in 2006