1. Aristeidis K. Georgoulias Contribution of Democritus University of Thrace-DUTH in AMFIC-Project Democritus University of Thrace Laboratory of Atmospheric Pollution and Pollution Control Engineering of Atmospheric pollutants

2. Main Objectives: 1. Exploit WP2, WP3, WP4 and WP5 outcome - WP2: Ozone, NO 2, SO 2, HCHO columns from GOME, SCIAMACHY and OMI, CO & CH 4 columns from SCIAMACHY, Aerosol properties from AATSR - WP3: Validation of SO 2, CO and ozone columns, aerosol concentration/properties - WP4: High resolution air quality maps - WP5: model forecasts for ozone, NO 2, SO 2, CO and aerosols 2. Demonstrate how to use data from satellites to explore and monitor processes relevant to the transport of air pollution 3. Provide an extensive system assessment report, suitable for feeding GEOSS and GMES requirements

3. DUTH Tasks: 1. Data fields will be homogenized and brought into a common spatial and temporal format (INSPIRE) 2. A web database will host the (observed, modeled and/or assimilated) data 3. A set of tools will allow easy manipulation and analysis of the data Automatic detection of features such as plumes and monitoring of their evolution Automatic detection of features such as plumes and monitoring of their evolution Data covariance analysis, enabling the detection of emission signatures of different sources Data covariance analysis, enabling the detection of emission signatures of different sources Cluster analysis Cluster analysis Export of data in different formats Export of data in different formats

4. Satellite System Predecessor: Satellite Earth simulator-AIRSAT http://www.satellite-earth-simulator.com/ Satellite Data Sources: TOMS (Earth Probe TOMS)TOMS (Earth Probe TOMS) SCIAMACHY (ENVISAT)SCIAMACHY (ENVISAT) GOME (ERS-2)GOME (ERS-2) MODIS (AQUA)MODIS (AQUA) Meteorological Data Source: NCEP/NCAR Reanalysis ProjectNCEP/NCAR Reanalysis Project

5. Meteorological parameters Temperature Temperature Humidity Humidity Pressure Pressure Wind speed Wind speed Precipitation Precipitation Atmospheric data Ozone vertical column Ozone vertical column NO 2 vertical column NO 2 vertical column Aerosol Optical Thickness over ocean Aerosol Optical Thickness over ocean Cloud Optical Thickness Cloud Optical Thickness Cloud Top Temperature Cloud Top Temperature Optical Depth Land and Ocean Optical Depth Land and Ocean Atmospheric Water Vapour Atmospheric Water Vapour Ocean data Chlorophyll Chlorophyll Sea surface temperature, day/night Sea surface temperature, day/night

6. An Example: Data from ICSU World Data Center for Remote Sensing of the Atmosphere http://wdc.dlr.de geographical area data set time period (Spectral Harmonic Analysis applied on NASA EP TOMS Level 2 data)

7. daily data in csv ascii format (1 o x1 o analysis) daily maps start animation select coordinates

8. time series for Xanthi [41.133 o N, 24.883 o E] graph & csv ascii format animation

9. Sea Level Pressure Relative Humidity Statistical exploitation of data (1): 60% covariance of Sea Level Pressure and Relative Humidity (2006)

10. Statistical exploitation of data (2): 17% covariance of Sea Level Pressure and Relative Humidity (2006) Sea Level Pressure Relative Humidity

11. Statistical exploitation of data (3): Correlation of Precipitation above the region of Thrace, Greece and large scale Sea Level Pressure for several temporal lags (1/2004- 12/2005) Seasonal Correlation of Precipitation above the region of Thrace, Greece and large scale Sea Level Pressure for several temporal lags (January- February-March 2004-2005)

12. Statistical exploitation of data (4): 95% covariance of Precipitation above the region of Thrace, Greece and Sea Level Pressure (1/2004- 12/2005) 98% seasonal covariance of Precipitation above the region of Thrace, Greece and Sea Level Pressure (January–February-March 2004-2005)

13. Automatic plume detection (1): Example: Etna eruption (13-15/9/2007)

14. Automatic plume detection (2): Image Enhancement by using methods of color enrichment

15.  Finding the location of plumes via chromatic analysis of images with the use of local chromatic histograms  Calculation of the distance and spreading of plumes via calculation of the distance between the centers of concentrations which result from the method of clustering Automatic plume detection (3): Methodology:

16. Trace gas transport from standard sources Maps with back-trajectories from HYSPLIT model and great standard sources, application of a cluster analysis technique, ascii data with information on back-trajectories that passed over major sources

17. Contribution of NOA (1)  Task 6.1. NOA will feed DUTH with INSPIRE requirements  Task 6.2.a. NOA will organise an intermediate user evaluation half-way the project, when we will ask the users to write an evaluation. Based on this evaluation and the first validation results of WP3, the retrieval algorithms (WP2) and model developments (WP4 and WP5) will be adjusted accordingly

18. Contribution of NOA (2)  Task 6.2b: KNMI, NOA and NSMC will organise a Workshop for end-users. The workshop will help federate and coordinate the demands of end-users from China. Prior to, and during the workshop, NOA will utilize the outcome of ongoing ESA GMES Service Element and DUE (Data User Element) Programmes, such as PROMOTE, GLOBAEROSOL and TEMIS for ensuring that the project outcome will directly serve the GMES Thematic Priority Atmosphere. Also, contribution to GEOSS objectives and needs will be a key element of this action

19.  Month 6: Determination of web database structure  Month 12: Delivery of the web database and software  Month 12: User evaluation  Month 18: Testing of the database and optimisation for users  Month 24: Operational database compliant with INSPIRE  Month 24: Delivery of an assessment report  Month 24: End-user workshop Timetable & Deliverables: