1. Using satellite data and data fusion techniques
for air quality mapping
(WP4000)
Jan Horálek

2. 1. Data fusion methodology
2. Description of WP4000
3. Data used and discussion on technical details

3. Linear regression model followed by kriging of its residuals (residual kriging)
The supplementary data for linear regression model (LRM) selected based on their relation with measured AQ data. kriging – spatial interpolation geostatistical method (i.e. knowledge of the spatial structure of air quality field is utilized, using variogram)
η(si) are the residuals of the LRM at the stations
λ i are the weights estimated based on the variogram

4. Linear regression model followed by kriging of its residuals (residual kriging) – continuation
variogram - measure of a spatial correlation parameters: sill, nugget, range Empirical variogram needs
to be fitted by an analytical function, e.g. spherical.
For some pollutants, both monitoring and modelling data may be logarithmically transformed, due to the lognormal distribution of these data.

5. Routine evaluation
cross-validation – the spatial interpolation is calculated for every measurement point based on all available information except from the point in question. These estimated values are compared with the measured ones by scatter-plot (including R2 and regression equation) and by statistical indicators, espec. RMSE and bias (MPE). Occasionally also MAE and other ones where Z(si) is the measured value in point si Ż(si) is the estimation in the point s i using other points N is the number of the stations

6. PM10 annual average 2010 – rural areas
In-situ data EMEP model
Linear regression model (log. transformed):
adj. R2 SEE
EMEP
EMEP, altitude
EMEP, altitude, wind speed

7. PM10 annual average 2010 – rural areas
Rural map (applicable for rural areas only)
cross-validation
RMSE = 4.5 µg. m-3
Bias = 0.2 µg. m-3

8. PM10 annual average 2010 – urban areas
In-situ data EMEP model
Linear regression model (log. transformed):
adj. R2 SEE
EMEP

9. PM10 annual average 2010 – urban areas
Urban map (applicable for urban areas only)
cross-validation
RMSE = 6.6 µg. m-3
Bias = -0.1 µg. m-3

10. PM10 annual average 2010
Final merged map

11. Analysis of the use of different dispersion models Outputs of different models, PM10, annual average 2009
Statistical indicators against in-situ data at rural stations
Different results for different dispersion models

12. Analysis of the use of different dispersion models DF mapping using different models, PM10, annual average 2009, rural
Statistical indicators using cross-validation at rural stations
Similar (in fact no) bias for mapping using different dispersion models

13. 1. Data fusion methodology
2. Description of WP4000
3. Data used and discussion on technical details

14. WP4000 overall description
To examine, test and apply the use of data fusion techniques for combining satellite datasets related to air quality with ground‐level observations and chemical transport model information.
Combination of three data inputs using data fusion:
in-situ measurement data (highly accurate point- based observations, but large spatial gaps)
chemical transport model data (spatially continuous data, but high uncertainties and, for some pollutants, bias)
satellite data (near real time observations of spatial patterns)

15. Combination of in-situ, model and satellite data
Source: Schneider et al. (2012). ETC/ACM Technical Paper 2012/9.

16. WP4000 overall description
Work sub-packages:
4100 Implementation of the data flow
4200 Data fusion based on the historical data
Data fusion based on the near real time data
Web presentation of the maps
Leading organization: CHMI
Partner organizations: IDEA-ENVI (4100, 4400), NILU

17. WP4000 schedule

18. 1. Data fusion methodology
2. Description of WP4000
3. Data used and discussion on technical details

19. In-situ monitoring data used
Historical data: AirBase/e-reporting database (Europe)
CAQR (Czech Republic)
… Other national data??
Near real time data: AirBase/e-reporting database (Europe)
Pollutants: PM10, PM2.5, NO2, SO2
Time step: annual (all), daily (all?), hourly (all?)
Year(s): (Czech Republic only)
2014(? ) + ? – in coordination with other WPs

20. Modelling data used Models: CAMx (4.7 x 4.7 km, Czech Republic)
– runs on HPC cluster 480cores (30 TB disk space)
– coupled with ALADIN
WRF-Chem (? x ? km/deg., major part of Europe)
– Which data format?
– Which time step? Hourly?
Near real time data: How will be WRF-Chem data transmitted?
Pollutants: PM10, PM2.5, NO2, SO2
Year(s): (Czech Republic only)
2014(? ) + ? – in coordination with other WPs

21. Satellite data used data used
Satellite data: NO2, SO2
– Which satellite data?
– Which data format???
– Which time step?
– Which geographical coverage?
PM2.5 ( + PM10 ?)
Near real time data: How will be satelite data transmitted?
Year(s): ? – in coordination with other WPs

22. Web presentation of the maps
Historical data
Near real time data

23. Thank you for your attention.