1. Uncertainty assessment in European air quality mapping and exposure studies Bruce Rolstad Denby, Jan Horálek 2, Frank de Leeuw 3, Peter de Smet 3 1 Norwegian Institute for Air Research (NILU), PO BOX 100, 2027 Kjeller, Norway 2 Czech Hydrometeorological Institute (CHMI), Praha 3 The Netherlands Institute for Public Health and the Environment (RIVM) EGU, Vienna, April 2012

2. Background Every year the European Environment Agency (EEA) publishes maps of air quality in Europe These maps are used for public information, to inform authorities and for trend analysis Uncertainty maps are also produced and provided alongside the air quality maps Population exposure for all of Europe is assessed in terms of average exposures per country and exposure above threshold

3. Uncertainty questions How can we best quantify the spatial uncertainty in the air quality maps? How can we best quantify the uncertainty in the exposure calculations? How can we best quantify the uncertainties in the trend analysis?

4. Example mapping methodology for PM 2.5 Select annual mean concentrations of monitored particulate matter (PM 2.5 /PM 10 ) from AirBase (200/1200 stations) Acquire spatially distributed supplementary data (population, altitude, meteorology, CTM) Create ’Pseudo’ PM 2.5 data from the PM 10 data using linear regression with some of the supplementary data Linearly regress the log transformed PM 2.5 data with the supplementary data to create a base map Krig the logarithmic residuals using ordinary kriging to 10 km grids and add to the base map The (sub)urban and rural stations are interpolated seperately and then combined based on a population weighting

5. Distribution of PM 10 and PM 2.5 stations

6. Creation of ’pseudo’ PM 2.5 from PM 10 Linear regression at station sites using PM 10 + latitude + longitude + sunshine duration + population Both rural and (sub)urban

7. Creation of base map for PM 2.5 Linear regression using spatially distributed CTM + altitude + population + wind speed data rural(sub)urban

8. Residual kriging of the residual Fitted emperical semi-variograms rural(sub)urban

9. Residual kriging of the residual Leave-one-out cross validation rural(sub)urban

10. Urban and rural interpolations combined to make maps

11. Residual kriging variance used for uncertainty maps

12. Concentration map is combined with population map to estimate exposure Population map of Europe

13. Calculation of aggregated population weighted uncertainty Population weighted concentration (C pw ) Spatial correlation ( ρ ) determined from variogram model (ϒ) ( c is sill) Covariance deconvolved to all grid points ( C i,C j ) using calculated kriging variance ( σ i,j )and spatial correlation ( ρ i,j ) Population weighted ( a i,j ) aggregated variance ( σ w 2 ) is calculated per country

14. Aggregated exposure per country Population weighted concentration for 2007 and 2008 CyprusNederland Germany

15. Based on the aggregated uncertainty per country, shifting the distribution by ± σ w ( bias ) Calculation of threshold uncertainty -5 +5

16. Population exposed above the limit value (25 ug/m 3 ) for 2007 and 2008 Aggregated exposure above threshold Italy Poland Romania Serbia Greece

17. Calculation of threshold uncertainty This is not a satisfactory method but is intended to be indicative Requires a better, more formal approach – e.g. Monte Carlo simulations of the original interpolations What other possibilities exist?

18. Summary European wide maps of air pollutants are made using linear regression and residual kriging Uncertainty of the maps is estimated using the residual kriging variance Aggregated uncertainty in population weighted concentrations is determined using the variogram and deconvolving Aggregated uncertainty in exposure thresholds is not satisfactoraly determined

19. Questions to the floor Is the residual kriging variance a sufficent uncertainty indicator for this application? – Does it account for monitoring, ’pseudo’, representativeness, spatial regression and interpolation uncertainties? Is the method applied to determine aggregated population weighted concentation uncertainty adequate or even correct? How can we determine the uncertainty of the exposure thresholds?

20. Report available Mapping annual mean PM2.5 concentrations in Europe: application of pseudo PM2.5 station data. ETC/ACM Technical Paper 2011/5 URL: http://acm.eionet.europa.eu/reports/ETCACM_TP_2011_5_s patialPM2.5mapping http://acm.eionet.europa.eu/reports/ETCACM_TP_2011_5_s patialPM2.5mapping Google: ”Mapping annual mean PM2.5”