1. Tinus Pulles How to establish the uncertainties? Particulate Emission Inventory for Europe

2. Tinus Pulles Outline Background Methods Inventory methods Uncertainty estimation The inventory Uncertainties Conclusions

3. Tinus Pulles Background: Objective This presentation uses data from a project performed for DG Environment to support EU Member States in developing a PM 2.5 inventory Quality requirements: Transparency means that the assumptions and methodologies used for the inventory should be clearly explained Comparability means that estimates of emissions should be comparable among Member States Completeness means that an inventory covers all sources Accuracy is a relative measure of the exactness of an emission or removal estimate Partners in the project where: TNO (NL, lead), AEA-T (UK) IVL (SE)

4. Tinus Pulles Inventory approach Act_ID Sector Location Time Value Activities Tech_ID Technologies EF_ID Tech_ID Pollutant Value Emission factors S_T_ID Act_ID Tech_ID fraction Select_Tech TFEIP Guidebook PM2.5 update TEAM TNO Emissions Assessment Model

5. Tinus Pulles Data sources Emission Factors from CEPMEIP: power plants

6. Tinus Pulles Data sources Emission Factors from CEPMEIP: technology selected Power Plants on Other Bituminous Coal & Anthracite Per country one technology selected Based on the CEPMEIP 1995 inventory Country data needed to improve: EF measurements? Technologies used?

7. Tinus Pulles Particulates emitted in Europe National totals Total emissions in the EU25: 1300 kTon. The other countries in this study add about 500 kTon Total PM2.5 emissions are about 40% of the PM10 emissions Larger countries contribute more to the total emissions than smaller countries. Some discrepancies with RAINS estimates

8. Tinus Pulles Particulates emitted in Europe Contributions by fuel Non-combustion sources contribute more than half of the PM10 emissions one third to the PM2.5 emissions. The very large share to PM10 emissions is due to the inclusion of wind blown dust in agriculture. Solid fuels are by far the largest combustion source for PM10, followed by Liquids and Biomass. For PM2.5 the largest combustion source is Liquids, followed by Biomass and Solids.

9. Tinus Pulles Comparison our national totals with national reports Horizontally: ETC-ACC gap filled national reported data for 2000 Vertically:our estimate for 2000 Major differences occur, but generally not too bad… Are these differences within our uncertainties?

10. Tinus Pulles Uncertainty analysis Monte Carlo simulation Probability Distribution Functions Activity data: normal distributions, standard deviation 10% Emission factors: Lognormal distributions Standard deviation based on expert judgement, derived from the between technology variations in CEPMEIP For a few sample countries: Main sector uncertainties Compare national report with uncertainty range from Monte Carlo

11. Tinus Pulles Uncertainties: example for United Kingdom Dots: probabiliuty distribution for our estimate Red line: UK’s report Seems to be all right!

12. Tinus Pulles Uncertainties: example for Germany Dots: probability distribution for our estimate Red line: Germany’s report Our estimate is significantly higher

13. Tinus Pulles Comparison for National Total PM 2.5 emissions For many countries the reported value is within the 95% confidence interval of our estimate For quite a few the deviation is larger.

14. Tinus Pulles Conclusions We have produced an PM 2.5 and PM 10 emission inventory for Europe (2000) The emission estimates in this inventory show considerable uncertainties For more than half of the countries the national reported value is within our 95% confidence interval We might have underestimated (residential) biomass use We did not detect a significant source that might be missed in national reports. The PM inventories might be as good as the ones used by Dick Derwent! (Thank you Dick) We do not know what we don’t know

15. Tinus Pulles Thank you

16. Tinus Pulles TNO’s TEAM model