TNO emissions in MACC

Transcription

1 TNO emissions in MACC presented by Jeroen Kuenen (TNO) Contributing: H. Denier van der Gon A. Visschedijk M. Jozwicka R. Van Gijlswijk

2 2 Outline Introduction Reported emissions TNO emissions: why, how and what? Derive country totals by source sector Gridding procedure Other related work Emission time profiles Composition of PM Future work

3 3 TNO MACC inventory Gridded European emission inventory for years Based on generally available data (e.g. reported emissions) to the extent possible Developed for air quality modellers and used in various EU FP7 projects Developed in projects MACC, MEGAPOLI, EUCAARI Used in projects PASODOBLE, ENERGEO, TRANSPHORM Used in model intercomparison studies (e.g. AQMEII)

4 4 Reported emissions Official reported emissions are important Reporting obligations (e.g. LRTAP/NEC) Input data for air quality modellers Problems for verification and use of reported data by modellers: Spatial resolution (50x50km) Gapfilling needed for countries not reporting Different methodologies in different countries (e.g. jumps at borders) Stability of reported emissions?

5 the official 2005 emission may no longer be the official 2005 emission. Consistency in base year reported EI data (PM10; kt) NETHERLANDS DENMARK POLAND GERMANY It s great that this can still be downloaded! Thnx to CEIP. 5

6 6 (max. Base Year value min. BY value)/ min BY value PM10 discrepancies. Differences: Base Year Austria 29% 23% Belgium 54% 22% Denmark 79% 23% France 13% 3% Germany 29% 12% Italy 13% 11% Netherlands 62% 20% Norway 4% 2% Poland 0% 0% Sweden 70% 28% United Kingdom 10% 11% Realize that often we discuss measures that have a few % impact. It might be progress in emission reporting. but clear that some guidance is needed for a user to understand and believe in the data

7 7 What else? Fully based bottom-up inventories also have their disadvantages Detailed country-specific data and expertise not taken into account Policy-relevant modelling studies should use the official emissions TNO approach Use EMEP reported by country per aggregated source sector as basis where possible Use alternative emissions per source sector as alternative where needed Use own, consistent European-wide gridding methodology at high resolution (1/8 x 1/16 degrees) to ensure consistency for as far as possible

8 Emission (2005=1) 8 Emissions in all countries Reported emission data analysis Year Reported data (data submitted in 2009) NOx SO2 CO NMVOC NH3 CH4 PM10 PM2.5 Direct use of reported annual emission data is not possible... Consistency checks and gapfilling needed For countries where no emission data for these years are available linear interpolation between and projected 2010 is used (data from GAINS)

9 Emission (ton) Emission (ton) 9 Reported emissions time series Look at the main sources of each pollutant in each country Compare with other estimates (e.g. GAINS) to get it right SNAP sectors contributing to >90% of PM10 emissions from Lithuania SNAP sectors contributing to >90% of NMVOC emissions from Greece Year Year

10 10 Result: changes in emissions

11 11 Gridding of emissions

12 12 EPER reported power plants overlaying the TNO data base

13 13 Non-urban road transport emissions distributed using a European traffic intensity road map based on EU Transtools project Completed East-ward with simple road network

14 14 Total NOx 2005 Intensity of NOx emission Europe 2005 on 1/8th x 1/16th degree lon-lat This data for the years is currently being used by many AQ modellers and also as a test set for the suitability / comparison to validate or derive emissions with NO2 satellite data

15 Resulting PM10 map 15

16 16 Checks are done on observed patterns Spatial distribution pattern does not change for Check for large changes whether they are correct Oil platform flaring: error in NL for SNAP sector 9 = corrected Denmark PM10 increase ~ 20% from = correct

17 17 NOx: decline in Western Europe, increase Eastern (mostly non EU) countries

18 19 Temporal emission profiles EI based on yearly emission totals for every sector in each country. For modeling purposes a set of temporal factors was constructed to breakdown annual total emissions into hourly emissions The profiles are for aggregated source sectors according to the SNAP level 1. Most profiles show a sinusoidal curve and distinguish monthly, daily and hourly factors

19 jan feb mar apr may may jun jun jul jul aug aug sep sep oct oct nov nov dec dec Emission (kt) (kt) Emission (kt) 20 Time profiles applied to anthropogenic emissions NH3 NOx PM2.5 Monthly Agriculture Waste treatment and disposal Agriculture Other mobile sources Agriculture Waste treatment and disposal Road transport Waste Other treatment and disposal Solv ent mobile use sources Other Road transport mobile sources Extraction distribution of fossil fuels Road Solv ent transport use Industrial processes Solv Extraction ent use Industrial distribution combustion of fossil fuels Extraction Industrial processes distribution of fossil fuels Residential, commercial and other combustion Industrial Industrial processes combustion Power generation Industrial Residential, combustion commercial and other combustion Residential, Power generation commercial and other combustion Power generation Month Month

20 21 Investigation of the impact of temporal profiles with the TNO Lotos Euros model Average annual concentration difference for ozone (µg/m3; left) and ammonia (%; right)

21 22 Conclusions on temporal profiles Temporal emission profiles are important to properly distribute emissions over time but the importance differs much by source sector Impact of temporal emission profiles on annual average concentrations over Europe is limited but Near anthropogenic hot spots (major cities) it may be substantial. For episodes the impact may be larger one source sector may partly compensate another (obscures real contribution).

22 23 Chemical composition of the MACC PM emissions Air quality models often need to split the PM emissions into its chemical components to account for the particulate behaviour. To accommodate this need a specific PM bulk composition profile file was composed based on work done by TNO in e.g. FP6 EUCAARI and others. Chemical components: elemental carbon (EC), organic carbon (OC), sulphate, sodium and other mineral components. In the MACC project a PM split table by country by source sector has been made, breaking down the PM10 and PM2.5 into components (%) Why per country? Because fuel and technologies differ widely between countries (e.g. residential combustion NL vs Poland)

23 PM10 emission (tonnes) 24 Example of application to Austria PM10 emissions Austria 2005 (45.5 kt) SO4 Other mineral OC (Full Molecular Mass) Na EC Source sector

24 25 Chemical composition of the MACC PM emissions Spatial distribution of the EC component in PM2.5

25 26 Plans MACC-II (just started) Further look into impact of temporal emission patterns Validation of emissions e.g., using EO data Improvement of emissions from international shipping Renew gridded emission time series Extend years covered ( ) Make corrections and improvements to grid where needed update some proxy maps review point sources against latest E-PRTR submissions Extend grid to include whole Mediterranean Sea and Iceland

26 Emission (Gg) 28 Preliminary results PM trend Emissions of PM10 by country group NONEU EU12+HR EU15+NO,CH Year

27 29 Conclusions / Recommendations There is a clear need for high-resolution gridded emission data Important as a sound basis for taking policy measures Use of satellite data is rapidly increasing; high resolution emission data needed as input This is still a top-down approach: does not take into account local and regional specific information Further major improvements are possible by using local data (e.g. FP7 project MEGAPOLI) Condensed PM likely to account for a large share of the current mismatch between models and observations: who s problem is that?

28 30 Thank you for your attention For further information please contact us