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Spatial Resolution of the Diffuse Air Emissions in the E-PRTR

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR. Jochen Theloke, Thomas Gauger, Balendra Thiruchittampalam, Melinda Uzbasich, Sonia Orlikova University of Stuttgart Institute for Energy Economics and the Rational Use of Energy Institute of Navigation. Outline. Introduction

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Spatial Resolution of the Diffuse Air Emissions in the E-PRTR

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  1. Spatial Resolution of the Diffuse Air Emissions in the E-PRTR Jochen Theloke, Thomas Gauger, Balendra Thiruchittampalam, Melinda Uzbasich, Sonia Orlikova University of Stuttgart Institute for Energy Economics and the Rational Use of Energy Institute of Navigation

  2. Outline • Introduction • Main objectives • Gridding methodologies • Results • A comparison with national data sets • Conclusion

  3. Aim of the project • Gathering of available data on diffuse releases to air for • NOx, SO2, PM10, CO and CO2 emissions released by • Transport (on-road, shipping, domestic aviation) • Stationary combustion sources, which are not covered by Annex 1 • Industrial releases from activities which are not covered by the E-PRTR regulation (Annex 1 and 2) • NH3, PM10 emission from agriculture activities • Underlying proxy data to be used for gridding diffuse emissions for each pollutant and each sector • Methodology development for gridding emission data • Derive gridded emission map layers covering EU27 + EFTA4 for selected sectors and pollutants with as a minimum a 5x5 km resolution

  4. Considered Sources – Diffuse sources

  5. Wickert, 2001

  6. NFR 1A4bi - residential stationary plants Old methodNew method Regionalisation EUROSTAT Population data on NUTS3 level Regionalisation JRC´s Population data on NUTS3 level • Gridding • CORINE Land Cover Classes • (EEA Data service) • Continuous urban fabric • Discontinuous urban fabric • Gridding • JRC´s Population data • Land use weighted • Gridded population Advantage Emissions areallocatedwhere the populationoccurs

  7. The European Population Density Map (2006) • digital raster grid with a spatial resolution of 100 m • describes population density (inhab/km²) • spatial coverage: EU27 (except Greece and UK), plus Norway, Iceland, San Marino, Monaco, Lichtenstein and the Vatican City • for Greece and UK: Apply of The European Population Density Map (2000)

  8. JRC`s population density grid: combines information on population per commune using the CORINE Land Cover (COoRdinate INformation on the Environment) and LUCAS (Land Use and Cover Area frame Statistical survey) systems. [http://ec.europa.eu/dgs/jrc/index.cfm?id=2820&dt_code=HLN&obj_id=217&lang=en] CLC Nomenclature aggregation (Gallego, F. J.)

  9. Degree of urbanisation (DGUR 2001) from EUROSTAT_GISCO • The distinction is based on population densities • three area types are defined as follows: • “A” - Densely populated area: refers to a set of closely related local units, each one of which having a density greater than 500 [inhab/km²], and the total population of which being of at least 50 000 inhabitants • “B” - Intermediate area: refers to a set of closely related local units that do not pertain to a densely populated area, each one of which having density greater than 100 [inhab/km²], and where the total population is at least of 50 000 inhabitants or it refers to a set that is adjacent to a highly populated area. • “C” - Thinly populated area: refers to a set of closely related local units that are not part of a densely populated area or of an intermediate area.

  10. Wood combustion • Input data for Regionalization: • Population on NUTS3 level • Forest area from CLC on NUTS3 level  In generally thinly populated area has more forest area on NUTS3 level and vice versa

  11. Energy consumption distinguished by fuel type for gridding • The most usage of Wood fuel is still in thinly populated area • the emissions are re-distributed on base of the assumption that in densely populated regions, the average wood consumption is about half of the amount used in thinly regions. • Gas is located in most densely populated urban areas • the emissions are allocated under taken into account the assumption that in densely populated areas the share of households which are connected to gas installations are three times higher than in thinly populated areas. • Oil and Coal is consumed independent from population density • the emission related to these fuels will be distributed by applying the overall population distribution. Wood Consumption Relation: A : B : C 1 : 1.5 : 2 Gas Connection Relation: A : B : C 3 : 2 : 1 Oil, Coal Consumption Relation: A : B : C 1 : 1 : 1

  12. Comparisonwith the NL datasets- residential combustion-NOx Beawarethat x and y have not the same scale!!!

  13. Residential Combustion-NOx er: NL; du: E_PRTR (1x1km2)

  14. Comparisonwith the NL datasets- residential combustion-CO

  15. Residential Combustion-CO er: NL; du: E_PRTR (1x1km2)

  16. Result from the comparison of European data set vs. national specific approach (NL) • CO-emissions (mainlywoodrelated) • 50 % of the cellshave a deviationof at least 50% • 20% of the cellshave a deviationof at least 80 % • More populatedareaspartlyunderestimated • Lesspopulatedareaspartlyoverestimated • NL specific: woodmainlyusedforcreating a niceatmosphere. (+ glas ofwine, dimmedlights, candles, snacksetc) • It will costyou extra, so it'smoreorless a hobby • Nearerto urban areasthanto rural areas • In the Netherlandsmosthousesareheatedwith Natural gas (>98%). Thereis a smallamountofhouses wich usesotherfuels (oil , lpg , hardcoal, petroleum) • Thereare 9 categoriesofhouses, depending on the age and type; thereisappliedfactorstodistribute the emissions. These factorsdifferdepending on the fuelused. These factorsare not per type ofhouse, but a calculatedfactorincluding the numberofhouseswithinoneof 9 categories.

  17. Gridding of on- road transport activities

  18. Road traffic volume in Trans-Tools

  19. Completion of the road network • Trans-Tools covering not the complete European street network • Enhancement of the Trans-Tools network by GISCO data set • Identification of missing traffic volumes with the TREMOVE model • Spatial weighting by population density for the enhanced network Diffuse emissions in the E-PRTR system - Jochen Theloke

  20. Road transport 2008 1x1km: NOx in [t] Road transport 2008 1x1km: NOx in [t]

  21. Agricultural sector • The emissions from 4B Animal husbandry and Manure Management and 4D Crop production and Agricultural soils are allocated on base of: • animal census from EUROSTAT and FAO • Corine Land Cover use • On base of this information it is possible to allocate the diffuse emissions caused by each animal species and each pollutant in a high spatial resolution of 5 x 5 km² across EU27 + EFTA 4.

  22. Animal census from EUROSTAT An extract from EUROSTAT data sets shows data, which are necessary for quantifying the amount of national emission in determined areas. • The total animal numbers • of each animal species (buffalo, cattle, sheep, pigs, goats, dairy cows) • for each administrative unit at NUTS2 level NUTS 2 Level

  23. Gridded Animal density from FAO • The modeled gridded livestock distributions are produced for the entire globe for the major livestock species: cattle buffalos • goats sheep pigs poultry • The griddedanimaldensityareused for fill the missing animal numbers on NUTS2 from Eurostat and for split at NUTS3 level • Resulting map values are animal densities per km², at a resolution of 3 minutes of arc (approximately 5km at the equator) [FAO] Distribution of ruminant livestock (cattle, buffalos, sheep and goats) [Source: Gridded livestock of the world (FAO), ergodd.zoo.ox.ac.uk/download/reports/wintwellcomeglwposters2a0.ppt]

  24. ONLY not E_PRTR related emissions from industrial sources!!!

  25. Conclusion • Ithasbeendeveloped a comprehensivemethodologyforallocate diffuse emissions over the whole EU27+EFTA4 domain in a consistentway and very high resolution (5x5km gridcells) for different sectors and pollutants • Ithasbeenfurtherdevelopedsectorspecificmethodologies on baseofrecentavailableproxydatasets • The resultshavebeen and will becomparedwith NL specificdatasetbased on baseof a countryspecificapproach • The datasetsreliable in termsof an europeanconsistency and the accuracyof the appliedproxies • Future enhancementstoothersectors, pollutants, countries proxydatasets and yearsarepossible

  26. Thank you for your attention! And thank you especially for good cooperation, support and fruitful discussions to the • DG ENV (Dania Cristofaro, Daniel Martin-Montalvo), • the EDGAR Team (Greet Janssens-Maenhout, John van Aardenne) • RIVM (Wim van der Maas and Bert Leekstra) and • the EEA (Martin Adams and Eva Gossens)

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