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

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Outline Introduction Main objectives Gridding methodologies Results A comparison with national data sets Conclusion

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Aim of the project 1)Gathering of available data on diffuse releases to air for ●NO x, SO 2, PM10, CO and CO 2 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) ●NH 3, PM10 emission from agriculture activities ●Underlying proxy data to be used for gridding diffuse emissions for each pollutant and each sector 2)Methodology development for gridding emission data 3)Derive gridded emission map layers covering EU27 + EFTA4 for selected sectors and pollutants with as a minimum a 5x5 km resolution

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Considered Sources – Diffuse sources SectorPollutantEmission Data Source Industrial (only diffuse souces not covered by E-PRTR) CO 2, CO, NO x, SO 2, PM10 CLRTAP, National totals derived by subtraction, UNFCCC National submissions On-Road TrafficCLRTAP, National totals, UNFCCC National submissions Domestic NavigationCLRTAP, National totals, UNFCCC National Submissions International NavigationCLRTAP, Memory items, UNFCCC, Memory items Domestic AviationCLRTAP, National totals, UNFCCC National Submissions Residential and Commercial Combustion AgricultureNH 3, PM10CLRTAP, National totals

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

Wickert, 2001

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke NFR 1A4bi - residential stationary plants Gridding CORINE Land Cover Classes (EEA Data service ) Continuous urban fabric Discontinuous urban fabric Old method New method Gridding JRC´s Population data Land use weighted Gridded population Regionalisation JRC´s Population data on NUTS3 level Regionalisation EUROSTAT Population data on NUTS3 level Advantage Emissions are allocated where the population occurs

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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) The European Population Density Map (2006)

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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. [ ] grouped classCORINE ClassLabel 1111Continuous urban fabric 2 112Discontinuous urban fabric 3 121, 133, 14Other urban , Low population artificial 5 21, 22, 23Agriculture Heterogeneous 7244, 31Forest 8 32Natural vegetation 9 33,4,5Bare land, wetland and water CLC Nomenclature aggregation (Gallego, F. J.)

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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 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 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.

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

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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. Oil, Coal Consumption Relation: A : B : C 1 : 1 : 1 Gas Connection Relation: A : B : C 3 : 2 : 1 Wood Consumption Relation: A : B : C 1 : 1.5 : 2 Energy consumption distinguished by fuel type for gridding

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

Comparison with the NL data sets- residential combustion-NO x Be aware that x and y have not the same scale!!!

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

Residential Combustion-NO x er: NL; du: E_PRTR (1x1km 2 )

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Comparison with the NL data sets- residential combustion-CO

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

Residential Combustion-CO er: NL; du: E_PRTR (1x1km 2 )

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Result from the comparison of European data set vs. national specific approach (NL) CO-emissions (mainly wood related) 50 % of the cells have a deviation of at least 50% 20% of the cells have a deviation of at least 80 %  More populated areas partly underestimated  Less populated areas partly overestimated  NL specific: wood mainly used for creating a nice atmosphere. (+ glas of wine, dimmed lights, candles, snacks etc)  It will cost you extra, so it's more or less a hobby  Nearer to urban areas than to rural areas  In the Netherlands most houses are heated with Natural gas (>98%). There is a small amount of houses wich uses other fuels (oil, lpg, hardcoal, petroleum)  There are 9 categories of houses, depending on the age and type; there is applied factors to distribute the emissions. These factors differ depending on the fuel used. These factors are not per type of house, but a calculated factor including the number of houses within one of 9 categories.

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Gridding of on- road transport activities

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Road traffic volume in Trans-Tools

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

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

 Road transport x1km: NOx in [t] Road transport x1km: NOx in [t]

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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.

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke An extract from EUROSTAT data sets shows data, which are necessary for quantifying the amount of national emission in determined areas. NUTS 2 Level The total animal numbers of each animal species (buffalo, cattle, sheep, pigs, goats, dairy cows) for each administrative unit at NUTS2 level Animal census from EUROSTAT

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Gridded Animal density from FAO The modeled gridded livestock distributions are produced for the entire globe for the major livestock species: cattle buffalos goatssheep pigspoultry The gridded animal density are used 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] [Source: Gridded livestock of the world (FAO), ergodd.zoo.ox.ac.uk/download/reports/wintwellcomeglwposters2a0.ppt] Distribution of ruminant livestock (cattle, buffalos, sheep and goats)

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

ONLY not E_PRTR related emissions from industrial sources!!!

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke Conclusion  It has been developed a comprehensive methodology for allocate diffuse emissions over the whole EU27+EFTA4 domain in a consistent way and very high resolution (5x5km grid cells) for different sectors and pollutants  It has been further developed sector specific methodologies on base of recent available proxy data sets  The results have been and will be compared with NL specific data set based on base of a country specific approach  The data sets reliable in terms of an european consistency and the accuracy of the applied proxies  Future enhancements to other sectors, pollutants, countries proxy data sets and years are possible

Spatial Resolution of the Diffuse Air Emissions in the E-PRTR- Jochen Theloke 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)