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

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


Outline

• Introduction

• Main objectives

• Gridding methodologies

• Results

• A comparison with national data sets

• Conclusion


Aim of the project1) 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

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 Sector Pollutant Emission Data Source

Industrial (only diffuse souces not covered by E-PRTR)

CO2, CO, NOx, SO2, PM10

CLRTAP, National totals derived by subtraction, UNFCCC National submissions

On-Road Traffic CLRTAP, National totals, UNFCCC National submissions

Domestic Navigation CLRTAP, National totals, UNFCCC National Submissions

International Navigation CLRTAP, Memory items, UNFCCC, Memory items

Domestic Aviation CLRTAP, National totals, UNFCCC National Submissions

Residential and Commercial Combustion

Agriculture NH3, PM10 CLRTAP, National totals


Wickert, 2001


NFR 1A4bi - residential stationary plants

GriddingCORINE Land Cover Classes

(EEA Data service)

•Continuous urban fabric

•Discontinuous urban fabric

Old method New method

GriddingJRC´s Population data

•Land use weighted

•Gridded population

RegionalisationJRC´s Population data on

NUTS3 level

RegionalisationEUROSTAT Population data

on NUTS3 level

AdvantageEmissions are allocated where the population occurs


• 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)


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]

grouped class CORINE Class Label

1 111 Continuous urban fabric

2 112 Discontinuous urban fabric

3 121, 133, 14 Other urban4 122-124, 131-132 Low population artificial

5 21, 22, 23 Agriculture

6 241-243 Heterogeneous7 244, 31 Forest

8 32 Natural vegetation

9 33,4,5 Bare land, wetland and water

CLC Nomenclature aggregation (Gallego, F. J.)


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.


Wood combustionInput 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


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


Comparison with the NL data sets- residential combustion-NOx

Be aware that x and y have not the same scale!!!


Residential Combustion-NOx

er: NL; du: E_PRTR (1x1km2)


Comparison with the NL data sets- residential combustion-CO


Residential Combustion-CO

er: NL; du: E_PRTR (1x1km2)


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.


Gridding of on- road transport activities


Road traffic volume in Trans-Tools


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 Theloke26

Road transport 2008

1x1km: NOx in [t]

Road transport 2008

1x1km: NOx in [t]


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.


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


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 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)


ONLY not E_PRTR related emissions from industrial sources!!!


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


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)

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

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