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Global bottom-up emission inventories: methodologies, application in atmospheric chemistry modeling and possible verification through satellite measurements.

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Presentation on theme: "Global bottom-up emission inventories: methodologies, application in atmospheric chemistry modeling and possible verification through satellite measurements."— Presentation transcript:

1 Global bottom-up emission inventories: methodologies, application in atmospheric chemistry modeling and possible verification through satellite measurements John van Aardenne European Commission DG Joint Research Centre "The views expressed are purely those of the presenters and may not in any circumstances be regarded as stating an official position of the European Commission."

2 EDGAR project: Ulrike Doering (JRC) Greet Maenhout (JRC) Suvi Monni (JRC) Jos Olivier (PBL) Valerio Pagliari (JRC), Jeroen Peters (PBL), Fulgencio SanMartin (now at OECD) Lorenzo Orlandini (now at DG AGRI), John van Aardenne (JRC, project leader)

3 Reliability of emission data Cost / human investment Continuous emissions monitoring extrapolation of existing emissions data Source sampling Emissions modeling Implied emission factor calculation Technology based emission factor calculation How do we calculate emission inventories E-PRTR EDGAR, National Reports National Reports Satellite observations

4 The Emissions Database for Global Atmospheric Research (EDGAR v4) provides global past and present day anthropogenic emissions of greenhouse gases and air pollutants by country and on spatial grid Inventories for policy purposes: - monitoring the progress/compliance in meeting specific emission targets - scenario studies in emissions reduction options ( period) Inventories for scientific purposes: - understanding processes that lead to anthropogenic and natural emissions - understanding change in atmospheric composition due to emissions trends Science for policy: - impact modeling: human health, ecosystems - assessment of transport of air pollutants across countries and continents (e.g HTAP) 1. Introduction: the EDGAR project Years CO 2 CH 4 N 2 O HFCs PFCs SF 6 CFCs HCFCs CO SO 2 NO X NMVOC NH 3 PM 2.5 PM 10 OC BC

5 2. Methodology: source categories Main groups: - Energy: Fuel Combustion (IPCC 1A) - Fugitive emissions from fuel (IPCC 1B) - Industrial Processes (non-combustion, IPCC 2) - Product Use (IPCC 3) - Agriculture (including Savanna burning), (IPCC 4) - Land Use Change and Forestry (IPCC 5) - Waste (IPCC 6) - Other anthropogenic sources (fossil fuel fires) (IPCC 7)

6 Technology based emission factor approach (see for example on road transport) x: compound, c: country, s: sector, yr: year, AD: activity data, TECH: technology, EOP: % of technologies that are controlled by end-of-pipe abatement measures, EF: uncontrolled emission factor by sector & technology, RED: reduction % on the uncontrolled EF by the installed abatement measure. Methodology follows state-of-the-art in large scale emission inventory calculations, IPCC 2006 Guidelines and EEA/EMEP Guidelines 2. Methodology: calculation of emissions by country and sector

7 AD: activity data, consumption of fuels for combustion (TJ diesel in road transport, with data from International Energy Statistics (IEA, DOE, UN) TECH:technology information: share of activity by technology (% of diesel used by passenger cars, trucks, from literature analysis, own studies) EF:uncontrolled emission factor (kg NO2 / TJ diesel, with datafrom IPCC and EEA/EMEP Emission Inventory Guidelines and literature analysis) EOP:share of the specific emission abatement measures (% of diesel passengers equipped with emission regulations (e.g. EURO II, from scientific literature, country reports and own studies) RED EOP :removal efficiency of abatement measure (% of uncontrolled EF removed, with datafrom IPCC and EEA/EMEP Emission Inventory Guidelines and scientific literature) 2. Example: Road transportation Italy (Diesel combustion)

8 Spatial allocation (x i, y i ) = representing the lower left corner of each 0.1 grid cell, AD: activity data (e.g. natural gas in power plant sector), for some maps technology data is gridded (e.g. underground hard coal mines), C: country, yr: year, Indicator (spatial grid, e.g. road density map). Grid cells share countries and include point source from different countries (no loss of emission information during gridding) Indicator (spatial proxy) is assigned to Activity data and Technologies (note : in most cases no need to adjust data to match EDGAR datasets) 2. Methodology: spatial allocation

9 line sources point sources area sources - Ship tracking data - Road network density etc. - Power plant location - Production facilities Agricultural fields - Population/ Animal density etc. 2. Methodology: spatial allocation

10 Greenhouse gas emissions (Tg CO2 eq.)

11 3. Example: Global emissions from road transportation EDGAR v4.0 Global emissions from road transportation (unit Tg) Road transport NOx emissions in Annex I and non-Annex I countries (Tg NO2) Allocation on grid: proxy data = road density (modified from VMAP, 2007) and population density (modified from CIESIN, 2007) a) Passenger cars, motorcycles, mopeds, light duty, buses = combined road density and population density b) heavy duty vehicles = road density

12 3. Trends: Road transportation

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18 3. Trends: Global emissions from agriculture (air pollutants) EDGAR v4.0 Global emissions from agriculture (unit Tg) Spatial allocation of agricultural emissions Agricultural soils IRRI rice map soil acidity map soil type thermal climate zones cropland and grassland maps of Hyde Agricultural waste burning Hyde cropland map Enteric fermentation Animal density maps of FAO for cattle, buffaloes, goats, swine and sheep (FAO) Rural population Example NH3 gridding Using grid: gc_01x01_LO (grass_crop) Rows: Using grid: crop_01x01_LO (crop) Rows: Using grid: buffalo_01x01_LO (buffaloes) Rows: Using grid: poultry_01x01_LO (poultry) Rows: Using grid: Rur_pop_perc (Rural_population) Rows: Using grid: cattle_01x01_LO (cattle) Rows: Using grid: goats_01x01_LO (goats) Rows: Using grid: pigs_01x01_LO (pigs) Rows: Using grid: sheeps_01x01_LO (sheeps_0.1x0.1) Rows:

19 3. Trends: Agriculture (air pollutants) NH 3 from agriculture, 1970 Majority of NH 3 emissions occur in manure management and N application to soils Global emissions have almost doubled between 1970 and 2005, strong growth especially in Asia In 2005, emission intensity similar in Asia and Europe

20 3. Emissions from Ozone precursors ( )

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24 4. Satellite data and verification of emission inventories Tropospheric NO2 columns for determined from the SCIAMACHY satellite instrument (Figure taken from HTAP 2007 assessment report Emission of NO x (as NO 2 ) on 0.1 degree grid in EDGARv4 (ton per grid cell)

25 4. Satellite data and verification of emission inventories Jianzhong Ma,, Andreas Richter, John P. Burrows, Hendrik Nüß and John A. van Aardenne, Comparison of model-simulated tropospheric NO2, over China with GOME-satellite data, Atmospheric Environment. Volume 40, Issue 4, Pages , 2006.

26 4. Satellite data and verification of emission inventories Jianzhong Ma,, Andreas Richter, John P. Burrows, Hendrik Nüß and John A. van Aardenne, Comparison of model-simulated tropospheric NO2, over China with GOME-satellite data, Atmospheric Environment. Volume 40, Issue 4, Pages , 2006.

27 4. Satellite data and verification of emission inventories Comparing inventories Direct comparison with satellite data Comparing model results with observations (ground or sat.) Annual budgets√-√ Sector detail√?? Time series√√√ Spatial allocation√√√ Discussion: possible verification of inventory data through satellite measurements?


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