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Global bottom-up emission inventories:

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

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Global bottom-up emission inventories:

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  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) http://edgar.jrc.ec.europa.eu

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

  4. 1. Introduction: the EDGAR project 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 (2000-2100 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) CO2 CH4 N2O HFCs PFCs SF6 • Years • 1970 - 2005 CFCs HCFCs COSO2 NOX NMVOC NH3 PM2.5 PM10 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. 2. Methodology: calculation of emissions by country and sector 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

  7. 2. Example: Road transportation Italy (Diesel combustion) 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) REDEOP: removal efficiency of abatement measure (% of uncontrolled EF removed, with datafrom IPCC and EEA/EMEP Emission Inventory Guidelines and scientific literature)

  8. 2. Methodology: spatial allocation Spatial allocation (xi, yi) = 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)

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

  10. Greenhouse gas emissions (Tg CO2 eq.)

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

  12. 3. Trends: Road transportation

  13. 3. Trends: Road transportation

  14. 3. Trends: Road transportation

  15. 3. Trends: Road transportation

  16. 3. Trends: Road transportation

  17. 3. Trends: Road transportation

  18. 3. Trends: Global emissions from agriculture (air pollutants) 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 EDGAR v4.0 Global emissions from agriculture (unit Tg) Example NH3 gridding Using grid: gc_01x01_LO (grass_crop) Rows: 1084154 Using grid: crop_01x01_LO (crop) Rows: 723988 Using grid: buffalo_01x01_LO (buffaloes) Rows: 96946 Using grid: poultry_01x01_LO (poultry) Rows: 928061 Using grid: Rur_pop_perc (Rural_population) Rows: 1480982 Using grid: cattle_01x01_LO (cattle) Rows: 877516 Using grid: goats_01x01_LO (goats) Rows: 587775 Using grid: pigs_01x01_LO (pigs) Rows: 684520 Using grid: sheeps_01x01_LO (sheeps_0.1x0.1) Rows: 748986

  19. 3. Trends: Agriculture (air pollutants) • Majority of NH3 emissions occur in manure management and N application to soils • Global emissions have almost doubled between 1970 and 2005, strong growth especially in Asia NH3 from agriculture, 1970 In 2005, emission intensity similar in Asia and Europe

  20. 3. Emissions from Ozone precursors (1990-2005)

  21. 3. Emissions from Ozone precursors (1990-2005)

  22. 3. Emissions from Ozone precursors (1990-2005)

  23. 3. Emissions from Ozone precursors (1990-2005)

  24. 4. Satellite data and verification of emission inventories TroposphericNO2 columns for 2004-2005 determined from the SCIAMACHY satellite instrument (Figure taken from HTAP 2007 assessment report Emission of NOx(as NO2) 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, HendrikNüß and John A. van Aardenne, Comparison of model-simulated troposphericNO2, over China with GOME-satellite data, Atmospheric Environment. Volume 40, Issue 4, Pages 593-604, 2006.

  26. 4. Satellite data and verification of emission inventories Jianzhong Ma,, Andreas Richter, John P. Burrows, HendrikNüß and John A. van Aardenne, Comparison of model-simulated troposphericNO2, over China with GOME-satellite data, Atmospheric Environment. Volume 40, Issue 4, Pages 593-604, 2006.

  27. 4. Satellite data and verification of emission inventories Discussion: possible verification of inventory data through satellite measurements?

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