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Fossil fuel, Biofuel and Biomass Burning emissions for 2000-2007 (trace gases and particles) C. Liousse, B. Guillaume, A. Konaré, C. Junker, C. Granier,

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Presentation on theme: "Fossil fuel, Biofuel and Biomass Burning emissions for 2000-2007 (trace gases and particles) C. Liousse, B. Guillaume, A. Konaré, C. Junker, C. Granier,"— Presentation transcript:

1 Fossil fuel, Biofuel and Biomass Burning emissions for (trace gases and particles) C. Liousse, B. Guillaume, A. Konaré, C. Junker, C. Granier, J.M. Grégoire, A. Poirson and E. Assamoi

2 Fossil fuel and Biofuel emissions ++ For the first time to our knowledge a coherent inventory for gases and particles based on the same method and proxy data (fuel consumption, fuel usage..) - - Africa data are extracted from our global model of emissions Pollutants : CO, CO2, NOx, NMVOC, SO2, BC, OCp, OCtot African Emissions are provided country by country Spatialization is done by using the GISS population map

3 A bottom-up method ( based on Junker and Liousse, ACPD 2006 ) United Nations : Energy database Fuel consumption data for 185 countries, 33 different fuels and over 50 different usage/technology categories Emissions are fuel-dependant, fuel usage-dependant and technology-dependant Our « lumping » : Industrial/Domestic/Traffic Developed/Semi developed/Developing => Emission factors for 3 country classifications, 8 different fuels and 3 usage categories Population density within each country (population map) and emissions country/country => 1°X1° spatial distribution of emissions

4 EF values for gases CO, NOx, SO2, NMVOC CO2

5 EF (BC)… EF (OCp)… EF values for Black carbon and primary organic carbon

6 Fossil fuel and biofuel combustions 0.64 Mt(BC) BCCO 49.7 Mt(CO) Year 2000 Africa 0 BC (tons/1°x1°) CO (kt/1°x1°)

7 Biofuel and Fossil fuel combustions Year 2000 Africa NMVOC 5.5 Mt(NMVOC) NOx 5.6 Mt(NO2) SO Mt(SO2) (kt/1°x1°)

8 Regional differences on the predominant sources over Africa Biofuel BC Fossil fuel BC

9 African BF and FF emissions per regions CO BC NOx NMVOC SO2

10 Comparison with EDGAR 2000 NMVOC : 8,35 Mtons/(5,5-6,7)* Mtons : (-20%)->(-34%) CO : 72,6 Mtons/49,7 Mtons : -31,5%  Less domestic emissions NOx : 4,9 Mtons/5,6 Mtons : + 14% SO2 : 4,17 Mtons/10,1 Mtons : +142% => Important role of South Africa (*depending on NMVOC Emission factors)

11 To conclude African Biofuel and fossil fuel emissions : In AMMA : EF characterization for unknown fuels (zems, trucks…) On going in our programs (POLCA and SACCLAP) : An improvement of African fuel consumption database  UNdata update from African database (in construction) ex : diesel consumption in Ivory Coast : + 200% when considering Africaclean database => phD E. Assamoi  By considering the appropriate fuel consumption (zems, trucks..) We have constructed a flexible and coherent inventory for gases and particles with the same proxidata and assumptions

12 Urban emission characterization at Cotonou - May 2005, August 2006 A fixed station at the most polluted place (Aerosol size, chemical composition, optical properties, CO/CO2…. ) Measurements of emission factors (« zem », trucks…) First example : CO/CO2 = 0.42 EF(Black carbon) = 0.79g/kgdm

13 A mobile experiment : transects in Cotonou with a taxi equipped by different samplers) : high level of concentrations Urban emission characterization at Cotonou - May 2005

14 Biomass burning emissions (savanna, forest and agricultural fires) The most adapted method to derive african BB emissions:  a bottom up method based on satellite burnt area map (see BBSO workshop, Dec. 2005)) Pollutants : BC, OCp, OCtot, CO, CO2, NOx, NMVOC, SO2 and all the species listed in Andreae and Merlet, 2004 Emissions = SB x GLCv x BEv x BDv x EFv SB : area burned => GBA 2000 product (0.5°x0.5°, monthly) => L3JRC products (1kmx1km, daily) GLCv : quantity of vegetation v present in cell (%) => GLC 2000 map (0.5°x0.5°) BEv,BDv : biomass density and burning efficiency by vegetation type EFv : emission factor by vegetation type => An important work based on Liousse et al., 2005, Michel et al., 2006 with inputs of P. Mayaux (Ispra) considering the GLC vegetation types

15 Mouillot Burnt areas AVHRR Burnt areas SPOT GBA Burnt areas ATSR-GBA products Hao statistical data Intercomparison with our other African Biomass burning BC emissions

16 GLC map (Ispra) ( 0.5°x0.5°) Our assumptions…

17 January 2000 December 2000 July 2000August Burned areas (km 2 /0.5°x0.5°) km km km km 2

18 December 2000 Burned BiomassBurned Areas BC CO 448 kt(BC) km Mt(dry matter) 51 Mt(CO)

19 BC (tons/0.5°x0.5°) 01/ / /200008/ kt(BC) BC emissions in TgC in 2000  Suitable comparison between UMD/GLC  High difference if comparing with the old inventory

20 Comparison with EDGAR 2000 CO : 222,5 Mtons/285 Mtons : +28% NOx : 11 Mtons/18 Mtons : + 64% SO2 : 2,03 Mtons/1,9 Mtons : -6,4%

21 African BC emissions by source types Biomass burning Biofuel combustions Fossil fuel combustions 2.28 Mt(BC) 0.44 Mt(BC) 0.20 Mt(BC) 0 BC (tons/1°x1°) These inventories have been tested In ORISAM-TM4 and RegCM3 models

22 All these inventories available for the AMMA people. => Fossil fuel and biofuel : 1°x1°, yearly, => Biomass Burning : 2000 : 0.5°x0.5°, monthly => now : 1kmx1km, daily=> in autumn 2007 (upon request with your needed spatial and temporal scales) What is available?

23 CAPEDB : Fossil fuel and biofuel sources

24 GBBE : Biomass burning sources

25 Model: MesoNH Chemistry. Res: 20km/20km, 100/100 points. 52 vertical levels (surface to 28km). From 05/08 00h to 07/ h. ECMWF every 6h dynamic forcing. Vertical profiles of clean atmosphere for chemical initialisation. RACM chemical scheme. Parameterized convection (Bechtold et al., 2001) and NOx from lightning (Mari et al., 2006). NO source from soil from a Neural Network parameterization (Delon et al., 2007). Impact of NO emissions from soils on ozone formation under tropical conditions. C. Delon*, D. Serça, J.P. Chaboureau, R. Dupont, C.Mari SOIL NOX EMISSIONS in the ANN parameterization are linked to surface temperature and WFPS, deep soil temperature (20- 30cm), fertilization rate, soil pH, sand percentage, and wind speed. pH and soil moisture are the most determinant parameters.

26 Impact on ozone and NOx concentrations NOx concentrations (ppt) 2006/08/06 noon. Vertical cross section along the BAE-227 flight path (NE of Niamey). NOx CONCENTRATIONS from 0 to 2000m without (left) and with (right) NO emissions from soils. Concentrations reach 300 ppt at noon when NO are emitted by the soil (<20ppt if no emissions). Measured NOx concentrations range from 100 to 500 ppt at 500m height. OZONE FORMATION in the lower troposphere (0-2000m) is enhanced by NO emissions from soils (+10 ppb). Measured O 3 concentrations reach 43 ppb. O3 concentrations (ppb) 2006/08/06 noon. The introduction of an on line soil NO emissions calculation in MesoNHC is an important step to improve chemistry description in the lower troposphere. The relation between NO flux and physical and meteorological parameters ensures an immediate impact of NO emissions on ozone levels (not possible with monthly inventories).

27 What is available? Soil NO flux parameterisation: with j=1  7 where x1 to x7 correspond to surface WFPS, surface soil temperature, deep soil temperature, fertilisation rate, sand percentage, pH and wind speed respectively Easily pluggable in regional chemistry transport models.. Available soon: NO flux inventory for the rainy season in West Africa (4-21°N, -5-13°E). Other seasons (dry and transition) will come in 2008.


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