1. Recent developments for carbonaceous aerosol inventories of the 1950-2100 period Liousse C., Guillaume B., Junker C., Michel C., Grégoire J.M. and Cachier H. Control of fossil fuel black carbon may be a cost effective way to reduce global warming emissions in conjonction with abatement of GHG emissions (Jacobson, 2002)

2. Liousse and Cachier, 2005 Climatic impact of carbonaceous aerosols is highly linked to their emission spatial distribution 1970 1997 Estimate of emission inventories still uncertain...

3. The market! Carbonaceous aerosol inventories always incomplete (fossil fuel/ biofuel/biomass burning, Biogenic OC, Primary OC/Total OC..) Confusion between PPOC and POM The two existing global inventory (Bond and Liousse inventories) for Fossil fuel and Biofuel sources of BC and OC in disagreement Regional inventories also in disagreement (Shaap/Liousse for Europe, Carmichael/Liousse/Shekkar Reddy for Asia) June 2002: Workshop in Toulouse to better understand the gaps => main causes : fuel distribution by activities and emission factor choice.

4. Bond Liousse Example for global fossil fuel source BC inventories in 1996

5. Liousse, 2005, IAPSAG book

6. Further in details comparing Bond and Liousse global inventories… Due to the incomplete picture of fuel data and use (including all activities, technology and norms) Due to the lack of experimental EF measurements => REDUCTIONS are needed In Liousse, 3 sectors = trafic/combined, domestic and industrial; 3 groups of countries developed/semi-developed/developing. EF for semi-developed and under-developed => mostly based on projections from developed EF In Bond, IEA fuel use takes into account combustion technology country/country but assumptions performed where no data

7. EF (gC/kgdm)Liousse Bond Domestic Industrial Hard Coal Dev1.39 0.149 3. 0.001-0.006 Hard Coal Underdev.2.28 1.10 3.21 0.28-4.5 Lignite Dev2.5 0.30 0.2 0.015-0.03 Lignite Underdev.4.10 1.98 0.22 0.01 Industrial Hard coal : BC/TPM (Liousse) = 25%; BC/TPM (Bond) = 1% for pulverized coal => Recent experiments in Toulouse BC/TPM=1.5% and EF(BC)= 0.1 (with filters=Dev.?) to 1.14 (no filter=Underdev.?) Industrial Lignite : More incomplete combustion than hard coal but no experiment

8. EF (gC/kgdm)Liousse Bond Traffic Dom. Indus. Diesel Dev. 2 2 0.07 0.8 0.25 0.25 Diesel Underdev. 10 2 0.35 1.31 0.25 0.24 Motor Gasoline Dev 0.03 0.03 0.03 0.08 0.08 0.13 Motor Gasoline Underdev. 0.15 0.15 0.15 0.26 0.08 0.09 Diesel : (x5) (Liousse) between Dev. and Underdev Experimental (Liousse EF Dev.) : 2gC/kgdm? need to be validated.. Motor gasoline : Bond : separation low duty/heavy duty, 2 strokes/4strokes Liousse : a mean value…; 1st experimental values: EF(BC)=0.007-0.2

9. A question of fuel reduction? Exercise at the european scale (25kmx25km) including more details on fuels, activities (technology details) and norms IIASA fuels : Example of details : Industrial : grate firing/fluidized bed/pulverized coal Gasoline : 2/4 strokes Light/Heavy duty Impossible to rely EF for each technologies and norms... Our assumptions based on experimental results* Ref EF(BC) => Liousse « global » … then fuel by fuel, ∆EF(BC) fixed by ∆(CO/CO2) => more CO, more particles… ∆BC/OC fixed by ∆(CO/CO2) => more CO/CO2, more incomplete combustion, less BC/OC, more EF(OC) *Need to be validated for solid fuels Liousse and Guillaume, 2005

10. LA-global 1°x1° LA-Europe-25kmx25km Annual BC : 1.5 TgC Annual OCp : 2.21 TgC Annual BC : 1.21 (1.48) TgC Annual OCp : 1.51 (1.93) TgC (With and Without norms) Shaap et al. : 0.48 TgC

11. Guillaume/Liousse: 1995 controlled Guillaume/Liousse:2010 tons/year Liousse: 1995 not controlled BC;OC 1995 Liousse,2004: 25 countries EU : 816;1117kt/yr 10 other countries: 158;318 kt/yr BC,OC 1995 Guillaume and Liousse, 2004: Not controlled scenario 25 countries EU: 1111; 2217 kt/yr 10 other countries: 178; 360 kt/yr Controlled scenario 25 countries EU: 625; 946 kt/yr 10 other countries: 64; 119 kt/yr 2010 Controlled scenario 25 countries EU: 345; 512 kt/yr 10 countries EU: 68; 109 kt/yr

12. European BC (Shaap et al. 2004) =0.45 Tg => a constant underestimate of BC concentrations over rural areas (due to the sources?, the modeling? European BC (with the assumptions done here) = 1.5 Tg => better comparisons A way to test the emission inventories is to model BC and OC particles Trying to validate with experimental measurements…? Pic du Midi, 3000m high 20022003

13. BF/FF =30% mean 26%=mean in China 60 to 45% in India The global trend of BC emissions (with low injection height)…. BC/prim.OC 1970=>1997 USA/France Liousse and Cachier, 2005

14. Important role of Biofuel? EF revision compared to Liousse et al., 1996 (new measurements) Fuel : UNSTAT database coupled with POLES database (EU model) TgCBCBC Liousse* Global10.25.9 India1.140.7 China1.830.9 *with UNSTAT database and no biofuel Without... With...

15. Trends of fossil fuel BC in Africa Liousse et al., 2004 (50-97)

16. BC/OC Projections SRES-B1/A2 Liousse and Cachier, 2005

17. BC/OC Projections SRES-B1/A2 Liousse and Cachier, 2005

18. BC/OC Projections SRES-B1/A2 Liousse and Cachier, 2005

19. Global BC emissions 1997 2100A2 BC/OC Projections SRES-B1/A2 Liousse and Cachier, 2005

20. Huge differences Between IPCC scenarii. China : 20% of global emissions in 1995 and 2020 whereas 30% in 2100; India : 10% (constant) BC/OC Projections SRES-B1/A2 Liousse and Cachier, 2005

21. BC/OC Projections SRES-B1/A2 TM3 model / comparison with Measurements during INDOEX Liousse and Cachier, 2005

22. NEW projections with scenarios given by POLES model Including both fossil fuel and biofuel emissions with indications of Activity partition = Traffic, Domestic, Industrial Reference scenario : Reflect the state of the world with what is actually (2000) embodied as environmental policy objectives (BAU) CCC scenario : Introduction of carbon penalties as defined by Kyoto for 2010 and a reduction of 37 Gt of CO2 in 2030. EFs for the Reference scenario : equal to today ’s Reduction of EF for the CCC scenario : Developed countries : based on removal efficiency forecast by the IIASA Rains model Semi-Developed countries : EFs of developed countries of 1997 Under-Developed countries : EFs of semi-developed countries of 1997 Thanks to P. Criqui, Grenoble Junker and Liousse 2005

23. BC(TgC) POLES,UNIPCC 2000 12.4 2030ref 13.7 2020A2 25 2030ccc 5.8 2100 A2 100 2100 : next 2100 B1 0.6 2030ref 2030ccc IPCC-2100A2 Liousse and Cachier, 2005 Future BC projections at the global scale Junker and Liousse 2005

24. WORLDEUROPE CHINAINDIA BC Trends 1997-2000-2030ref and 2030cc Junker and Liousse 2005

25. To conclude : Disagreement on the EFs needs to be resolved (on going analysis should help) with a strong link with the experimental teams (BC/OC Problem of analysis) Need to find consistency between the reduction factors applied to Fuel/activity/technology/norms => one of the ACCENT activity for 2005

26. Biomass burning emissions Burnt biomass: determined from statistical data (Hao et al., 1991) = a factor of 2 to 3 of uncertainty. Improvement has been found by coupling both satellite tools (fire pixe counts and burnt areas) (Liousse et al. 2004, IGAC book on emissions,Michel et al., 2005, JGR). Emission factors = revision has been done for consistency (see Liousse et al., 2004) ACCESS/ABBI (Michel et al. 05) (TRACE P and ACE ASIA period)

27. Determination of african BC emissions from 1981 to 1991 ( Liousse et al., 2004 )

28. A comparison (a way to validate) : Modeled BC / TOMS index? Z > 2500 Agreement only during peak of dry season Relationship between emissions and ENSO not seen with TOMS signal West Africa Liousse et al., forthcoming

29. BC (Modeled/Exp. Data) at Lamto, Ivory Coast Model underestimate Model agreement Need more data Liousse et al., forthcoming

30. To conclude with BC and OC trends from biomass burning emissions : A review : Liousse et al. 2004 IGAC book on Emissions. Present BB : Use of satellite data very useful ; more uncertain parameters are now biomass density and pollutant injection height. ACCENT Workshop on Biomass burning : fall 2005 A complex link between climatological factors, population activities.. (see emission variations from 1980-1991) Derivation of BB inventories from the past to the Future : not to be scaled on population variations Regional : next for Africa : in the frame of AMMA => 2005-2007 = a weekly regional inventory for gases and particles from SPOT satellite data

31. Caution = confusion between POC and TOC POC inventories : what plenty of people have now.. needed for model with an aerosol module Allowing to create ASOA and BSOA particles (also needed Anthropogenic and Biogenic VOCs) TOC inventories : needed for model without aerosol module In Liousse et al., 1996 : TOC inventory for fossil fuel sources was obtained from BC inventory and a constant BC/TOC ratio measured at a distance of the source area (in order to include secondary formation of organics) TOC inventory for biomass burning sources was obtained from airplane data

32. Example of results with the ORISAM 0D-Module (Organic and Inorganic Spectral Aerosol Module) BC,OCp, other SO 4 2 - NO 3 - HSO 4 - H+H+ NH 4 + H20H20NH 3 H 2 SO 4 HNO 3 PhotoX Condensable org. VOCsSOA SO 2 NOx A variable POC/TOC ratio is obtained from simulations using a OD aerosol module (high dependancy on sources and temperature) => global distribution map of this ratio is in construction…. (Liousse et al., 2005)