Modeling African aerosol using updated fossil fuel and biofuel emission inventories for 2005 and 2030

Liousse C. and E. Assamoi

Thanks to different african institutes and persons for all the regional emission data.

  First AMMA modeling (ORISAM-TM4 & RegCM) have used global fossil fuel and biofuel source inventories to study african aerosol impact with problems to reconstruct AOD along the Guinean Gulf

  Preliminary…

DIESEL CONSUMPTION BETWEEN TWO DATABASE : UN (GLOBAL) AND AFRICACLEAN (REGIONAL)

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UN AFRICACLEAN

 Important discrepancies between global inventory and regional zoom obtained with Africaclean database for the traffic emission inventory

  Discrepancies on fuel consumption

FF BC BF BC Existing global inventories

  Purpose

  Develop a new regional anthropogenic inventory (BC and OC) with regional african specificities and a new spatialization for 2005.

  Test this new inventory in ORISAM-TM5 against available measurements.

  Projections in the future with associated modeling tests; our concern : the expected population increase …..

Population

1960

2020

Methodology to develop african fossil fuel and biofuel inventories for 2005

Data consumption : Local inquiries including Africaclean results (see diesel data) Where no available : - United Nations database for fossil fuel (industry and domestic) - International Energy Agency data (power plants) - updated Brocard (1996) for biofuel (fuelwood and charcoal)

Assamoi & Liousse (2009) for two-wheel inventory with the maximum scenario (with measured EF)

Emission factors depending on fuel/activity sectors/technology A proxy method for technology and norms by using GDP : semi developed/developing countries Data from : AMMA 2005 campaign in Cotonou (Guinot et al. 2009)

Junker and Liousse 2008 Bond et al. 2004

⇒ A bottom up inventory : E(kg)/country = C(t)/sector x EF(kg/t) With activity sectors : traffic/domestic/industry/power plant

« Regional » resolution grid for mapping emission (country=>regional distribution) :

  0.25°x0.25°(SEDAC : http://sedac.ciesin.columbia.edu/)

C D IALGERIA REG REG REGANGOLA UN UN UNBENIN REG REG UNBOTSWANA UN UN UNBURKINA_FASO REG REG REGBURUNDI UN UN UNCAFRREP UN UN UNCAMEROON REG UN UNCAPEVERDE UN UN UNCHAD REG UN UNCOMOROS UN UN UNCONGO UN UN UNDJIBOUTI UN UN UNEGYPT REG UN UNEQGUINEA UN UN UNETHIOPIA UN UN UNGABON UN UN UNGAMBIA UN UN UNGHANA REG UN UNGUINEA REG UN UNGUINEA-BS REG UN UNIVORYCST. REG REG REGKENYA UN UN UNLIBERIA REG UN UNLIBYA UN UN UNMADAGASCR UN UN UNMALAWI UN UN UNMALI REG UN UNMAURITANI UN UN UNMAURITIUS UN UN UNMOROCCO REG REG REGMOZAMBIQU UN UN UNNAMIBIA UN UN UNNIGER REG UN UNNIGERIA REG REG REGREUNION UN UN UNRWANDA UN UN UNSAFRICA UN UN UNSAOTOME& UN UN UNSENEGAL REG UN UNSEYCHELLS UN UN UNSIERRALEO REG UN UNSOMALIA UN UN UNSTHELENA UN UN UNSUDAN UN UN UNSWAZILAN UN UN UNTANZANIA UN UN UNTOGO REG UN UNTUNISIA UN UN UNUGANDA UN UN UNW.SAHARA UN UN UNZAIRE UN UN UNZAMBIA UN UN UNZIMBABWE UN UN UN

PAYSSOURCE CONSO

Focus on Western African fossil fuel emissions

Following a questionary sent to all african countries here are the fuel consumptions we collected => (11 answers/26 african countries)

Future BC and OC projections over Africa

New projections by using our present new regional inventory of 2005 and the POLES model (Criqui et al.) including both fossil fuel and biofuel emissions (fuel/activity).

Reference scenario : Reflect the state of the world with what is actually (2000) embodied as environmental policy objectives 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

Regional distribution of OC emissions

2005old

1.23 TgC

2005new

4.2 TgC

2030ref

8.4 TgC

2030ccc

7.1 TgC

Need further sensitivity tests e.g. in South Africa

For BC : BC old : 0.80 TgC BC new : 0.82 TgC

OLD UN NEW REG CHANGEALGERIA 5194,53 6418,39 0,24BENIN 526,78 6305,62 10,97BURKINA_FASO 322,83 1716,87 4,32CAMEROON 1202,43 1639,51 0,36CHAD 95,02 147,83 0,56EGYPT 29313,70 47613,26 0,62GHANA 2019,91 2805,84 0,39GUINEA 226,94 603,21 1,66GUINEA-BS 56,98 86,76 0,52IVORYCST. 1025,37 1731,19 0,69LIBERIA 65,32 109,78 0,68MALI 258,45 2514,30 8,73MOROCCO 2742,73 5405,56 0,97NIGER 426,14 512,48 0,20NIGERIA 9140,94 37892,97 3,15SENEGAL 1398,47 1355,32 0,00SIERRALEO 122,47 239,93 0,96TOGO 342,00 1603,53 3,69

OC emissions (tons/year)COUNTRIES

  African BC and OC emissions   Biomass burning : AMMABB (L3JRC products : Liousse et al., 2009)

  Fossil fuel and biofuel : Junker and Liousse (2008) / our new inventories for 2005 and 2030

  Augmented with ORISAM (trace mode)   6 Size distribution aerosol mass concentrations in a sectional aerosol model

[0.04-0.126mm;0.126-0.4mm;0.4-1.265mm;1.265-4mm;4-12.649mm;12.649-40mm].

  in this run : 4 aerosol species : BC hydrophilic; BC hydrophobic; OC hydrophilic; OC hydrophobic

  TM5 Version standard   6°x4° global, 3°x2° and 1°x1° over Africa

  25 vertical levels : 1010 hPa to 0.47 hPa

  Off-line meteo (ECMWF) : the same for present and future simulations

  Wet deposition by stratiform/convective form

ORISAM (ORganic and Inorganic Sectional Aerosol Model) –TM 5 (Chemistry transport model) model

first simulations !

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BC (tons/1°x1°)

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2.28 Mt (BC)

Modeling results (ORISAM-TM5) in January (old vs. new)

BC : Concentrations increase along the Guinean Gulf max concentration x 1.6

OC : A new pattern along the Guinean Gulf

max concentration x 4.3

Same pattern over biomass burning areas for BC and OC

BC (2005)

OC (2005)

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Results of modelisation (ORISAM-TM5) in July (old vs. new)

BC : Change mainly appearing over Nigeria : max concentration x 1.5

OC increase in Southern Africa (due to sources improvements and new spatialization) : max concentration x 2.2

Same pattern over biomass burning areas for BC and OC

BC (2005)

OC (2005)

First simulations : global ORISAM-TM5

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Ouagadougou

At this scale, we don’t expect to satisfactorily compare : regional modeling is on going with RegCM model (F. Tummon phD).

Modeling BC results (ORISAM-TM5) in 2030ref

A new hot spot of emissions along the guinean golf associated with the south african hot spot

Modeling Results at different sites

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CONCLUSION   Modeling Tests with new african BC and OCp inventory are encouraging (new spatialization and improvements on fossil fuel and biofuel emissions).

  Future projections provide a new hot spot of emissions along the guinean golf associated with the south african hot spot. Sensitivity test with 2030ccc and a new 2030ccc with improved projections for South Africa.

For the next…

  Use the full ORISAM-TM5 (dust, SOA, sulphate, coarse nitrate, ammonia,…) to further compare modeled and measured aerosol optical depth.

  Develop the flaring emission inventories (Nigeria). Improvement of mapping emissions.

  POLCA Experimental data : on going analysis of Bamako experiment (january 2009) ; next campaign in Dakar in november 2009 (phD T. Doumbia). Next in AMMA2/IDAF : network of urban data (Bamako, Ouagadougou, Abidjan, Yaoundé, Lagos). Next with Mauricio : Accra, Lagos?

 On going regional modeling with RegCM model (Tummon et al., Konaré et al. ..)

The aim : linked to these projects, a group of african experts on emissions/modeling is under construction.

West Africa : University of Cocody (Ivory Coast), University of Niamey, University of Mali, University of Cameroon,University of Dakar, University of Benin, University of Ouagadougou, University of Nigeria

South Africa : North West University, Cape town University

Results of modelisation (old and new) vs. measurements (2005) 3/4

Results of modelisation (old and new) vs. measurements (2005) 4/4