Recent developments for carbonaceous aerosol inventories of the 1950-2100 period Liousse C.,...

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)

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TOA DRF for BC and OC in 1970 (W/m2)

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TOA DRF for BC and OC in 1997 (W/m2)

Liousse and Cachier, 2005

Climatic impact of carbonaceous aerosolsis highly linked to their

emission spatial distribution

1970

1997

Estimate of emission inventories still uncertain...

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) forFossil 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.

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BC1996cooke

Bond

Liousse

Example for global fossilfuel source BC inventories

in 1996

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CL

DL

LB

CL

DL

LB

CL

DL

LB

CL

DL

LB

Bond

Liousse

WORLD

EUROPE

INDIA

CHINA

BC TgC/yr

Liousse, 2005, IAPSAG book

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

EF (gC/kgdm) Liousse BondDomestic Industrial Domestic Industrial

Hard Coal Dev 1.39 0.149 3. 0.001-0.006Hard Coal Underdev. 2.28 1.10 3.21 0.28-4.5Lignite Dev 2.5 0.30 0.2 0.015-0.03Lignite 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

EF (gC/kgdm) Liousse BondTraffic Dom. Indus. 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 0.24Motor Gasoline Dev 0.03 0.03 0.03 0.08 0.08 0.13Motor 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/4strokesLiousse : a mean value…; 1st experimental values: EF(BC)=0.007-0.2

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 coalGasoline : 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 incompletecombustion, less BC/OC, more EF(OC)

*Need to be validated for solid fuelsLiousse and Guillaume, 2005

LA-global 1°x1° LA-Europe-25kmx25km

Annual BC : 1.5 TgCAnnual OCp : 2.21 TgC

Annual BC : 1.21 (1.48) TgCAnnual OCp : 1.51 (1.93) TgC(With and Without norms)

Shaap et al. : 0.48 TgC

Guillaume/Liousse: 1995 controlled

Guillaume/Liousse:2010

tons/year

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/yr10 other countries: 178; 360 kt/yr

Controlled scenario25 countries EU: 625; 946 kt/yr10 other countries: 64; 119 kt/yr

2010 Controlled scenario

25 countries EU: 345; 512 kt/yr

10 countries EU: 68; 109 kt/yr

European BC (Shaap et al. 2004) =0.45 Tg => a constant underestimateof 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

2002 2003

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10000000BC (biofuel only)

BC (total)

BF/FF =30% mean26%=mean in China60 to 45% in India

The global trend of BC emissions (with low injection height)….

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1970 1975 1980 1982 1984 1986 1988 1990 1992 1994 1996 1997

years

USAFrance

BC/prim.OC

1970=>1997USA/France


Important role of Biofuel?

EF revision compared to Liousse et al., 1996 (new measurements)Fuel : UNSTAT database coupled with POLES database (EU model)

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BC1997og

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BC1997nu

TgC BC BCLiousse*

Global 10.2 5.9India 1.14 0.7China 1.83 0.9

*with UNSTAT database and no biofuel

Without...

With...

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

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BENIN

IVORYCST

SENEGAL

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BC en Tonnes de carbone

What to do to obtain fuel consumption in 2100 ?

1989 A? 2100

- for 23 different fuel

- for 177 countries

A = factors given by IPCC report for each scenario; for the 3 fuel

groups (solid, liquid and gazeous fuels) and for the 4 countries groups

(OECD, REFs-EFSU, ASIA, Rest of the world).

These factors applied to the 23 fuels and the 177 countries.

Example for Asia country group. Variation from 1989 to 2020 and 2100.

Solid fuel Liquid fuel

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1989 2020 21000

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1989 2020 2100

A2 scenario

B1 scenario

BC/OC ProjectionsSRES-B1/A2


What to do to obtain BC emissions in 2100 ?

B ?

Fuel consumption in 2100 BC emissions in 2100

B ?No data given by IPCC report.

Estimations based on existing data for 1989and scenario description.

We define B as B= U% x EF(BC)

where U% is the percent of fuel devoted to combined, industrial ordomestic usage. EF(BC) is the emission factor for particles ing/kgfuel depending on the usage type. Both are depending on

country development.

Example for diesel :

Combined I ndustrial Domestic

U%

Combined I ndustrial Domestic

EF(BC)

Estimation perf ormed for the 23 fuels

89 89 89% B1 A1 B2 A2 % B1 A1 B2 A2 % B1 A1 B2 A2

DL d 60 30 40 40 50 25 70 50 50 40 15 0 10 10 10sd 30 30 40 40 60 35 70 50 50 25 35 0 10 10 15ud 80 30 40 80 80 0 70 50 0 0 20 0 10 20 20

2100 2100 2100

89 89 89EF B1 A1 B2 A2 % B1 A1 B2 A2 % B1 A1 B2 A2

DL d 2 0,040,040,4 0,80,070,040,040,040,072 2 2 2 2sd 10 0,040,040,8 2 0,090,040,040,070,092 2 2 2 2ud 10 0,040,0410 10 0,350,040,040,040,352 2 2 2 2

2100 2100 2100



Example of coal in developing countries

U% ?

I n 1989 : 95 % of fuel devoted to domestic usage and5% to industrial sett ings.

Our estimation in 2100 :f or the A2 scenario : same distributionfor the B1 scenario : opposite distribution (95% f orindustrial usage and 5 % for domestic settings)

EF(BC) in g/kgf uel ?

I ndustrial Domestic1989 1 2.2

2020 A2 1 4.52100 A2 1 4.5

2020 B1 0.25 32100 B1 0.1 1.5



0 1250 2500 3750 5000Bc in metric-ton/yr

0 125000 250000 375000 500000BC in metric-tons/yr

Global BC emissions

1997

2100A2



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1960 1970 1975 1980 1985 1990 1995 1997 2020A2 2100A2 2100B1

100 TgC

25 TgC

BC in TgC

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1970 1975 1980 1985 1990 1995 1997 2020A2 2100A2 2100B1

years

BC China

BC India

28I10

Huge differences Between IPCC scenarii.China : 20% of global emissions in 1995 and 2020 whereas 30% in 2100; India : 10% (constant)



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moysagarmr70

moysagarmr80

moysagarmr89

moysagarmr97

exp Bcvalues in µg/m3

BC2100A2


TM3 model / comparison with Measurements during INDOEX


NEW projections with scenarios given by POLES model

Including both fossil fuel and biofuel emissions with indications of Activity partition = Traffic, Domestic, IndustrialReference 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 for2010 and a reduction of 37 Gt of CO2 in 2030.EFs for the Reference scenario : equal to today ’sReduction of EF for the CCC scenario : Developed countries : based on removal efficiency forecast by the IIASA Rains modelSemi-Developed countries : EFs of developed countries of 1997Under-Developed countries : EFs of semi-developed countries of 1997

Thanks to P. Criqui, Grenoble

Junker and Liousse 2005

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BC2030rf

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BC2030cc

BC(TgC) POLES,UN IPCC

2000 12.42030ref 13.7 2020A2 252030ccc 5.8 2100 A2 1002100 : next 2100 B1 0.6

0 50000 100000BC 2100 A2

2030ref

2030ccc

IPCC-2100A2Liousse and Cachier, 2005

Future BC projections at the global scale


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CHINA INDIA

BC Trends 1997-2000-2030ref and 2030cc


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 toFuel/activity/technology/norms

=> one of the ACCENT activity for 2005

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)

BC (ACESS) 1-10 March 2001



BC (ACESS) 1-10 April 2001

BC (ABBI) 1-10 March 2001

BC (ABBI.) 11-20 March 2001

BC (ABBI) 21-31 March 2001

BC (ABBI.) 1-10 April 2001

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BC emissions (tonnes)

March 1-10 March 11-20 March 21-31 April 1-10 April 11-20 April 21-30 May 1-10

period

ACESS ABBI

ACCESS/ABBI (Michel et al. 05)(TRACE P and ACE ASIA period)

Annual BC emissions

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1981-1982 1982-1983 1985-1986 1986-1987 1987-1988 1988-1989 1989-1990 1990-1991 from Hao etal. 1991

Determination of african BC emissionsfrom 1981 to 1991 (Liousse et al., 2004)

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nov-81mars-82

juil-82nov-82mars-83









juil-91

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4,00E-01

6,00E-01

8,00E-01

1,00E+00

1,20E+00

1,40E+00

1,60E+00

WATM3

WATOMS

A comparison (a way to validate) : Modeled BC / TOMS index?

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col

row

0.0e+00 2.5e-08 5.0e-08 7.5e-08 1.0e-07

BC conc (g/m3)

Z > 2500

•Agreement only during peak of dry season•Relationship between emissionsand ENSO not seen with TOMS signal

West Africa

Liousse et al., forthcoming

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nov-85fév-86mai-86aoû-86nov-86fév-87mai-87aoû-87nov-87fév-88mai-88aoû-88nov-88fév-89mai-89aoû-89nov-89fév-90mai-90aoû-90nov-90fév-91mai-91aoû-91nov-91fév-92mai-92aoû-92nov-92fév-93mai-93aoû-93

lamto mod lamexp

BC (Modeled/Exp. Data) at Lamto, Ivory Coast

aug sep oct nov dec0,0

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zammext

zammint

Model underestimate

Model agreementNeed more data

Liousse et al., forthcoming

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 uncertainparameters are now biomass density and pollutant injectionheight. ACCENT Workshop on Biomass burning : fall 2005

A complex link between climatological factors, populationactivities .. (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

Caution = confusion between POC and TOC

POC inventories : what plenty of people have now..needed for model with an aerosol moduleAllowing to create ASOA and BSOA particles (also neededAnthropogenic 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

Example of results with the ORISAM 0D-Module (Organic and Inorganic Spectral Aerosol Module)

BC,OCp,otherSO4

2-NO3

-

HSO4-

H+

NH4+

H20 NH3

H2SO4

HNO3PhotoX

Condensable org.VOCs SOA

SO2

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)

Recent developments for carbonaceous aerosol inventories of the 1950-2100 period Liousse C.,...

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