Fossil fuel, Biofuel and Biomass Burning emissions for 2000-2007 (trace gases and particles). C. Liousse, B. Guillaume, A. Konaré, C. Junker, C. Granier, J.M. Grégoire, A. Poirson and E. Assamoi. Fossil fuel and Biofuel emissions.
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C. Liousse, B. Guillaume, A. Konaré, C. Junker, C. Granier, J.M. Grégoire, A. Poirson and E. Assamoi
++ 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
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
Emissions are fuel-dependant, fuel usage-dependant and technology-dependant
Our « lumping » :
=> 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
EF values for gases
CO, NOx, SO2, NMVOC
EF values for Black carbon and primary organic carbon
Year 2000 Africa
Fossil fuel and biofuel combustions
Year 2000 Africa
Biofuel and Fossil fuel combustions
Fossil fuel BC
=> Important role of South Africa
(*depending on NMVOC Emission factors)
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
ex : diesel consumption in Ivory Coast : + 200% when considering Africaclean database => phD E. Assamoi 2007-2010
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
Urban emission characterization at Cotonou - May 2005
A mobile experiment : transects in Cotonou with
a taxi equipped by different samplers) : high level
The most adapted method to derive african BB emissions:
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 2000-2007 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
Intercomparison with our other
African Biomass burning BC emissions
Hao statistical data
Mouillot Burnt areas
AVHRR Burnt areas
SPOT GBA Burnt areas
GLC map (Ispra) ( 0.5°x0.5°)
Burned areas (km2/0.5°x0.5°)
723 Mt(dry matter)
BC emissions in TgC in 2000
African BC emissions by source types
These inventories have been tested
In ORISAM-TM4 and RegCM3 models
What is available?
All these inventories available for the AMMA people.
=> Fossil fuel and biofuel : 1°x1°, yearly, 2000-2003
=> Biomass Burning :
2000 : 0.5°x0.5°, monthly => now
2000-2007 : 1kmx1km, daily=> in autumn 2007
(upon request with your needed spatial and temporal scales)
Fossil fuel and biofuel sources
Biomass burning sources
Impact of NO emissions from soils on ozone formation under tropical conditions.
C. Delon*, D. Serça, J.P. Chaboureau, R. Dupont, C.Mari
Model: MesoNH Chemistry.
Res: 20km/20km, 100/100 points.
52 vertical levels (surface to 28km).
From 05/08 00h to 07/08 2006 00h.
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).
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.
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 O3 concentrations reach 43 ppb.
NOx concentrations (ppt) 2006/08/06 noon.
Vertical cross section along the BAE-227 flight path (NE of Niamey).
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).
O3 concentrations (ppb) 2006/08/06 noon.
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.