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Emissions from residential Emissions from residential energy useenergy use
Chandra VenkataramanDepartment of Chemical EngineeringIndian Institute of Technology, Bombay
TF HTAP Emissions Inventory and Future Projections Workshop
October 18-20, 2006, Beijing
AcknowledgmentsAcknowledgments
•Organizers: for invitation / hospitality and defining workshop issues.
•Collaborators: Gazala Habib, Shekar Reddy, ShubhaVerma, Manish Shrivastava, Baban Wagh, IIT Bombay; Antonio Miguel, Arantza Fernandez, Sheldon Friedlander, UCLA; Tami Bond, UIUC; Jamie Schauer, U Wisc Madison.
•Funding Support: ISRO-GBP, MHRD.
• The source of the problem (or problem with the source).• Emitted pollutants of regional / global
relevance.• Inventory methodology.• Uncertainties and their containment (more than
mere reduction).• Transport pathways – South Asia.• Recommendations.
OutlineOutline
• Energy consumed by households excluding transportation: includes cooking, home heating / air-conditioning, lighting and home appliances.
Residential energy Residential energy
•Commercial energy accounting excludes about two billion people who rely on solid biofuels for residential energy.
International energy outlook, 2006
Residential energy use trends Includes:OilNatural GasElectricityCoal
Evidence of long range transportEvidence of long range transport
120
100
80
60
40
20
cm-3
Spatial distribution of black carbon containing particles with potassium from TOFMS (Guazzotti et al., 2003, JGR).
• Particles (median aerodynamic diameters 0.5-1 μm).• Particle constituents (OC, BC, inorganic ions).• Gases: CO, VOCs (ozone/PM precursors), N2O, CH4.• Organics (gas and particle phase) – hundreds of organic
compounds including formaldehyde, benzene, polycyclic aromatic hydrocarbons, laevoglucosan (sugar anhydrides) substituted phenols, guaiacyl / syringyl compounds, sterols.
Regional / global effects from long-range transport
• Air quality – PM, VOCs, organics (POPs?).• Visibility – PM, BC, ions.• Radiation / climate – BC, OC, GHGs, extinction cross-
section.
Pollutants in Pollutants in biofuelbiofuel smokesmoke
Emissions inventory methodologyEmissions inventory methodology• Activity levels (usually fuels) (kg day-1)
Per capita usage, user population, fuel-mix.
• Technology divisionsDevices, efficiency (thermal and combustion).
• Emission factors (g kg-1)Pollutants of interest for each fuel-technology system.
• Spatial distribution and resolutionAppropriate proxy – typically population.
• Temporal resolution and seasonal cycleFoods / fuels may vary with season.
Activity levels (kg day-1, national / distributed)• Energy and fuel-use surveys : high uncertainty and low representative-
ness for biofuels (kg capita-1 day-1).• User population : not documented. • Mix of fuels: not known in most cases.
Uncertainties in fuel useUncertainties in fuel use
Fuel-technology divisions • Wood, dung, crop waste, mixed-fuels, ...• Traditional open-combustion chamber, massive mud, bucket with grate, packed bed (rice husk), …
Statewise cooking energy consumption from 6 fuel types (PJy-1)
Specific cooking energy (MJ kg-1 of food cooked)
Statewise cooking fuel use for 6 fuel types (Tgy-1)Statewise cooking fuel use for 6 fuel types (Tgy-1)
Statewise average food items consumed (kgc-1m-1) (NSS, 2001)
Cooking device efficiencies (%)
Calorific value (MJ kg-1)
Statewise rural and urban fraction of fuel user population for 6 fuel types (Fuelwood,
dung-cake, crop waste, coal, LPG and kerosene) (NFHS, 2000)
Statewise population using 6 fuel types
Statewise rural, urban population (Census, 2001)
Statewise end use energy (PJy-1) in four cooking process and 6 fuel types
Statewise food items consumption in 4 cooking process (boiling, skillet-baking,
leavened-baking, meat cooking)
Uncertainty containment: activity levelsUncertainty containment: activity levels
Uncertainties in regional Uncertainties in regional biofuelbiofuel useuseFuelwood Dung-cake Crop waste
Crop waste open burning Forest fire
0 50 100 150 200 250 300
278
63
37
168
39
6%11%
47%
29%
7%
0 50 100 150 200 250 300Biomass burning (MTy-1)
Bio
mas
s ty
pe
Biofuel combustion: 379-555 MTy-1
Biofuels379 MTy-1
•Fuel mix: 73:17:10% of FW:DC:CW. •Upper bounds derived, as μ + 95%CI, within factor of 1.5.•Spatial variability on 25 km grid.
Fuelwood278 MTy-1
Dung-cake 63 MTy-1
Crop waste 37
MTy-1
Global Global biofuelbiofuel useuse
0
500
1000
1500
2000
2500
3000
3500
4000
1850 1875 1900 1925 1950 1975 2000
biof
uel c
onsu
mpt
ion
(Tg/
yr)
Industrial BiofuelsDomestic CharcoalDomestic DungDomestic CropsDomestic Fuelwood
Courtesy David Streets, Argonne National Labs, USA
Emission factors : in laboratoryEmission factors : in laboratoryFuel-technology divisions
Traditional single pot mud stove5-wood species, animal dung and
10-crop waste typesDilution sampler
Optimized for aerosol stabilizationMass of fuel, duct velocity,
temperatures in combustion zone, duct and plenum recorded each minutePollutants
PM2.5: Cyclone sampler.OC-BC: Thermal optical
transmittance (S. California Particle Centre and Supersite).
SO2, NO2, ions, trace elements and absorption (U Wisc, Madison, UIUC).
Dilution sampler Burn cycle
Multi-stream aerosol sampler
AIHL Cyclone
Equilibration cylinder
Inlet for air
Filter holders Cyclone outlet pipe
Connection to PumpCritical Orifices for flow control
Venkataraman et al. Science, 2005, 307, 1424-1426. Habib et al., in preparation, 2005.
Variability across fuels: PM emissionsVariability across fuels: PM emissions
0 2 4 6 8 10 12 14 16
Wood-LBR
Wood-HBR
Fibrous hollow stalks
Woody stalks
Straws
Dried cattle manure
Kerosene
LPG
Fireplaces
Forest fire
Diesel
Sour
ce c
ateg
orie
s
Emission factors (gkg-1)
ECOCAssociated organic matterIonsTrace metal
Variability across fuels: Variability across fuels: Mass absorption crossMass absorption cross--sectionsection
0 1 2 3 4 5 6 7 8
Mass absorption cross section [m2(gPM2.5)-1]
Wood-LBR
Wood-HBR
Fibrous hollow stalks
Woody stalks
Straws
Dried cattle manure
Kerosene
LPG
Fireplaces
Forest fire
Diesel
So
urc
e c
ate
go
ries
Do stoves measured in the field perform Do stoves measured in the field perform differently?differently?
Emission Factors (mean +/- 1 st. dev.)
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0
Honduras 2004 Traditional Stove - field data
Honduras 2005 Improved Stove w/o chimney - fielddata
Honduras 2005 Improved Stove with chimney - fielddata
Aprovecho 2005 Trad. & impr. Cookstoves- lab data
Brocard et al (1996) trad. African stoves -simcooking in field
Zhang et al (2000) trad. & impr. Cookstoves in lab
Venkataraman & Rao (2001) trad. & impr. woodstove in lab
Smith et al (2000) trad. & impr. wood stoves - labtest
Emission Factor (g/kg_wood)
Previous WorkARACHNE results
Yes, there is a big difference between lab and field measurements.
Courtesy Tami Bond, University of Illinois, USA
Integrated uncertainties in pollutant emissionsIntegrated uncertainties in pollutant emissions
BC Fuelwood: 137Ggy-1
BC Dung-cake : 8 Ggy-1
5 10 20 30 60 90kgkm2y-1
5 10 20 30 60 90kgkm2y-1
BC Crop waste : 19
Ggy-1
5 10 20 30 60 90kgkm2y-1
Fuelwood Dung-cake Crop waste
Fossil fuelForest fire
Crop waste open burning
0 20 40 60 80 100 120 140 160
Fuelwood
Dung-cake
Crop waste
BC emissions (Ggy-1)
Bio
fuel
type
Biofuel38%
6%
0 20 40 60 80 100 120 140 160
Biofuel BC: 175-360 Ggy-1
30%
26%
•Upper bounds derived, as μ + 95%CI, within factor of 2.5.•Larger range from more uncertain emission factors.
Level I: In-field monitoring
Level II: Regional design & testing lab
Confirm effectiveness of installed interventionsProvide rapid feedback to entrepreneurs
Motivation: Quantify local benefits
Provide independent evaluation of stove designsDetermine best practices for local conditions
Motivation: Evaluate program success & potential for change
Level III: University LaboratoryCompare costly with less-expensive measurements
Understand nature and causes of emissionsMotivation: Scientific understanding
AHDESA, HondurasTrees Water & People, USA
Aprovecho, USA
Uncertainty containment: emission factorsUncertainty containment: emission factorsCourtesy Tami Bond, University of Illinois, USA.
Transport pathways: seaTransport pathways: sea--breeze breeze during the NE monsoon, India (LMDZT model)during the NE monsoon, India (LMDZT model)
1000 hPa12 GMT
Feb Mar
Vertical velocityPa s-1
Aerosol lofting during sea and land breeze, Aerosol lofting during sea and land breeze, March 21March 21--25, 199925, 1999
Sea breeze
Landbreeze
μg m-3
140 km
ModellingModelling with region tagged emissionswith region tagged emissions
AFWAAFWA
ROWROW
EAEA
SEASEA
SISI
CNICNI
NWINWI
IGPIGP
Fraction from biofuelFraction from biofuel
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0Bond, Venkataraman and Masera, 2004
Scales of transport of residential Scales of transport of residential biofuelbiofuelemissions: MATCH model, 2001emissions: MATCH model, 2001
Question: how much of indoor biofuel smoke penetrates to the outside?
• Need local measurements of simultaneous indoor / outdoor concentrations.
• Local-scale grid model and / or receptor model to estimate indoor source contribution to the outdoors.
Local / regional air quality linkage to HTAPLocal / regional air quality linkage to HTAP
Current InventoriesCurrent InventoriesGeographical and temporal resolution for modellingHTAP:- Regional inventories show spatial features at 25 km resolution (e.g Habib et al., 2004; Venkataraman et al., 2005). Global would need resolution at or finer than wind fields used in driving models (100-200 km?).
-Monthly mean temporal resolution to capture seasonal variations in food-fuels, and space heating needs.
-Regional inventories (Streets et al, 2003; Habib et al., 2004; Venkataraman et al., 2005) can be nested / merged with global ones (e.g. Bond et al., 2004 for BC). Compatible in uncertainties.
-Upcoming global biofuel trends inventory (Streets et al., 2006)indicates need for long-term accounting.
Inventory improvementInventory improvement• Global activity database linked to survey data
(stove, fuel, food).- Food consumption statistics and measurements cooking energy may be more robust than fuel use surveys.
• Emission factors - Need to reconcile lab and field measurements.- Need University-NGO collaboration to make statistically valid measurement datasets of emissions.- Need to leverage health study networks to make local-global link.- Need simple automated field instruments that can be widely deployed.
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