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Road transport‘s emission inventory for the year 2000
Jens Borken & Heike Steller - DLR – Transportation Research, Berlin/DETamas Meretei - KTI – Transportation Research Institute, Budapest/HUFilip Vanhove - TML – Transport & Mobility Leuven/BEwith contributions of S. Baidya and S. Martin (DLR)
with financial support from the EU FP6 within the IP QUANTIFY (www.pa.op.dlr.de/quantify)
2 > GEIA Paris 29.11.2006> Borken
Emission inventory for road transportEmission inventory from road transportation, here for the year 2000.
i.e. all movements of motorised vehicles on public roads; Differentiated by 5 vehicle categories and 4 fuel types.
Tail-pipe exhaust emissions: CO2, SO2, CO, VOC, NMVOC, CH4, NOx, prPMnot included (in this version):
Evaporative emissions (VOC); Brake, clutch + tyre wear, resuspension (PM); Discharges from accidents, maintenance, end-of-life (HFCs)
All countries on the world & on 1°x1° grid
~ to population density with modifications for each region and vehicle category, e.g.: Heavy duty trucks: ~90% in rural areas; mopeds: ~90% in urban areas.
3 > GEIA Paris 29.11.2006> Borken
0
5
10
15
20
25
30
1970 1975 1980 1985 1990 1995
10^1
5 kg
CO
2
8
9
10
11
12
13
14
15
16
% roadtotal
Importance of road transport‘s emissions for climate change
Growing absolute and relative emissions from road transport.
Share of … in 2000: Maritime: 3%, Aviation: 2%
How big climate impact of indirect greenhouse gases?
Data: EDGAR 3.2
4 > GEIA Paris 29.11.2006> Borken
Bottom-up (Tier 2), starting from transport activity for 2*5 vehicle categories, on country level:
Passenger tr. Freight transport FuelsCars Light duty vehicles Gasoline- LPG/CNG - (m)ethanolBus Heavy duty vehicles Diesel - biodiesel2 wheelers
activity data per country by TML Leuven (Vanhove et al.)
emission factors per region by KTI Budapest (Meretei et al.)relative to test cycle; regular maintenance assumed: Probably lower estimate
Essential validation: Fuel sales statistics per country/region! (IEA with modifications)
For many countries this is the first calculation for several exhaust gases at thisdifferentiation.
Activity based approach scaled to fuel consumption
5 > GEIA Paris 29.11.2006> Borken
0
500
1'000
1'500
2'000
2'500
NAMEU15
LAM
EASJP
NMEASEASASCISAFRCECOCN
bio vkm HDVLDV
0
500
1000
1500
2000
2500
3000
NAMEU15
LAM
EASJP
NMEASEASASCISAFRCECOCN
bio vkmbuscarmoped
Global transport volumes in y2000
Passenger Freight
rest: 40%rest: 40%
Vanhove et al. 2006
6 > GEIA Paris 29.11.2006> Borken
0
100
200
300
400
500
600
NAM EU15 LAM EAS JPN MEA SEA CIS AFR SAS CEC OCN
Mtoe
Road fuel consumption (calc.) vs. road fuel sales (IEA)
Global consumption (calc.): 1.370 MtoeGlobal fuel sales (IEA with corrections for CHN, IND): 1.403 Mtoe Δ: -2%
our calc. with5% error bar
IEA
only problematic region
We modelled the biggest consumers individually: USA, EU15, JP, KOR, CHN, HKG, TWN, IND, RUS, BRA, MEX, ARG, VEN
85% of global road fuel consumption !
7 > GEIA Paris 29.11.2006> Borken
0
100
200
300
400
500
600
NAM EU15 LAM EAS JPN MEA SEA CIS AFR SAS CEC OCN
Mtoe
Road fuel consumption (calc.) vs. road fuel sales (IEA)
Δ Gasoline consumption (calc.) vs. sales (IEA): -3%OECD: -3% / non-OECD: -21%
Δ Diesel consumption (calc.) vs. sales (IEA): -1%OECD: +12% / non-OECD: +10%
diesel
gasoline
We recalculateIRN, SAU, TUR, ISR.
Almost perfect agreement for each fuel type.
All regions match within 5%, except MEA, AFR, OCN.
8 > GEIA Paris 29.11.2006> Borken
1
10
100
1000
10000
Tg/aEDGAR 2.0 (Olivier et al. 1996)EDGAR 3.2 (Olivier et al. 2002)EDGAR 3.2FT (Olivier & v. Aardenne 2005)QUANTIFY (Borken et al. 2006)
Comparison with global road transport‘s emissions: EDGAR
CO2 CO NO2 NMHC w/o evaporation(EDGAR 2000: 27 Tg evap.)
10*CH4
1990 1995/7 2000
1990 1995/7 2000
4223
111
29
15
8
Using EDGAR‘s transport data: Eyring et al. 2005; Granier & Brasseur 2003; Matthes et al. 2003; Matthes 2005; Niemeier et al. 2006.
Borken et al. 2
006 Data: v20
0611
13
9 > GEIA Paris 29.11.2006> Borken
Hemispheric distribution of road transport emissions
0
20
40
60
80
100
120
140
-180
-150
-120 -90 -60 -30 0 30 60 90 120
150
180
Mt
QUANTIFYEDGAR
CO2
0
100
200
300
400
500
600
700
-180
-150
-120 -90 -60 -30 0 30 60 90 120
150
180
kt
QUANTIFYEDGARNMVOC
0
1000
2000
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4000
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6000
-180
-150
-120 -90 -60 -30 0 30 60 90 120
150
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kt
QUANTIFYEDGARCO
0
100
200
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400
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600
-180
-150
-120 -90 -60 -30 0 30 60 90 120
150
180
kt
QUANTIFYEDGARNOx
Emissions distributed ~ to population densitywith modifications:
2wheels ~90% urban, HDVs ~ 90% rural.
CO2 and NOx equal, for CO and NMVOC different level and distribution.
Of total road transport‘semissions
~60-75% in Northern mid-latitudes,
~33% in tropics,
<10% Southern hemisph. (exc. SO2: 22%)Bo
rken et al. 2
006 Data: v20
0611
13
Americas EUR-CIS
MEASAS
SEA-EAS-JP
10 > GEIA Paris 29.11.2006> Borken
Road transport‘s NOx emissions in the year 2000
<30°S: 2%
<0°S: 7%
>0°N: 23%
>30°N: 67%
11 > GEIA Paris 29.11.2006> Borken
NOx: Highest emission densities in OECD + IND + CHN
<30°S: 2%
<0°S: 7%
>0°N: 23%
>30°N: 67%
12 > GEIA Paris 29.11.2006> Borken
Road transport‘s NMVOC emissions in the year 2000
<30°S: 2%
<0°S: 7%
>0°N: 29%
>30°N: 61%
13 > GEIA Paris 29.11.2006> Borken
NMVOC: Highest emission densities in OECD + IND + CHN
<30°S: 2%
<0°S: 7%
>0°N: 29%
>30°N: 61%
14 > GEIA Paris 29.11.2006> Borken
Summary: Road transport emissions in 2000NMHCSO2
NOx prPMTotal: 111 Tg Total: 15 Tg
Total: 29 Tg Total: 1,33 Tg
Borken et al. 2
006 Data: v20
0611
3
15 > GEIA Paris 29.11.2006> Borken
Bulk (~75%) of road transport emissions in few countries onlyNMHCSO2
NOx prPMTotal: 111 Tg Total: 15 Tg
Total: 29 Tg Total: 1,33 Tg
Borken et al. 2
006 Data: v20
0611
3
16 > GEIA Paris 29.11.2006> Borken
Emissions by vehicle category in most important regions
High shares from passenger cars and light trucks –typical for OECD countries.
Gasoline powered vehicles dominate CO, VOC;
diesel powered vehicles dominate NOx, PM, SO2.
Borken et al. 2
006 Data: v20
0611
13
0.0
1.0
2.0
3.0
4.0
5.0
6.0
CO2/100
0
CO/10
10*C
H4
NMHC
NO2
10*P
M
10*S
O2
Tg/a HDTLDTbus2wheelscar
USA
7.1
17 > GEIA Paris 29.11.2006> Borken
Emissions by vehicle category in most important regions
Borken et al. 2
006 Data: v20
0611
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
CO2/100
0
CO/10
10*C
H4
NMHC
NO2
10*P
M
10*S
O2
Tg/aHDTLDTbus2wheelscar
EAS
High shares from mopeds, bus and truck;
very little travel with passenger cars (in 2000).
Development of both growth in transport volumes, shift to passenger cars, and reduction in emissionlimits is very dynamic!
18 > GEIA Paris 29.11.2006> Borken
Emissions by vehicle category in most important regions
Borken et al. 2
006 Data: v20
0611
13
0.0
0.5
1.0
1.5
2.0
2.5
3.0
CO2/100
0
CO/1010
*CH4
NMHC
NO2
10*P
M
10*S
O2
Tg/aHDTLDTbus2wheelscar
EAS
0.0
1.0
2.0
3.0
4.0
5.0
6.0
CO2/100
0
CO/1010
*CH4
NMHC
NO2
10*P
M
10*S
O2
Tg/a HDTLDTbus2wheelscar
USA
7.1
For comparison:
Level factor 2-4 lower, exc. sulfur (!),
Very different vehicleshares,
Each compound dominated by different vehicle types – different per region,
typical pattern,
=> Differentiation needed.
19 > GEIA Paris 29.11.2006> Borken
0.0
1.0
2.0
3.0
4.0
5.0
6.0
CO2/100
0
CO/10
10*C
H4
NMHC
NO2
10*P
M
10*S
O2
Tg/a
HDTLDTbus2wheelscar
Variation of emission estimates – the case of OECD countriesUncertainty mostly due to
• distribution between vehicle types,
• uncertainty of emission factors.
Compared to sales data:
• ΔCO2: gasoline <2%, diesel <5%;
• ΔSO2: gasoline <5%, diesel <10%
Compared to other regional inventories, variation in emission factors:
• ΔNO2 ~15%
• ΔCO ~30%
• ΔHC ~50%
• ΔPM ~80%
Borken et al. 2
006 Data: v20
0606
20
EU15
20 > GEIA Paris 29.11.2006> Borken
0.0
0.5
1.0
1.5
2.0
CO2/100
0
CO/10
10*CH4
NMHC
NO2
10*PM
10*SO2
Tg/aHDTLDTbus2wheelscar
Borken et al. 2
006 Data: v20
0611
3Variation of emission estimates – the case of non-OECD countries
Emissions estimates more uncertain, as
• irregular driving conditions (overloading),
• irregular maintenance (-> super-emitters),
• higher diversity of fleet with very old, old, and new vehicles,
• high dynamics,
• less reliable data gathering.
Compared to sales data:
• ΔCO2: gasoline ~5-10%, diesel ~10-15%;
• ΔSO2: gasoline ~10-20%, diesel ~20-30%
Compared to other regional inventoriesvariation in emission factors:
• ΔNO2 ~25% ΔCO ~50%
• ΔHC ~75% ΔPM ~100%
CHN
21 > GEIA Paris 29.11.2006> Borken
Summary and outlookValidated, consistent, comprehensive and differentiated emission inventory for roadtransport on country by country and 1° longitude by 1° latitude for the year 2000.
New: prPM from gasoline powered vehicles.
~2/3 of total emissions & highest emission densities in Northern mid-latitudes. Only ~10-15 countries are responsible for the bulk of global pollutant emissions:
USA, DE, FR, GB, I, JP, CHN, IND, RUS, BRA, (MEX, THA, IDN)
Vehicles in OECD: Abundant transportation, notably by passenger cars + trucks; Vehicles in non-OECD: High emissions from mopeds, busses, trucks,
but emission from passenger cars are rising.
In progress: Sensitivity of emissions wrt different shares of vehicles and super-emitters; Determine BC/OC & PM1 fractions; Develop transport stories compatible with SRES scenarios for 2025 – 2050 - 2100.
22 > GEIA Paris 29.11.2006> Borken
Further readingBorken, Steller, Vanhove, Meretei: GLOBAL AND COUNTRY INVENTORY OF ROAD PASSENGER AND FREIGHT TRANSPORTATION, THEIR FUEL CONSUMPTION AND THEIR EMISSIONS OF AIR POLLUTANTS IN THE YEAR 2000. Transportation Research Record/86th Annual Meeting of the Transportation Research Board, Washington/USA (submitted) www.trb.org
Steller & Borken: Global road transport’s emission inventory for the year 2000.Proceedings of the TAC-Conference, June 26 to 29, 2006, Oxford/UK (in print)www.pa.op.dlr.de/tac
Borken & Steller: Global road transport’s emission inventory for the year 2000.Meteorologische Zeitschrift (in preparition)
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