The 13th AIM International Workshop
17-18 February 2008, Tsukuba

Emission Inventory of Air Pollutantsfor East Asia and Regional Air Quality
Modeling

Gakuji KURATA
Kyoto University

1

Contents
 Background of the study(Ancillary Benefits / Co-Benefits ) Model outline
 Development of the Calibration Method by
Source – Receptor Analysis.
 Results
 Challenges for future2

Background (1)“Ancillary Benefits of Climate Change
Policies to the Air Quality” (or Co-benefits
between both policy) is considered to bevery important to promote the installation ofcountermeasures of climate change,especially for developing countries.

Ancillary Benefits

Control ofGHG Emission

Decrease the cost of
Air Pollution Control3

Background (2)To evaluate the “Ancillary Benefits of Climate
Change Policies to the Air Quality”,
Quality it is necessary
to integrate the air pollution control policy into
climate change policy.
 IPCC 4th assessment report suggest more
research for ancillary benefits (or Co-benefits)
between GHG and air pollution policies.
(There are still many uncertainties; Change inemission of gaseous and particulate species inthe future, human and ecological impact of airpollutants, future cost for air pollution control, etc.)
4Outline of the study
How to integrate the ancillary benefits into thecurrent model framework.
• Global Scale• Time-lag between emission
and impact
9 Long lifetime of GHGs
9 Slow response of the system

Climate Modeling

Econometric
ModelEmission
Inventory / Estimate
GHGs
Fossil Fuelconsumption

Pollutants

Downscaling• Regional / Local Scale• No time-lag between
emission and impact

Air Pollution
Modeling

Emission Factor
TechnologyAncillary Benefits

Climate ChangePolicy

Air Pollution
Control Policy
Co-benefits
5Emission Inventory and Downscaling
Downscaling by various spatial distribution data.
(such as population, road network, LPS, sub-regional
statistics,etc.)
EconometricModel• Global Scale• Time-lag between
emission and impact

Climate Modeling

Downscaling

Emission
Inventory / Estimate
GHGs
Fossil Fuelconsumption

Pollutants• Regional / Local Scale• No time-lag between
emission and impact

Air Pollution
Modeling

Emission Factor
TechnologyBenefits
Bottom-up inventoryAncillary(AIM/Enduse)
Climate ChangeCollecting Emission Factor (EF) data.

PolicyAir Pollution
Control Policy

Co-benefits

6GIS Platform for Downscaling
IPCC SRES Scenario, New Scenario

GIS Platform
National / Regional Emission （Air Pollutants）
Population migration
Industry migration
Landuse Change
Transport Network

Local Statistics（Current・Prediction）

Secondary Boundary data

Emission from Province level
Mesh allocation indicator（Current・Future）
Population/GDP/Transport Network
Landuse/Industry/LPS

1km Mesh Emission Data

Atmos.
Ocean
GlobalClimate
Model

ClimateDataset（Furure）

3D Chemical Transport Model

7

Air pollution modeling
There are still very large uncertainties …
• Activities• Emission Factor
Econometric• Chemical Reaction
Model• Vertical mixing (convection / diffusion )• Dry• /GlobalWet Scaledeposition•NotTime-lag
only between
for future projection,
Emissionemission
andforimpactbut also
current situation
Inventory / Estimate
Climate Modeling

GHGs

Pollutants• Regional / Local Scale• No time-lag between
emission and impact

Air Pollution
Modeling

Emission Factor

FuelSo, first, we need to improveFossile
theframeworkofTechnology
consumption
the Air Pollution Modeling.
•Verify and Improve the model performance.
Ancillary Benefits•Develop
thefeedbacklooptoimprove
Climate Change
emission inventories.
Policy•Improve the robustness for future prediction.
Co-benefits

Air Pollution
Control Policy

8Observation
Observation
Data
Data

Emission inventory(Mesh data)

Emission
Emission
Inventory
Inventory(Regional)(Regional)changechange

Min.
Min.
Error
Error

S-R
S-R
MatrixMatrix

Downscaling
Downscaling
Calculated
CalculatedConcentration
Concentration

ArcGISArcGISTime
Timevariation
variation(Annual,(Annual,Daily)
Daily)

Emission
Emission
Mesh
Meshdatadata

LanduseLanduseTerrainTerrain

MM5
MM5

Meteo.
Meteo.
Field
Field

Meteorological
Model

Calibration

Boundary
Boundary
Condition
Condition

CMAQ
CMAQ
ECMWF
ECMWF
Meteo.
Meteo.Data
Data

SourceSourceReceptor
Receptor
AnalysisAnalysis

ChemicalTransport
Model

Outline oftheframework(Yanagi, 2008)

Target Area
60°E

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

150°E

160°E

 Domain

East and South Asia
 Grid size

８０ｋｍ

40°N

CMAQ

40°N

30°N

30°N

20°N

20°N

10°N

10°N

0°

0°

10°S

10°S

 Number of Grids

MM5 100 x 82
CMAQ 97 x 79

Target species

MM5
80°E

CO（Carbon monoxide）

90°E

100°E

110°E

120°E

130°E

140°E

(Yanagi, 2008)

reason: lifetime in the free troposphere isabout 1-2 month→Suitable for calibration

10Used observation data TRACE-P :Transport and Chemical Evolution over the Pacific
80°E

90°E

100°E

110°E

120°E

130°E

140°E

150°E

160°E

170°E

180°

50°N

Feb. – Apr. 2001
 NASA GTE


40°N

40°N
30°N

DC8
30°N
20°N



P3B

20°N
10°N

10°N

Flight Truck of ;● : DC8● : P3B

0°

0°
90°E

100°E

110°E

120°E

130°E

140°E

150°E

(Yanagi, 2008)

160°E

11

Observation
Observation
Data
Data

Emission inventory(Mesh data)

Emission
Emission
Inventory
Inventory(Regional)(Regional)changechange

Min.
Min.
Error
Error

S-R
S-R
MatrixMatrix

Downscaling
Downscaling
Calculated
CalculatedConcentration
Concentration

ArcGISArcGISTime
Timevariation
variation(Annual,(Annual,Daily)
Daily)

Emission
Emission
Mesh
Meshdatadata

LanduseLanduseTerrainTerrain

MM5
MM5

Meteo.
Meteo.
Field
Field

Meteorological
Model

Calibration

Boundary
Boundary
Condition
Condition

CMAQ
CMAQ
ECMWF
ECMWF
Meteo.
Meteo.Data
Data

SourceSourceReceptor
Receptor
AnalysisAnalysis

ChemicalTransport
Model

Outline oftheframework(Yanagi, 2008)

Emission inventory
 Used inventories

Anthropogenic・・・Streets et al.(2003), EDGAR3.2
Fast Track (Olivier et al.2005)
 Daily Biomass Burning ・・・ABBI (Asian Biomass
Burning Inventory) (Michel et al., 2005)
 We classified emission source to four group.

Anthropogenic (China / Other countries)
 Biomass Burning Background （i.e. Boundary condition）
13

Observation
Observation
Data
Data

Emission inventory(Mesh data)

Emission
Emission
Inventory
Inventory(Regional)(Regional)changechange

Min.
Min.
Error
Error

S-R
S-R
MatrixMatrix

Downscaling
Downscaling
Calculated
CalculatedConcentration
Concentration

ArcGISArcGISTime
Timevariation
variation(Annual,(Annual,Daily)
Daily)

Emission
Emission
MeshMeshdata
data

LanduseLanduseTerrainTerrain

MM5MM5Meteo.
Meteo.
Field
Field

Meteorological
Model

Calibration

Boundary
Boundary
Condition
Condition

CMAQ
CMAQ
ECMWF
ECMWF
Meteo.
Meteo.Data
Data

SourceSourceReceptor
Receptor
AnalysisAnalysis

ChemicalTransport
Model

Outline oftheframework(Yanagi, 2008)

Meso-scale Meteorological Model
and Chemical Transport Model MM5 : 5th Generation Mesoscale ModelDeveloped by Pennsylvania State Univ.(PSU) andNational Center for Atmospheric Research(NCAR,USA)

 CMAQ : Community Multiscale Air Quality Model

Developed by US/EPA 3D Eulerian type Chemical Transport Model

15Observation
Observation
Data
Data

Emission inventory(Mesh data)

Emission
Emission
Inventory
Inventory(Regional)(Regional)changechange

Min.
Min.
Error
Error

S-R
S-R
MatrixMatrix

Downscaling
Downscaling
Calculated
CalculatedConcentration
Concentration

ArcGISArcGISTime
Timevariation
variation(Annual,(Annual,Daily)
Daily)

Emission
Emission
Mesh
Meshdatadata

LanduseLanduseTerrainTerrain

MM5
MM5

Meteo.
Meteo.
Field
Field

MeteorologicalModel

Calibration

Boundary
Boundary
Condition
Condition

CMAQ
CMAQ
ECMWF
ECMWFMeteo.
Meteo.Data
Data

SourceSourceReceptor
Receptor
AnalysisAnalysis

ChemicalTransport
Model

Outline oftheframework(Yanagi, 2008)

Calibration Method
Following 4 Steps

1. Minimize the model error.
2. Source – Receptor Analysis
3. Minimize the error between model andobservation.
4. Correction of Emission Inventory17

Minimization of Model errors

Selection of Chemical Mechanism
Improve the spatial resolution
Improve the time-step of Input / Output

Source – Receptor Analysis



We assumes the linear relationship between
emission amount and concentration, because thereaction rate of CO is small.
Contributions of each source category werecalculated by source – receptor analysis.
18Minimization of the Errors.
① Improved Model concentration.

C M n = ∑ pi ki ,n xi

i ：Source category

n：each observationi

ModifiedCorrection
Concentration
Coefficient

S-Rmatrix

CO emission② Minimization of the error after modification.

Min ∑ ( C M n − C O n )2nModified concentration

Observation(Yanagi, 2008)
19

Example of the calculation(March 2001, one month; CO)

20Result of Contribution Analysis
120°E

130°E

140°E

40°N



Biomass Burning

DC8 Flight #16

30°N
Anthropogenic（Other countries）

Anthropogenic（China）

30°N

Observation

Background20°N

Calculation
130°E

140°E

CO (ppbv)

120°E

Observation

TimeContribution

Time(Yanagi, 2008)21

Obtained correction coefficient
Anthropogenic（constant during all flight）

China・・・2.7
Other countries・・・2.5

Background ・Biomass Burning （varied for each flight）

Correction Coefficient

DC88765432１198

P3B

Background
Biomass Burning

7654321１0 8 9 10 11 12 13 14 15 16 17 18 190

Flight #Correction Coefficient

10109678

9 10 11 12 13 14 15 16 17(Yanagi, 2008)

Anthropogenic
Anthropogenic && Background
Background ・・・Underestimate
・・・Underestimate
Biomass・・・Over estimate
estimate (but(but varied)varied)
Biomass Burning
Burning ・・・Over

22Calibration Results


DC8 Flight #16

120°E

130°E

140°E

40°N

30°N

Biomass Burning

Anthropogenic （Other countries）

30°N

Anthropogenic（China）
Background
Observation

120°E

130°E

140°E

After calibration

CO濃度(ppbv)

Before calibration

20°N

TimeTime(Yanagi, 2008)23

Change of errors Histogram of ”Calculation－Observation”
Histogram from all flight data.

After calibration

Before calibration
300

250

250

Frequency

Frequency

300

200
150

200
150

100

100

5050

0-800

0-600-600

Bias
Bias
Variance
Variance
Error
Error

-400

Error

-200

00

200

-400

--79.542
79.5424331.043
4331.043
42.60%
42.60%

00

-200

Error

Bias
Bias

Improved

Variance
Variance
Error
Error

200

400

--0.1870.187
2464.217
2464.217
7.45%
7.45%

(Yanagi, 2008)24

Air pollution modeling
There are still very large uncertainties …
• Activities• Emission Factor
Econometric• Chemical Reaction
Model• Vertical mixing (convection / diffusion )• Dry• /Global
Wet Scaledeposition•NotTime-lag
only between
for future projection,Emission
emission
andforimpact
but also
current situation
Inventory / Estimate
Climate Modeling

GHGs

Pollutants• Regional / Local Scale• No time-lag between
emission and impact

Air Pollution
Modeling

Emission Factor

FuelSo, first, we need to improveFossile
theframeworkofTechnology
consumption
the Air Pollution Modeling.
•Verify and Improve the model performance.
Ancillary Benefits
AncillaryBenefits•Developthefeedback
looptoimprove
Climate Changeemission inventories.
Policy•Improve the robustness for future prediction.
Co-benefits

Air Pollution
Control Policy

Co-benefits

25

Challenges for future Improve the model performance Develop the calibration method for NOx, SO2,VOC ( more reactive species) Develop a calibration method to sectoral
activities.
 Long-term prediction of Regional / Local Air
Quality under several scenarios.
 Estimation of Ancillary Benefits of Climate
Change Policy to Air Quality.
26