A New Anthropogenic Emission Inventory System for Asia in Support of Atmospheric Modeling

A New Anthropogenic Emission Inventory System for Asia in Support of Atmospheric Modeling

Qiang Zhang,1 David G. Streets,1 Kebin He,2 Shekar Reddy,3 Akiyoshi Kannari,4 Il-Soo Park,5 Joshua Fu,6 and Zbigniew

Klimont7

1Argonne National Laboratory, Argonne, USA2Tsinghua University, Beijing, China

3UK Met Office Hadley Centre, Exeter, UK4Independent Researcher, Tokyo, Japan

5Meteorological Research Institute, Seoul, Republic of Korea6University of Tennessee, Knoxville, USA

7International Institute for Applied Systems Analysis, Laxenburg, Austria

6th CMAS Conference, Chapel Hill, NC, USAOctober 1-3, 2007

TRACE-P inventory for the year 2000:It’s now 2007, and we have learned a lot from its use!

SO2

Scope and methodology of a new, dynamicemission inventory dataset for Asia

Updated inventory with improved methodology

Incorporate from best available datasets

Extrapolate from TRACE-P inventory

We develop a new Asia emission inventory dataset with improved methodology

Major improvements from TRACE-P Asia inventory

Detailed technology based approach

Dynamic methodology representing rapid technology renewal

Size-fractioned primary PM emissions

The best available national inventories incorporated

New VOC speciation methodology

Improved spatial allocation

Updated temporal profiles

Constraint from top-down method (inverse model/satellite)

We found that detailed characterization of technology is necessary to improve CO, PM, and HC emission estimates

Good Efficiency

Moderate Efficiency

Poor Efficiency

TRACE-P level of analysis

Lime

We use a dynamic methodology to representthe rapid technology change in the last decade

Emission factors could change very quickly with the fast technology renewal progress: trends of technology distribution and NOx emission factors in power plants

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

6.50

7.00

7.50

8.00

8.50

9.00

g/k

g

>300 MW

200-300 MW

100-200 MW

<100 MW

Nox Emission Factor

We have developed a technology-based PM emission modelfor China, as an extension of the RAINS-PM model

We first developed a size-fractioned PMemission inventory for China

0.0

0.5

1.0

1.5

2.0

2.5B

eij

ing

Tia

nji

n

He

be

i

Sh

an

xi

Inn

er

Mo

ng

oli

a

Lia

on

ing

Jil

in

He

ilo

ng

jia

ng

Sh

an

gh

ai

Jia

ng

su

Zh

eji

an

g

An

hu

i

Fu

jia

n

Jia

ng

xi

Sh

an

do

ng

He

na

n

Hu

be

i

Hu

na

n

Gu

an

gd

on

g

Gu

an

gx

i

Ha

ina

n

Ch

on

gq

ing

Sic

hu

an

Gu

izh

ou

Yu

nn

an

Tib

et

Sh

an

nx

i

Ga

ns

u

Qin

gh

ai

Nin

gx

ia

Xin

jia

ng

Province

Em

iss

ion

(T

g)

>PM10

PM2.5-10

PM2.5

This PM inventory has been used in CMAQ for evaluating the regional impacts on Beijing air quality in support of Olympic plan

High emitters around Beijing

The best available national emission inventory datasets are incorporated to our system

4.862 - 6164 6164 - 109241 109241 - 570387 570387 - 862973 862973 - 2121227 2121227 - 6062683 6062683 - 16729084 16729084 - 90370000

125 126 127 128 129 130

Longitude (degree)

33

33.5

34

34.5

35

35.5

36

36.5

37

37.5

38

38.5

39

Lat

itu

de

(deg

ree)

2003 SO2 emissions for Korea (II-Soo Park)

2000 NOx emissions for Japan (Kannari et al., 2007)

We use a new VOC speciation methodology for better supporting air quality modeling

~100 emitting sources

Fully coupled with SPECIATE 4.0 database

A few local VOC profiles are used

~500 individual VOC species

Supported mechanisms: CBIV, CB05, SAPRC99, SAPRC07, RADM2, GEOS-CHEM

Providing gridded emissions by mechanisms directly, using a speciation/spatial allocation matrix approach

Updated temporal profile is developed using various local information, e.g., monthly profile of NOx emissions by sector

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

0.200

JAN

FEBM

ARAPR

MAY

JUN

JUL

AUGSEP

OCTNOV

DEC

All Sources

Power

Industry

Heating & Residential

Local information is very helpful for improving spatial precision of inventories, e.g. NOx emissions from power plants

Allocate by population Allocate by unit information

The satellite-observed NOx emission increase in China could be directly verified by our high-resolution emission data

-20 -10 -5 -1 -0.1 0.1 1 5 10 50 Gg

2004 NOx – 2001 NOx, power plants

2001: 4.6 Tg-NO2

2004: 7.1 Tg-NO2, +54%

Summer

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1996 1997 1998 1999 2000 2001 2002 2003 2004

Inventory

Satellite

Winter

0.70

0.80

0.90

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.70

1996 1997 1998 1999 2000 2001 2002 2003 2004

Inventory

Satellite

We are finishing a comparison of NOx emission trends in China between emission inventories and satellite imagery

WINTER SUMMER

The satellite and inventory trends agree well in the summer,but the satellite trend grows faster in the wintertime

The bias in winter might come from a combination of Underestimate of seasonal variation in emissions, and

Large error of satellite retrievals in winter

Inventory: +57%

Satellite: +108%

Inventory: +62%

Satellite: +67%

Our new CO inventory now agrees well with top-down estimates over East China region

Source: Kopacz et al., 2007

Final product: gridded emissions over Asia at 30 min × 30 min resolution

PM2.5, 2006 VOC, 2006

Our new emission estimates for Asia for the year 2006 are significantly higher than TRACE-P inventory for the year 2000

0.00

0.50

1.00

1.50

2.00

2.50

SO2 NOx CO NMVOC BC OC

INT

EX

200

6/T

RA

CE

-P 2

000

China

Asia

IMPORTANT NOTE: The emission changes between the two inventories reflect a combination of: (a) actual growth in emissions due to increasing economic development, (b) the effects of replacing the TRACE-P inventory by local inventories in several countries, and (c) improvements and corrections made to the original TRACE-P inventory.

The changes should not be viewed solely as real emissions growth!

Data summary and availability

Data summary– Years: 2004, 2006

– Sectors: Power, Industry, Domestic, Transport

– Species: SO2, NOx, CO, VOC, Primary PM, Hg, NH3, and CH4

– VOC speciation: SAPRC99, SAPRC07, CBIV, CB05, RADM2, GEOS-CHEM

– PM speciation: TSP, PM10, PM2.5, BC, OC, Hg, Ca, and Mg

– Grid size: 0.5 degree for whole Asia; fine grid emissions (0.1-0.2 degree) available for selected regions/sectors

Data availability– 0.5 degree data for 2006 (with SAPRC99 speciation) are available at:

http://www.cgrer.uiowa.edu/EMISSION_DATA_new/index_16.html

– 0.5 degree data with other speciation are generally open for community upon request (dstreets@anl.gov or zhangq@anl.gov)

– Fine grid emissions are available on a collaboration basis with the agreement of Chinese collaborators

Further reading for methodologies and numbers

Streets, D.G., J. Hao, Y. Wu, J. Jiang, M. Chan, H. Tian, and X. Feng (2005), Anthropogenic mercury emissions in China, Atmos. Environ., 39, 7789-7806.

Streets, D.G., Q. Zhang, L Wang, K. He, J. Hao, Y Wu, Y. Tang, and G.R. Carmichael (2006), Revisiting China’s CO emissions after TRACE-P: Synthesis of inventories, atmospheric modeling, and observations, J. Geophys. Res., 111, D14306, doi: 10.1029/2006JD007118.

Zhang Q., Streets, D.G., K. He, Y.X. Wang, A. Richter, J. P. Burrows, I. Uno, C. J. Jang, D. Chen, Z. Yao, and Y. Lei (2007), NOx emission trends for China, 1995-2004: The view from the ground and the view from space, J. Geophys. Res., in press

Zhang Q., Streets D.G., K. He, and Z. Klimont (2007), Major components of China’s anthropogenic primary particulate emissions, Environ. Res. Lett., submitted

Next step: provide high-resolution emissions by combining GIS information and county-level statistics

0.1 degree, without GIS information 0.1 degree, with GIS information

0.5 degree, without GIS informationCounty emissions

Next step: develop a new multi-resolution emission model system for China (Argonne and Tsinghua)

Acknowledgements

USEPA, especially Dr. Carey Jang

NASA INTEX project

China National Basic Research Program (973) & National High-Technology Research Program (863)

Collaborators at Tsinghua University, China