Chemical Weather Forecasting Perspectives & Challenges Gregory R. Carmichael Department of Chemical...

Chemical Weather Forecasting Perspectives & ChallengesGregory R. CarmichaelDepartment of Chemical & Biochemical EngineeringCenter for Global & Regional Environmental Research and theUniversity of Iowa

The University of Iowa, USA

Characterization of Urban Signals Science Support to Policy

Climate : Air QualityAnalysis Framework

The University of Iowa, USA

Air Quality

ICLEI is not your typical environmental NGO. Were a membership organisation (Cape Town, Johannesburg, Tshwane and eThekwini are all members) that helps put into practice the phrase Think globally, act locally.

If you already know about ICLEI, its very likely through interaction with us has been through our Local Agenda 21 campaign.

TRACE-P EXECUTIONEmissionsFossil fuelBiomass burningBiosphere, dustLong-range transport fromEurope, N. America, AfricaASIAPACIFICP-3Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLISDC-83D chemical model forecasts: - ECHAM - GEOS-CHEM - Iowa/Kyushu - Meso-NHFLIGHTPLANNINGBoundary layerchemical/aerosolprocessingASIANOUTFLOWStratosphericintrusionsPACIFICForecasting in Support of Field Experiments

Fly here to sample high O3

Ace-Asia April/May 2001

The Use of Models in Planning

Experimentalmeasurements

Theoreticalmodeling

Flight Tracks Along the Asian Pacific Rim During the TRACE-P Mission

From P. Westphals web site: http://www.nrlmry.navy.mil/aerosol/Case_studies/20010413_epac/These dust outbreaks caused severe problems in ChinaThese photos are reduced-resolution versions of photos taken by Dr. Zev Levin while visiting Baicheng, Jilin Province, China (NE of Beijing) during the dust storm. The first two were taken on April 7th. The third was taken on April 8th. The two buildings seen in the foreground of the third image are also seen in the second

The CFORS forecast (upper left) of the two dust systems are shown above. The dust plume (pink) represents the region with dust concentrations greater than 200 mgrams/m3. White indicates clouds. The SeaWifs satellite image (upper right) also clearly shows the accumulation of dust spiraling into the Low Pressure center. Also note the strong outflow of dust in the warm sector ahead of the front over the Japan Sea. The two systems are clearly seen in the satellite derived TOMS-AI (aerosol index) (lower right). The dust event is clearly seen in the China SEPA air pollution monitoring network. Lower left hand panel shows extremely large ground level concentrations (http://www.ess.uci.edu/~oliver/tracep/airqual/index.html). The sandstorm and sand-drifting weather, which swept across most parts of China caused severe visibility and air quality problems http://news.xinhuanet.com/english/20010409/395181.htmNASA-Seawifs

Sources of airborne pollution in Asia are many: home cooking, power generation, industry, traffic, and biomass burning

Methodology for Asian Emission Estimates

Energy Use

RAINS-AsiaModel

EmissionControls

Activitydata

Other human activities

Biomassburning

Natural emissions

Biogenic, Volcanic...

Emission factors, Regulations

Anthropogenic emissions

Total emissions

Organization of emissions data

National (23 countries)

Regional (94 regions)

Urban (22 cities)

Point (355 sources)

Historical: 75-95 (5-yr)

Current: 90-00 (1-yr)

Projections: 95-30 (5-yr)

TRACE-P Species for year 2000:

SO2, NOx, CO2, CO, CH4, NMVOC (19 classes), BC, OC, NH3

1 x 1 down to1 km x 1 km

Lat/ long

Gridded emissions

The Emissions Vary Greatly by Region Reflecting Many Social/Economic Factors

The TRACE-P/Ace-Asia emission inventory shows the important sources of each type of air pollutant in Asia

Chart1

0.36033813640.10681118520.07534297760.4466913106000.0108163902

0.18448076210.07079692960.37288486410.2671761186000.1046613255

0.233585070.27213942670.11918993870.225471752600.03443090320.115183912

0.1046467050.3755746660.2788275010000.240951128

0.10275512950.08819971780.00001020400.56548704250.21448183050.029065982

0.16360836830.33734194240.26674534520.0037854694000.2285188747

0.0543407140.64219589420.11098385790.0139324437000.1785470901

0.00568937410.64742585550.02466130110.0025481157000.3196753536

00.061158539000.82870252770.07685571080.0332835858

Industry

Residential

Transportation

Power generation

Agriculture

Other

Biomass burning

sector

PollutantsIndustryDomesticTransportPower GenerationCement Product.Biofuel Combust.Biomass BurningTotal (Gg)PollutantsIndustryDomesticTransportPower GenerationBiofuel Combust.Biomass BurningTotal (Gg)

SO212365.31748439153665.3189947762585.4615328.6019879676371.173865103534315.87(Gg)SO236%11%8%45%0%1%100%

NOx4938.1534598961895.0816286459981.32629624097151.73042801.558714615926767.8504993977(Gg)NOx18%7%37%27%0%10%100%

CO22304.92552.831176.122224.86339.752132.531136.599867.59(Tg)CO227%6%12%23%22%12%100%

CO29150.8192474023104621.6333789977671.342700674667120.3434738018278564.138800868(Gg)CO10%38%28%0%0%24%100%

NMVOC8532.233162002517592.499321348813910.8622889174197.413542535311917.338592811852150.3469076158(Gg)NMVOC16%34%27%0%0%23%100%

BC138.08108441911631.8354854056282.012699445435.4026805413453.69252605352541.024475865(Gg)BC5%64%11%1%0%18%100%

OC59.28471864176746.3413386551256.977001882526.5520104063331.098124603810420.2531941891(Gg)OC1%65%2%0%0%32%100%

CattlePigsOther AnimalsFertilizer UseBiofuel UseBiomass BurningOther SourcesTotal (Gg)

NH35024.7098082957.3891222538.82306412284.116461683.0188997421915.92954908632114.98527518.9719028284

Rice CultivationAnimal EmissionsLandfillsWastewater TreatmentCoal Combustion+ProductionNatural Gas and OilBiofuel CombustionBiomass BurningTotal (Gg)

CH424237.7536168.008866.8814044.238407.003357.358634.683104.85106820.75

David Streets:Taken from file Asia_ bioburn_2000_final.xls.

David Streets:NOTE: This category represents the direct emissions from combustion of fuelwood, agricultural residues, and animal waste in cookers, stoves and heaters. Adjustments may be made if necessary to reflect subsequent absorption of C in re-growth of some vegetative products.

klimont:Includes - dairy cattle and - non-dairy cattle

klimont:Includes:- poultry,- sheep,- goats,- horses,- camels,- asses

klimont:Includes application of nitrogen fertilizers.

David Streets:Biofuel use data taken from previous TRACE-P analyses. Emission factor used is 1.3 g/kg, from Andreae and Merlet.

David Streets:Taken from file Asia_bioburn_2000_final.xls

klimont:Includes humans, pets, waste treatment and disposal (does not include emissions from traffic).

nelson:Includes 'industry' category, which (from Klimont) includes nitrogen fertilizer manufacturing plants, but does not include emissions from fossil fuel combustion.

streets:Taken from file Asia_bioburn_2000_final.xls



sector

0000000

0000000

0000000

0000000

0000000

0000000

SO2

NOx

CO2

CO

NMVOC

BC

OC

Sheet1

0

0

0

0

0

0

0

NH3

Summary

0

0

0

0

0

0

0

0

(CH4)

Total

000000

000000

000000

000000

000000

000000

000000

Industry

Domestic

Transport

Power Generation

Biofuel Combust.

Biomass Burning

SO2

0000000

0000000

0000000

0000000

0000000

0000000

SO2

NOx

CO2

CO

NMVOC

BC

OC

NOx

PollutantsIndustryDomesticTransportPower GenerationAgricul-tureOtherBiomass BurningTotal (Gg)PollutantsIndustryDomesticTransportPower GenerationAgricul-tureOtherBiomass BurningTotal (Gg)

SO212365.33665.32585.515328.60.00.0371.234315.9(Gg)SO236%11%8%45%0%0%1%100%

NOx4938.21895.19981.37151.70.00.02801.626767.9(Gg)NOx18%7%37%27%0%0%10%100%

CO22304.92685.41176.12224.90.0339.81136.69867.6(Tg)CO223%27%12%23%0%3%12%100%

CO29150.8104621.677671.30.00.00.067120.3278564.1(Gg)CO10%38%28%0%0%0%24%100%

CH410976.49421.61.10.060405.822911.13104.9106820.8CH410%9%0%0%57%21%3%100%

NMVOC8532.217592.513910.9197.40.00.011917.352150.3(Gg)NMVOC16%34%27%0%0%0%23%100%

BC138.11631.8282.035.40.00.0453.72541.0(Gg)BC5%64%11%1%0%0%18%100%

OC59.36746.3257.026.60.00.03331.110420.3(Gg)OC1%65%2%0%0%0%32%100%

NH30.01683.00.00.022805.02115.0915.927519.0NH30%6%0%0%83%8%3%100%

CattlePigsOther AnimalsFertilizer UseBiofuel UseBiomass BurningOther SourcesTotal (Gg)

NH35024.7098082957.3891222538.82306412284.116461683.0188997421915.92954908632114.98527518.9719028284

Rice CultivationAnimal EmissionsLandfillsWastewater TreatmentCoal Combustion+ProductionNatural Gas and OilBiofuel CombustionBiomass BurningTotal (Gg)

CH424237.7536168.008866.8814044.238407.003357.358634.683104.85106820.75













NOx

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

Industry

Residential

Transportation

Power generation

Agriculture

Other

Biomass burning

CO2

SUMMARY OF ANTHROPOGENIC EMISSIONS IN ASIA IN THE YEAR 2000

RegionEmissions from Energy, Industry, and Agriculture (Tg)

SO2NOxCO2COCH4NMVOCBCOCNH3

China20.3010.533534100.037.8214.740.942.6613.34

Other East Asia2.314.33194115.034.833.730.100.200.88

of which, Japan0.802.1911996.581.131.880.0520.0620.35

of which, Rep. of Korea0.831.324082.661.431.130.0210.0220.17

Southeast Asia3.153.06117334.0418.4311.060.321.372.96

South Asia7.104.76203362.342.6310.670.662.829.42

India5.464.05168751.132.438.630.522.197.23

Other South Asia1.640.71346.0011.1710.202.040.140.632.19

International Shipping1.081.29510.120.000.030.0680.0510.00

Asia Total33.9523.978731211.1103.740.232.097.0926.60

33.9523.9787.31211.14103.7140.232.097.0926.60

RegionEmissions from Biomass Burning (Tg)


China0.080.8228315.740.542.690.110.730.23

Other East Asia0.000.21623.140.120.540.0210.200.05

Southeast Asia0.171.0652231.631.845.690.211.550.41

of which, Indonesia0.050.311508.960.531.610.0590.440.12

South Asia0.100.7126916.600.612.990.110.860.23

of which, India0.070.5419912.260.422.210.0830.650.17

Other South Asia0.030.1770.004.340.190.780.030.210.06

Asia Total0.372.80113767.13.1011.920.453.330.92

0.372.8011.3767.123.1011.920.453.330.92

RegionTotal Anthropogenic Emissions (Tg)


China20.3911.353817115.838.3617.431.053.3813.57

Other East Asia2.334.53200318.174.954.280.120.390.92

of which, Japan0.802.2012036.811.131.920.0530.0740.35


Southeast Asia3.324.12169565.720.2716.750.532.923.37

South Asia7.195.47230278.943.2413.660.773.689.65

India5.544.59188663.332.8510.840.602.847.40

Other South Asia1.650.88416.0015.5110.392.820.170.842.25


Asia Total34.3226.779868278.6106.852.22.5410.4227.52

34.3226.7798.68278.56106.8152.152.5410.4227.52

China20.3911.3538.1711.5838.3617.431.053.3813.57

Japan0.802.2012.030.681.131.920.050.070.35

India5.544.5918.866.3332.8510.840.602.847.40

Other South Asia1.650.884.161.5510.392.820.170.842.25

China1.000.560.380.571.880.850.050.170.67

Japan1.002.750.120.851.412.400.070.090.44

India1.000.830.191.145.931.960.110.511.34

Other South Asia1.000.530.040.946.301.710.100.511.36

NOTES: Emissions are presented as follows: SO2 as SO2, NOx as NO2, CO2 as CO2, CO as CO, CH4 as CH4, NMVOC as full MW of constituent compounds, BC as C, OC as C, NH3 as NH3. This inventory only includes anthropogenic emissions (e.g., no volcanic SO2, bio

This work is sponsored by the TRACE-P project of the National Aeronautics and Space Administration. See the following web site for further information on this inventory, including access to gridded emissions data: http://www.cgrer.uiowa.edu/EMISSION_DATA/

Citation: A year-2000 inventory of gaseous and primary aerosol emissions in Asia to support TRACE-P modeling and analysis, Streets, D.G., T.C. Bond, G.R. Carmichael, S. Fernandes, Q. Fu, D. He, Z. Klimont, S.M. Nelson, N.Y. Tsai, M.Q. Wang, J.-H. Woo, and

Total Anthropogenic Emissions (Tg)

SO2NOxCH4NMVOCBCOCNH3COCO2

China20.3911.3538.3617.431.053.3813.57115.83817

Other East Asia2.334.534.954.280.120.390.9218.172003

of which, Japan0.802.201.131.920.0530.0740.356.811203


Southeast Asia3.324.1220.2716.750.532.923.3765.71695

South Asia7.195.4743.2413.660.773.689.6578.92302

India5.544.5932.8510.840.602.847.4063.31886

Other South Asia1.650.8810.392.820.170.842.2515.51416.00


Asia Total34.3226.77106.852.22.5410.4227.52278.69868

34.3226.77106.8152.152.5410.4227.52278.5698.68

Total Anthropogenic Emissions (Tg)

SO2NOxNMVOCCH4NH3BCOCSO2NOxNMVOCCH4NH3BCOC

China20.3911.3517.4338.3613.571.053.38Japan0.802.201.921.130.350.0530.074

of which, Japan0.802.201.921.130.350.0530.074Indonisa0.88416821.31736386.90264167556.44289174291.39039659260.20638233031.1377807114

Southeast Asia3.324.1216.7520.273.370.532.92China/54.0782.273.4867.6722.7140.210.676

South Asia7.195.4713.6643.249.650.773.68

India5.544.5910.8432.857.400.602.84

Other South Asia1.650.882.8210.392.250.170.84



China20.3911.353817115.838.3617.431.053.3813.57

Other East Asia2.334.53200318.174.954.280.120.390.92

of which, Japan0.802.2012036.811.131.920.0530.0740.35


Southeast Asia3.324.12169565.720.2716.750.532.923.37

South Asia7.195.47230278.943.2413.660.773.689.65

India5.544.59188663.332.8510.840.602.847.40

Other South Asia1.650.88416.0015.5110.392.820.170.842.25


Asia Total34.3226.779868278.6106.852.22.5410.4227.52

34.3226.7798.68278.56106.8152.152.5410.4227.52


SO2NOxCO2/1000CO/20CH4/2NMVOCBCOCNH3

China20.3911.353.85.819.1817.431.053.3813.57

Other East Asia2.334.532.00.92.484.280.120.390.92

of which, Japan0.802.201.20.30.571.920.0530.0740.35

of which, Rep. of Korea0.831.320.40.10.721.160.0220.0280.17

Southeast Asia3.324.121.73.310.1416.750.532.923.37

South Asia7.195.472.33.921.6213.660.773.689.65

India5.544.591.93.216.4310.840.602.847.40

Other South Asia1.650.880.40.85.202.820.170.842.25

International Shipping1.081.290.10.00.000.030.0680.0510.00

Asia Total34.3226.779.913.953.4152.22.5410.4227.52

34.3226.770.113.953.4152.152.5410.4227.52

CO2

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

China

Other East Asia

International Shipping

Southeast Asia

India

Other South Asia

Asia Total

(Tg/yr)

CO

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

(CO2 is scaled by 0.01)

China

Other East Asia


Southeast Asia

India

Other South Asia

Asia Total

(Gg/yr)

CH4

00

00

00

00

00

00

00

00

00

China

Japan

NMVOC

0000

0000

0000

0000

0000

0000

0000

0000

0000

China

India

Other South Asia

Japan

Speciated NMVOC

0000

0000

0000

0000

0000

0000

0000

China

South Asia

Southeast Asia

of which, Japan

BC

000

000

000

000

000

000

000

China/5

Indonisa

Japan

Emission Distribution (Tg)

OC

00000000

00000000

00000000

00000000

00000000

00000000

00000000

00000000

00000000

00000000

00000000

00000000

NH3

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

CO2/1000

CO/20

BC

OC

SO2(Tg)

Biomass burning

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

NOx

CH4/2

NMVOC

NH3

SO2(Tg)

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

0000000

China

Other East Asia


Southeast Asia

India

Other South Asia

Asia Total

(Tg/yr)

Table. Total emissions of 9 pollutants by 20 countries in Asia (2000) Unit: Gg/yr

COUNTRYSO2NOxCO2COCH4NMVOCBCOCNH3

China20,38511,3473,816,640115,74938,35617,4321,0493,38513,570

Japan8012,1981,202,9606,8061,1431,9205374352

Korea, Rep8291,322411,1922,8241,4331,1612228172

Korea, DPR227273120,3663,5561,3452342210698

Mongolia10122168,8152,86147245219173155

Taiwan, China376521199,6642,127560510810152

Brunei62010,036155043002

Cambodia408935,8481,707708305148986

Indonesia8841,317587,40023,1056,4436,9032061,1381,390

Laos219643,8812,5473874861812958

Malaysia273494144,0715,5528691,42426151146

Myanmar65226145,3158,4462,6911,67165421341

Philippines713326152,2524,1022,5631,39836192273

Singapore16318555,9301388581324

Thailand9611,086350,92810,8153,5673,05272364388

Vietnam193283169,2009,2482,9071,39088432686

Bangladesh140220123,1584,8273,60881952268763

Bhutan683,689172423621210

India5,5364,5911,886,03163,34032,85110,8446002,8377,399

Nepal385539,2742,08791734621135168

Pakistan1,416539221,3497,0765,4151,344853681,214

Sri Lanka585728,3091,348407275115592

International Shipping1,0831,29251,2801171276851

Asia Total34,31626,7689,867,589278,564106,82152,1502,54110,42027,519

Species: SO2 (as SO2) Data: EMISSIONS 2000 Version: final Date: 07/01/2002

REGIONIndustry (Gg)Domestic (Gg)Transport (Gg)Power Generation (Gg)Biomass Burning (Gg)Total (Gg)

Anhui179.4538.735.36219.892.71446.14

Beijing124.3257.787.61168.990.15358.84This inventory was prepared by D.G. Streets, N.Y. Tsai, and K.F. Yarber, Decision and Information Sciences Division, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contac

Fujian90.5026.223.9470.791.37192.83

Gansu121.2861.913.44250.631.38438.64

Guangdong364.0750.7831.13397.192.17845.34

Guangxi313.6932.0227.54414.282.37789.89

Guizhou299.10326.262.40417.481.351046.60

Hainan9.091.802.2621.400.2234.76

Hebei511.26176.7323.22637.882.201351.29

Heilongjiang103.5742.5610.96126.1212.47295.69

Henan388.77153.2512.49646.773.651204.94

Hong Kong9.781.320.6364.550.0076.28

Hubei228.5973.708.38264.202.64577.51

Hunan353.63134.2110.18142.342.75643.12

Jiangsu473.9982.0362.90571.560.721191.19

Jiangxi217.1963.517.89207.173.48499.24

Jilin96.2233.836.5397.123.03236.72

Liaoning381.5487.9336.18441.971.10948.73This inventory is for anthropogenic sources only. It does not include volcanic emissions, which are added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Nei Mongol53.5127.0822.34544.2111.45658.59

Ningxia21.088.268.65213.200.43251.62

Qinghai13.088.710.8426.192.7351.54

Shaanxi285.2097.957.53491.301.06883.04

Shandong702.26161.8036.431063.842.991967.32

Shanghai152.9719.9516.49328.230.20517.84

Shanxi589.56102.1210.12778.030.671480.51

Sichuan687.99306.5713.01700.554.341712.46

Tianjin114.9539.7211.02222.890.18388.76NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Xinjiang62.22171.526.0298.474.07342.29

Xizang2.572.300.495.225.2915.87

Yunnan167.1883.653.95137.334.07396.18

Zhejiang234.5837.998.69258.941.37541.57

China Total7353.192512.19408.6210028.7382.6020385.33

Chugoku, Shikoku98.3810.037.1239.35

Chubu62.7811.1513.2556.54

Hokkaido, Tohoku41.9512.669.3242.01No data

Kanto53.4512.2825.5871.73

Kinki50.2210.1813.9540.32Not applicable

Kyushu, Okinawa39.8711.378.8057.70

Japan Subtotal346.6567.6778.02307.651.30801.29

North121.975.773.3727.74

Pusan38.8726.4072.0536.31

Seoul, Inchon287.6138.7425.0523.12

South69.861.0215.5234.37

Korea, Rep of, Subtotal518.3171.93115.99121.540.77828.54

Korea, DPR155.1622.013.9644.971.32227.42

Mongolia31.153.240.4648.6417.40100.89

Taiwan, China245.169.6030.4490.610.24376.05

Other East Asia Total1296.43174.45228.87613.4121.042334.20

Brunei1.110.812.092.450.006.46

Cambodia13.697.556.116.996.0940.43

Indonesia271.37168.60209.74186.0848.38884.17

Laos2.582.352.880.0312.8120.65

Malaysia134.668.6319.6397.3212.90273.14

Myanmar3.5725.461.420.6033.9264.97

Philippines285.9574.6218.95320.8612.40712.78

Singapore17.796.943.64134.790.00163.16

Thailand488.7522.3121.84400.0127.78960.69

Vietnam66.0170.009.0132.6415.27192.93

Southeast Asia Total1285.48387.27295.311181.77169.563319.39

Bangladesh33.1656.3921.4120.169.26140.38

Bhutan2.961.520.810.050.696.03

India1782.84359.40346.702973.2273.905536.06

Nepal8.7819.422.940.765.7837.68

Pakistan586.84136.36181.45505.146.031415.82

Sri Lanka15.6418.3216.355.362.3257.99

South Asia Total2430.22591.41569.663504.6997.977193.95

International Shipping1083.001083.00

Asia Total12365.323665.322585.4615328.60371.1734315.87


Nancy Tsai:China coal data taken from RAINS-Asia 8.0 model for 1995; raw coal consumption has been scaled down according to info in Sinton and Fridley, 2000, as calculated in subsequent worksheets. Added to that is oil consumption data from RAINS-Asia 7.5.1 (bl_cle scenario) for the year 2000.

Nancy Tsai:Coal use data are from RAINS-Asia 8.0 for 1995, adjusted as in subsequent worksheets. Biofuel and oil emissions from RAINS-Asia 7.5.1 for the year 2000 are added.

Nancy Tsai:China data are from RAINS-Asia 7.5.1 (bl_cle scenario), which includes emissions from local ship traffic.

Nancy Tsai:China data are from RAINS-Asia 7.5.1 (bl_no_control scenario).

Nancy Tsai:All data are from RAINS-Asia 7.5.1, for the year 2000 (bl_cle scenario).



David Streets:Emissions from international shipping, per Streets et al., 2000.

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

Species: NOx (as NO2) Data: EMISSIONS 2000 Version: final Date: 07/01/2002


Anhui115.6719.4955.43131.5825.33347.49

Beijing47.4814.1978.8486.241.35228.10This inventory was prepared by D.G. Streets, Q. Fu, N.Y. Tsai, and K.F. Yarber, Decision and Information Sciences Division, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For further information,

Fujian45.3614.1760.6536.5010.21166.89

Gansu48.6014.7323.7688.6019.39195.08

Guangdong133.5519.14328.45184.3919.26684.78

Guangxi71.0318.2359.2645.0319.80213.35

Guizhou51.1236.2126.6671.9810.02196.00

Hainan2.971.4927.359.462.0343.30

Hebei192.7140.04190.92241.5120.91686.09

Heilongjiang102.9532.8297.17211.9575.03519.93

Henan130.7239.2598.86230.7334.40533.96

Hong Kong6.612.8157.6581.100.00148.17

Hubei170.7741.6776.21218.9323.95531.53

Hunan137.3739.7166.05103.0025.09371.22

Jiangsu194.9735.55165.19290.536.82693.06

Jiangxi87.6321.6235.0657.3331.98233.62

Jilin97.7023.1954.25135.8927.25338.28

Liaoning203.2733.85184.51287.0111.45720.08This inventory is for anthropogenic sources only. It does not include any natural sources, which may be added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Nei Mongol47.3713.6264.86245.40125.88497.13

Ningxia10.262.7315.2976.525.62110.41

Qinghai8.383.088.8628.1545.5293.99

Shaanxi68.8817.7636.71125.479.53258.35

Shandong170.7339.57265.72307.8728.56812.44

Shanghai62.284.44122.08192.141.94382.88

Shanxi190.5425.1566.13269.736.30557.85

Sichuan168.7470.9185.74245.9238.77610.08


Xinjiang20.6317.6836.9030.6566.97172.84

Xizang1.250.764.644.2284.8895.75

Yunnan49.3426.6252.1056.2723.40207.73

Zhejiang102.5220.58111.26193.0912.84440.29

China Total2790.40702.012631.674406.52816.2111346.82

Chugoku, Shikoku75.3218.75122.2257.00

Chubu77.9226.81227.6450.91


Kanto66.4338.92439.2476.77


Kyushu, Okinawa42.3721.55151.1869.17

Japan Subtotal357.96167.721339.97322.839.662198.14

North37.246.6462.1646.30

Pusan14.8626.24298.4440.12

Seoul, Inchon153.4240.54299.6657.24

South21.326.48158.3446.05


Korea, DPR172.700.2233.6758.887.79273.26

Mongolia8.793.5712.7812.96182.58220.68

Taiwan, China158.5318.46218.76123.041.93520.72


Brunei2.370.288.668.910.0020.22

Cambodia5.793.2314.273.6062.2089.09

Indonesia128.21134.40572.81168.97312.981317.36

Laos0.322.2915.400.0577.9295.98

Malaysia71.1113.70209.69142.3457.17494.02

Myanmar1.8716.6830.7013.89162.84225.99

Philippines31.605.56181.0438.8669.10326.15

Singapore34.800.58103.3745.850.00184.60

Thailand92.5534.13561.02209.07189.261086.03

Vietnam28.6538.6256.1526.30133.32283.04


Bangladesh23.6327.5846.2659.7363.09220.29

Bhutan0.351.282.830.743.238.43

India720.34584.231539.761202.81543.764590.90

Nepal1.4316.086.700.3730.4755.05

Pakistan72.8237.47252.83115.4760.67539.26

Sri Lanka7.097.0932.000.8210.3957.39

South Asia Total825.66673.731880.381379.94711.605471.31


Asia Total4938.151895.089981.337151.732801.5626767.85


streets:Compare 179 Gg in 1990, 104 Gg in 1996 (Ng report) and projection of 147 Gg for 2003 (ERM study).

David Streets:Domestic use of raw coal adjusted per SO2 calculations. Replacement energy added as a blend of LF (LPG) and natural gas.

streets:From Hao et al., ES&T, 36, 552-560, 2002:1996: 12.03 Tg1997: 11.66 Tg1998: 11.18 Tg(no biofuels nor HK nor biomass burning); coal consumption continued to decline between 1998 and 2000. Some minor sectoral differences remain.

streets:Compare with 2051 Gg in 1997 from Japan calculations for IPCC communication.

streets:Compare with 1278 Gg in 1997 from official estimates (623 Gg from transport, 203 Gg from power generation).

Year 2000 emissions for North Korea were calculated based on 1995 NOx emissions from RAINS-Asia and adjustment factors based on fuel type for 1995 and 2000 data, to reflect economic downturn.





streets:Compare with 596 Gg for 1996 and projected 565 Gg for 2001 from official estimates.

Fu:recalculated in NOx_S_SE_Asia_2000.xls






























Using ratio of NO2/SO2 from Corbett et al., 1999 and SO2 emissions from Streets et al., 2000, extrapolated from 1995 to 2000. This is international shipping only.

Species: CO2 (as CO2) Data: EMISSIONS 2000 Version: final Date: 07/01/2002

REGIONIndustry (Tg)Domestic (Tg)Transport (Tg)Power (Tg)Cement Product. (Tg)Biofuel Combust. (Tg)Biomass Burning (Tg)Total (Tg)

Anhui45.014.306.7027.388.0425.799.99127.21

Beijing28.4510.1710.3017.983.022.320.5372.77This inventory was prepared by D.G. Streets, Q. Fu, and K.F. Yarber, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For more information, contact [email protected] or call (630) 252-3448. Gridded

Fujian14.353.516.029.786.8822.213.8966.64

Gansu18.835.903.1015.012.6611.485.8462.82

Guangdong48.4811.1837.2045.7321.1319.927.92191.56

Guangxi16.781.997.4110.267.7740.698.4193.31

Guizhou13.4514.373.4921.712.6826.093.8185.61

Hainan0.860.673.432.101.092.540.8211.50

Hebei89.5217.1621.0255.9415.5727.838.25235.28

Heilongjiang39.4711.6311.6548.123.2134.2026.41174.69

Henan48.0413.4011.9353.5414.3239.1513.58193.95

Hong Kong3.693.175.3825.260.580.210.0038.30

Hubei66.5911.219.0949.338.3249.849.40203.79

Hunan34.2710.938.4520.958.5750.269.86143.29

Jiangsu64.559.5620.7966.8716.4945.902.69226.85

Jiangxi15.823.754.5514.774.9548.6212.59105.04

Jilin33.207.756.6030.912.4823.9210.42115.28

Liaoning102.8411.7922.1568.196.4532.844.19248.45

Nei Mongol12.514.229.7064.962.0714.0636.86144.39This inventory is only for direct combustion from anthropogenic sources. It does not include natural sources, nor CO2 uptake. Biomass burning values are annual average emissions typical of the mid-1990s.

Ningxia3.720.892.259.500.942.671.7921.75

Qinghai3.631.531.112.470.441.6512.4023.23

Shaanxi28.166.204.8221.243.7317.213.7385.09

Shandong63.6013.2527.8874.7022.4143.7311.29256.85

Shanghai37.192.7112.0635.470.951.320.7790.46

Shanxi83.2910.097.9150.435.383.412.45162.96

Sichuan79.6119.808.8145.4414.2783.0115.04265.99

Tianjin18.167.468.2417.710.972.350.6955.59

Xinjiang13.7310.044.572.780.155.6118.3855.25NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Xizang0.540.230.570.373.020.8123.5929.13

Yunnan24.207.726.689.486.1133.4112.42100.02

Zhejiang30.495.8311.5631.6814.3030.665.06129.59

China Total1083.03242.41305.42950.06208.92743.72283.093816.64

Chugoku, Shikoku89.5411.0225.3865.593.50

Chubu66.7916.2947.2867.073.47

Hokkaido, Tohoku24.7618.8133.2640.506.97No data

Kanto69.7524.4991.24103.154.47

Kinki70.7916.2649.7746.804.78Not applicable

Kyushu, Okinawa35.5612.7931.4183.304.37

Japan Subtotal357.1999.66278.33406.4130.1427.553.681202.96

North27.274.536.0325.411.18

Pusan7.0618.1031.2624.150.25

Seoul, Inchon86.5729.7432.4233.840.82

South15.572.0815.5923.961.91

Korea, Rep of, Subtotal136.4854.4485.30107.3520.724.162.75411.19

Korea, DPR58.361.734.0817.286.4029.682.84120.37

Mongolia3.763.440.644.680.043.9852.2868.82

Taiwan, China82.949.0535.2471.000.080.520.84199.66

Other East Asia Total638.73168.32403.57606.7357.3865.8962.382003.00

Brunei5.940.461.381.440.150.670.0010.04

Cambodia2.110.201.781.260.048.3022.1635.85

Indonesia95.3637.8169.6751.669.42173.68149.79587.40

Laos0.670.031.090.020.003.2338.8443.88

Malaysia28.964.8027.9228.254.5213.5336.10144.07

Myanmar2.020.833.752.290.1939.3596.88145.31

Philippines18.897.8321.6820.795.2740.4137.38152.25

Singapore18.850.216.8518.451.2410.330.0055.93

Thailand39.014.5965.2861.5112.8179.9087.82350.93

Vietnam12.443.187.248.282.6482.7452.68169.20

Southeast Asia Total224.2559.92206.65193.9536.28452.15521.651694.86

Bangladesh9.025.724.9110.790.3862.2030.15123.16

Bhutan0.140.090.380.120.061.791.113.69

India310.8860.51171.28428.6932.77682.62199.281886.03

Nepal1.581.050.790.160.1124.7810.8039.27

Pakistan35.9613.2828.0734.013.3985.0221.62221.35

Sri Lanka1.331.533.760.370.4514.356.5128.31

South Asia Total358.9182.18209.20474.1337.17870.77269.462301.81


Asia Total2304.92552.831176.122224.86339.752132.531136.599867.59


David Streets:Taken from Asia_bioburn_2000_final.xls. NOTE: This category represents the direct emissions from combustion of forests, agricultural residues in fields, and savanna. If net C releases are required, this category may be omitted or adjusted to reflect C absorption in subsequent vegetation re-growth.

Species: CO (as CO) Data: EMISSIONS 2000 Version: final Date: 07/01/2002

REGIONIndustry (Gg)Domestic Biofuels (Gg)Domestic Fossil Fuels (Gg)Transport (Gg)Biomass Burning (Gg)Total (Gg)

Anhui815.681348.19154.221116.06608.024042.16

Beijing545.04123.36295.531731.3132.402727.64This inventory was prepared by D.G. Streets, Q. Fu, D. He, N.Y. Tsai, M.Q. Wang, and K.F. Yarber, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contact: [email protected]

Fujian254.59944.5585.29742.05243.212269.70

Gansu281.79574.44216.70606.21275.451954.58

Guangdong677.71681.38218.914322.84484.696385.52

Guangxi574.391544.8255.21994.96518.423687.80

Guizhou303.691313.22635.94524.36238.583015.80

Hainan12.8786.785.52410.3049.71565.18

Hebei1412.211454.91646.752791.35500.606805.82

Heilongjiang431.371849.99314.501226.971677.245500.07

Henan788.352046.56450.541858.37825.885969.71

Hong Kong0.001.590.36125.460.00127.41

Hubei833.992451.04434.541226.20574.255520.01

Hunan831.142226.66517.041161.97601.935338.75

Jiangsu1042.282475.39251.682150.41163.686083.44

Jiangxi516.741515.88166.81558.32767.333525.08

Jilin538.151293.99284.79763.95628.423509.29

Liaoning1141.531776.34462.472484.54240.946105.81This inventory is for anthropogenic sources only. It does not include natural sources, which may be added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Nei Mongol555.58734.11165.44807.221784.514046.87

Ningxia49.50139.3235.27199.1489.32512.55

Qinghai46.1179.3064.50212.36508.15910.41

Shaanxi328.01861.63211.19862.28227.682490.80

Shandong1016.512392.75334.772908.84685.897338.76

Shanghai831.0766.61102.001111.5246.652157.85

Shanxi1541.74179.24326.701058.58147.923254.19

Sichuan1245.854277.95709.301839.48915.618988.19


Xinjiang80.26268.83432.86970.85762.392515.19

Xizang4.8838.659.6894.37997.931145.51

Yunnan362.601540.27287.231033.62795.864019.58

Zhejiang573.401366.36121.021138.37308.193507.34

China Total17998.4735789.098217.1238001.0915742.90115748.66

Chugoku, Shikoku

Chubu

Hokkaido, TohokuNo data

Kanto

KinkiNot applicable

Kyushu, Okinawa

Japan Subtotal1438.00303.1011.414823.42230.126806.05

North

Pusan

Seoul, Inchon

South


Korea, DPR1105.801426.343.29836.56183.663555.65

Mongolia109.65115.523.17122.882509.532860.75

Taiwan, China616.5426.641.481430.0952.262127.01


Brunei0.006.921.476.540.0114.93

Cambodia10.68483.541.4874.241137.511707.44

Indonesia186.5610521.33215.913219.628961.4023104.81

Laos0.00163.560.1931.032352.172546.95

Malaysia30.05425.0422.572705.032369.805552.48

Myanmar29.411666.230.79482.466266.998445.87

Philippines92.671212.9727.19369.812398.884101.52

Singapore0.000.000.02138.060.00138.08

Thailand329.641482.4318.323757.005227.2910814.68

Vietnam48.914314.7163.701904.472916.689248.47


Bangladesh96.982672.3827.19131.171899.124826.84

Bhutan0.1690.310.276.2375.26172.22

India6028.3528988.60153.8515910.6412258.3063339.73

Nepal8.321235.8121.74106.53714.482086.88

Pakistan308.874099.2893.081345.261229.187075.68

Sri Lanka7.08609.799.50294.84427.121348.33

South Asia Total6449.7637696.16305.6317794.6616603.4678849.68


Asia Total29150.8295721.938899.7077671.3467120.34278564.14

David Streets:Taken from file Asia_bioburn_2000_final.xls.

David Streets:Due to difficulties in estimating Japanese industrial CO emissions, current methodology is replaced by official estimate obtained from national greenhouse gas inventory.

streets:Due to great difficulty in estimating industrial emissions in ROK, current methodology has been replaced by scaling of Japanese emissions by ratio of steel production. This value should be considered highly uncertain.

David Streets:Estimated as ratio to NOx emissions, using EPA AP-42 emissions factor from Table 1.3-1. This is international shipping only.

Species: CH4 (as CH4) Data: EMISSIONS 2000 Version: final Date: 07/01/2002

REGIONRice Cultivation (Gg)Animal Emissions (Gg)Landfills (Gg)Wastewater Treatment (Gg)Coal Combustion+Production (Gg)Natural Gas and Oil (Gg)Biofuel Combustion (Gg)Biomass Burning (Gg)Total (Gg)

Anhui756.12348.80121.03230.04299.045.84109.4318.031888.32

Beijing1.7819.56120.4453.1165.915.8410.270.97277.86This inventory was prepared by S. Fernandes, D.G. Streets, and K.F. Yarber, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contact: [email protected] or call (630) 252-344

Fujian362.8487.18100.24133.3928.300.8987.078.06807.96

Gansu0.47249.8734.7498.46120.671.5347.169.08561.97

Guangdong764.77249.91406.13332.1132.6918.7964.2816.061884.74

Guangxi665.56460.5543.15172.5132.550.20134.2718.771527.56

Guizhou173.14381.8061.19135.47221.200.48103.557.921084.75

Hainan111.9080.4426.1230.240.210.008.191.53258.63

Hebei21.01499.82225.47259.17394.7513.30118.0914.761546.38

Heilongjiang126.87267.38282.61141.77381.5435.39146.4564.561446.56

Henan78.13886.39203.93355.71478.8213.73166.1124.432207.25

Hong Kong0.000.00340.8226.060.002.610.130.00369.61

Hubei776.25263.17260.49231.6669.121.07205.7217.271824.75

Hunan1041.74349.39132.49247.49149.310.35200.8718.062139.68

Jiangsu1060.99142.04238.00285.84183.033.41199.934.822118.07

Jiangxi834.04221.6448.63159.1093.470.21193.6222.931573.64This inventory is for anthropogenic sources only. It does not include emissions from natural sources such as wetlands, which are added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Jilin30.99243.03177.60104.84123.994.57102.4319.36806.81

Liaoning43.09179.15309.71162.87342.9035.49140.627.321221.14

Nei Mongol10.06396.0376.6291.31425.691.4960.3366.191127.73

Ningxia5.3753.0214.0621.6095.800.8711.452.85205.01

Qinghai0.00271.1817.6919.9114.261.916.9317.88349.74

Shaanxi17.23174.4669.04138.54175.4710.1070.736.89662.46

Shandong35.36706.14330.96348.91559.4021.25191.9120.142214.07

Shanghai82.5612.46274.3164.339.5917.865.331.37467.80

Shanxi0.28181.30177.32126.701306.306.9714.834.431818.14

Sichuan2279.67802.70235.75438.83332.5263.49343.7227.954524.64NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Tianjin3.8217.43110.6838.4718.448.5310.711.23209.31

Xinjiang7.81406.0547.9173.98149.9417.4123.4926.67753.27

Xizang0.07332.186.1510.070.830.003.3834.67387.34

Yunnan78.18534.5944.98164.79114.740.20131.5642.771111.80

Zhejiang414.5159.16174.74179.7417.042.14122.529.14978.98

China Total9784.618876.804712.984877.006237.49295.933035.05536.1238355.9737819.86

Chugoku, Shikoku

Chubu


Kanto

KinkiNot applicable

Kyushu, Okinawa

Japan Subtotal225.14341.28360.0010.006.67167.9623.867.631142.541134.91

North

Pusan

Seoul, Inchon

South

Korea, Rep of, Subtotal288.02147.62561.52231.0049.27143.406.965.051432.841427.79

Korea, DPR134.8247.1555.83105.00882.970.00112.276.921344.96

Mongolia0.00347.352.8113.000.320.0011.0897.17471.73

Taiwan, China104.8247.22248.69107.674.1043.712.102.02560.34

Other East Asia Total752.81930.631228.87466.67943.33355.06156.25118.784952.414833.63

Brunei0.530.801.906.680.0039.520.710.0050.14

Cambodia352.62197.825.5556.740.210.0038.9256.58708.44

Indonesia1926.70953.24702.021057.0094.96328.84853.92526.216442.89

Laos73.38123.932.5131.370.000.0013.26142.62387.07

Malaysia126.1163.8597.37107.762.27272.1847.30152.26869.11

Myanmar1153.55712.6153.36203.000.0014.75160.63392.802690.69

Philippines1019.87481.32371.11394.000.340.05163.40132.872562.95

Singapore0.001.6118.4920.000.003.3341.790.0085.22

Thailand1574.89456.34142.86298.0053.71443.47304.95293.003567.21

Vietnam1343.14533.1772.23383.0051.0521.44356.78146.582907.39

Southeast Asia Total7570.793524.711467.392557.55202.541123.581981.651842.9120271.1118428.20

Bangladesh1159.711384.0355.75584.001.0327.57307.8887.593607.55

Bhutan2.1822.550.296.250.000.007.363.2741.91

India4312.9917853.871172.014673.001004.07847.772566.20421.5132851.4332429.92

Nepal91.66568.456.03120.001.600.00100.6828.77917.19

Pakistan475.322877.72214.39668.0016.93706.35418.0638.635415.40

Sri Lanka87.69129.249.1891.760.000.0061.5627.26406.69

South Asia Total6129.5522835.871457.656143.011023.631581.693461.73607.0443240.1742633.14


Asia Total24237.7536168.008866.8814044.238407.003357.358634.683104.85106820.75


streets:42341 Gg (EPA)

streets:Calculated value over-ridden by a value based on Japan's IPCC submission. We presume that the difference is either due to waste composition or landfill methane capture and use. We do not have sufficiently detailed information to ascertain the reason, so we prefer to use the Japan value.

streets:Calculated value over-ridden by a value based on the Japan IPCC submission. We presume that there must be control of emissions from wastewater treatment in Japan.

streets:276 Gg (EPA); 1389 Gg (IPCC inventory by Japanese government)



streets:318 Gg (EPA); Differences accounted for in oil and gas numbers


streets:2645 Gg (EPA); most of the difference is from livestock and landfills.

streets:1845 Gg (EPA); Different in most categories.

streets:79 Gg (EPA); Differs in Biofuels

streets:4619 Gg (EPA); Differences in Animal, Biofuel, Landfills


streets:2202 Gg (EPA). Differences in several categories, but mainly animals.

streets:22272 Gg (EPA); different in many categories, but mainly animals.


streets:4705 Gg (EPA); different in several categories, but mainly animals.

streets:From AP-42 emission factor for commercial diesel engine using low-grade oil.

Species: NMVOC (full MW) Data: EMISSIONS 2000 Version: final Date: 07/01/2002


Anhui106.53201.70121.283.01104.18536.70The data for China were developed by Z. Klimont (IIASA) and D.G. Streets. Data for other East Asian countries were developed by Z. Klimont et al., for CRIEPI. The data set for East Asia is presented by permission of CRIEPI and IIASA. The data for Southeas

Beijing105.6953.16228.581.635.56394.63

Fujian85.57140.51133.030.9143.59403.61

Gansu57.4198.7159.991.3643.82261.29

Guangdong240.94130.17829.105.4483.481289.13

Guangxi76.36215.57145.363.0689.96530.31

Guizhou56.14223.1874.321.8542.80398.28

Hainan15.8916.20110.230.388.52151.23

Hebei176.93249.32338.045.4885.54855.32

Heilongjiang215.93274.43160.273.97318.31972.91

Henan164.98316.80227.055.24141.36855.43

Hong Kong50.6730.3023.614.220.00108.80

Hubei176.52369.24152.693.7898.95801.19

Hunan108.41338.94165.601.96103.60718.51

Jiangsu250.44347.84228.566.0127.97860.81

Jiangxi79.67221.53162.541.54131.86597.15

Jilin105.57194.53100.322.56108.62511.59

Liaoning254.33278.20331.275.3840.71909.88

Nei Mongol61.10115.84100.083.44300.83581.29This inventory is for anthropogenic sources only. It does not include biogenic emissions, which are added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Ningxia13.8322.3321.040.8314.4572.48

Qinghai9.9215.0120.840.3676.69122.83

Shaanxi68.66139.39100.881.9539.30350.19

Shandong315.29346.96411.056.89117.081197.28

Shanghai192.1430.01156.003.977.96390.09

Shanxi167.6774.83127.885.7225.36401.46

Sichuan223.36618.34227.763.72158.311231.49

Tianjin78.0139.14133.552.027.17259.89

Xinjiang69.5765.2785.860.39115.62336.70NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Xizang3.235.859.520.05152.70171.35

Yunnan67.51238.72143.200.98143.96594.37

Zhejiang123.48198.42187.822.8352.80565.36

China Total3721.745610.435317.3690.942691.0917431.56

Chugoku, Shikoku107.3555.6648.785.28

Chubu126.2299.3982.603.80


Kanto289.96183.01164.794.67


Kyushu, Okinawa65.7168.4357.758.91

Japan Subtotal767.48583.36500.1327.9941.251920.21

North106.5254.1826.681.57

Pusan38.0720.47107.941.10

Seoul, Inchon105.3054.99180.231.16

South295.4669.2066.931.86


Korea, DPR69.3826.4799.823.5934.80234.05

Mongolia6.544.8011.350.61428.72452.02

Taiwan, China263.6777.66154.875.039.12510.34


Brunei38.081.553.470.050.0043.16

Cambodia4.7456.6551.640.14192.20305.37

Indonesia825.863282.821183.442.461608.066902.64

Laos3.0022.2535.510.00424.84485.60

Malaysia301.35219.49462.701.13439.111423.78

Myanmar73.69224.11219.110.101154.151671.17

Philippines129.73615.28216.970.49435.331397.80

Singapore33.5015.6630.990.640.0080.79

Thailand332.76646.421137.043.03932.283051.54

Vietnam52.47536.15296.340.59504.631390.17


Bangladesh85.92317.8577.840.35337.45819.42

Bhutan0.6912.037.850.0115.2435.82

India1069.754366.293141.8153.412212.4110843.67

Nepal7.05162.8136.440.00139.58345.88

Pakistan174.58524.10434.681.15209.111343.62

Sri Lanka24.9087.4783.170.0179.07274.62

South Asia Total1362.905470.543781.7954.932992.8713663.03


Asia Total8532.2317592.5013910.86197.4111917.3452150.35

klimont:Includes - combustion in industry- oil industry, extraction and processing (not distribution of products, which are included in Transport)- all solvent use in industry- chemical industries, printing etc.- process emissions, e,g, coke

klimont:Includes- residential combustion- domestic solvents- architectural paint use- dry cleaning

klimont:Includes only public power plants

David Streets:Taken from file Asia_bioburn_2000_final.xls.

streets:Compare 57 Gg for 1990 and 48 Gg for 1996 (Ng).

streets:Compare with 1916 Tg for 1997 from Japan calculations for IPCC communication.

streets:Compare with 162 Gg HC (not full VOC) for 1997 from Korea environment yearbook. Seems greatly under-estimated.

streets:Compare 827 Gg for 1996 and projected 800 Gg for 2001 (NMHC) from Taiwan environment yearbook.

David Streets:Emissions for international shipping scaled to NOx emissions using Table 1.3-3 of US EPA AP-42.

Species: NMVOC (full MW) Data: SPECIATED EMISSIONS 2000 (Gg) Version: final Date: 07/01/2002

REGIONEthanePropaneButanesPentanesOther AlkanesEthenePropeneTerminal AlkenesInternal AlkenesAcetyleneBenzeneTolueneXylenesOther AromaticsHCHOOther AldehydesKetonesHalocarbonsOtherTotal

Anhui32.1318.3020.6615.1947.4957.7324.8915.5520.5427.6821.7339.3715.4927.3314.2714.9520.068.3795.30537.07

Beijing13.1920.8831.6126.9060.2225.3810.089.5111.2118.028.3835.5416.2433.857.353.495.365.1152.57394.88

Fujian20.7613.5116.7513.8441.7742.6917.5411.0417.1623.6315.8227.6911.4429.308.189.119.045.4869.01403.77

Gansu15.7010.0013.438.8426.3726.0510.299.599.3113.7710.2318.818.6414.135.176.698.603.3042.53261.45

Guangdong37.9055.82114.5195.03197.3595.6839.9325.5146.7049.1323.8186.4845.53121.6227.8017.0920.1316.04173.081289.15

Guangxi33.1818.0721.6517.0244.4664.6926.8116.4124.7430.0024.0534.3413.9531.0912.9415.3216.185.4680.13530.50

Guizhou31.4713.4315.119.3630.7750.4019.4014.2718.4528.2722.5026.8314.7319.796.8910.288.112.9755.42398.46

Hainan4.005.0112.1210.6424.4712.124.962.816.215.232.9711.885.7616.052.931.942.231.0718.90151.31

Hebei47.1637.7552.7740.2398.9382.1333.2623.3831.2445.4131.6057.3531.8156.3017.3116.3218.3310.43123.95855.68

Heilongjiang52.6136.9239.9826.8396.4087.5337.5828.4228.9839.2433.2057.1522.1343.3138.0729.5042.7511.22221.60973.39

Henan51.7532.0737.0327.4582.4790.1838.3424.3132.9346.3933.8859.2526.5346.6720.8622.0127.5411.41144.79855.88

Hong Kong1.562.1818.063.4926.182.701.121.711.080.800.736.733.624.001.310.741.573.2028.60109.40

Hubei54.8131.5131.6623.8372.0791.8138.3525.6934.2048.0238.2958.8424.5139.6817.1021.1921.0410.31118.87801.78

Hunan49.6925.9027.5919.9857.9287.8436.6123.4033.3044.4034.9947.7721.6238.9416.0220.1719.757.47105.49718.85

Jiangsu46.8234.2646.4532.3991.3889.1634.7924.7435.8147.9435.7469.5326.9753.3811.5818.4514.3217.70129.98861.40

Jiangxi38.0117.5019.2115.1053.7072.1231.0218.1226.2131.5025.8137.0816.7935.7117.4518.4922.035.9095.66597.43

Jilin31.5818.5818.3213.7445.1953.1323.5016.2118.8526.7420.0439.7114.5526.8414.4714.3420.185.8990.09511.95

Liaoning50.0345.5459.9048.51123.9383.4534.2524.6633.9944.4834.3067.9430.6158.2615.3214.6414.4011.82114.38910.42

Nei Mongol26.8714.9215.6211.4236.8852.7620.9318.4613.9823.0119.3629.2113.5023.3723.6624.1340.105.58167.70581.46

Ningxia3.922.743.372.507.586.912.872.032.454.552.535.062.483.841.661.842.650.7512.7972.52

Qinghai5.042.943.052.047.9310.283.823.912.224.923.255.622.513.853.666.2311.191.2939.08122.84

Shaanxi21.3513.0515.9212.0434.8538.3215.2610.0914.2219.2615.3027.0311.9620.647.078.158.823.9953.12350.43

Shandong60.5552.6671.4554.93146.68112.9547.7731.1644.1766.5039.9573.9535.6873.0225.2124.4726.0717.44193.211197.83

Shanghai10.5317.0136.6920.4566.5428.4010.208.537.8912.918.1731.4912.7827.295.764.337.0513.0761.29390.39

Shanxi29.0814.9421.5815.2740.9764.9412.039.417.7418.7234.3028.6116.9421.625.573.747.034.6044.65401.75

Sichuan91.9446.6743.3629.73105.16155.2862.4840.0257.5175.5164.6683.4533.8460.7224.5034.7231.6313.40177.521232.09

Tianjin10.5714.5821.1817.9943.6316.237.035.267.3810.905.5522.8110.4120.074.822.424.123.6431.48260.04

Xinjiang16.5115.2817.2613.2838.1824.179.659.177.1314.748.5221.639.6214.788.2110.5818.723.7775.69336.90

Xizang6.803.081.691.104.9215.005.925.881.885.644.093.591.372.317.5311.4221.201.5966.28171.30

Yunnan40.7320.0522.3316.4944.7074.4728.0619.3025.3334.1628.0634.4416.0330.9616.8718.4321.365.0097.84594.61

Zhejiang30.0622.0427.8422.8257.3859.1625.0015.9824.3130.2322.1838.5916.6140.1211.2113.9112.309.6586.19565.57

China Total966.28677.20898.16668.421856.451773.65713.77494.53647.13891.71674.011187.80534.651038.84400.74419.09503.88226.972867.2117440.48

Japan28.9837.44170.4673.47300.2059.5529.9924.9026.1020.1915.94132.3561.5691.0928.098.8546.1269.24700.461924.96

Korea, Rep of25.5235.14138.9763.30243.7357.0730.9019.5322.0625.3413.5785.7533.5968.0619.757.2821.2525.72227.041163.59

Korea, DPR9.126.1820.0615.7936.2211.284.535.715.536.306.0321.1111.0416.546.312.606.302.8141.01234.46

Mongolia14.245.453.812.7410.1631.0912.4016.303.7310.1510.659.304.044.8929.6427.8250.762.64202.09451.89

Taiwan, China9.4814.9982.0529.71120.3714.788.617.228.686.544.4842.4414.6826.518.612.2610.3112.2787.79511.76

Other East Asia Total87.3399.19415.35185.01710.68173.7786.4473.6766.1168.5350.67290.94124.90207.0992.4048.80134.74112.671258.384286.66

Brunei6.756.384.972.5817.180.220.100.140.190.060.290.540.330.530.090.040.190.621.9543.15

Cambodia17.463.867.015.0115.0535.3712.1110.459.0611.5910.658.794.1910.0613.5915.2724.521.3290.08305.45

Indonesia476.63177.93188.16126.23607.68872.00329.46238.99305.01337.90465.10356.10127.63300.78164.87219.28229.6447.271347.726918.38

Laos30.494.805.343.5213.6354.3516.2216.107.9613.0912.439.693.607.9732.8427.2650.691.25174.52485.75

Malaysia87.2448.7773.7653.28221.83121.3242.1335.4740.5438.6440.6064.0730.7682.8946.1836.6357.9214.25289.281425.57

Myanmar107.5120.6926.3619.2881.83196.2262.9453.5541.6453.8651.3452.5619.7050.2297.9980.57139.166.31510.101671.83

Philippines75.2639.2936.9723.20104.21147.1558.6140.6745.1458.5861.2689.2424.2450.3746.0043.9766.7019.12373.981403.96

Singapore0.952.225.984.1310.904.271.682.070.971.111.024.131.323.294.172.601.808.8719.4380.91

Thailand147.1865.47120.5695.00355.07307.03117.7389.35104.26107.83146.43148.8572.90196.5397.4088.02126.8821.81648.053056.35

Vietnam94.7128.4935.0626.6989.82191.3870.5948.4764.4572.3867.7054.3525.8366.1845.1956.4165.765.57281.691390.73

Southeast Asia Total1044.19397.91504.15358.921517.211929.32711.59535.25619.24695.04856.82788.32310.48768.82548.30570.06763.26126.403736.8016782.07

Bangladesh61.7924.0720.1412.3145.31105.3143.3927.2432.3439.2335.9429.289.8423.4033.7833.9545.766.18190.96820.21

Bhutan1.820.561.280.931.983.881.561.321.521.491.631.370.691.591.731.341.510.079.5635.83

India668.76372.61498.57373.47912.631360.98559.74363.23526.60587.75517.15489.93237.82551.47313.23335.01307.0470.871802.9310849.78

Nepal23.157.395.123.5813.3246.5819.2013.5316.6118.6419.2211.985.4912.2715.7614.6514.971.1483.42345.99

Pakistan88.0255.9579.1654.62138.09157.9965.7742.3264.4971.3858.6060.1227.0767.8430.8437.5333.069.51201.491343.84

Sri Lanka16.305.4512.659.7623.6933.6311.828.9512.3212.4611.5913.265.6814.768.668.8910.271.2253.42274.77

South Asia Total859.84466.03616.91454.651135.031708.35701.48456.60653.87730.96644.12605.94286.58671.32404.00431.39412.6188.982341.7813670.41

International Shipping0.850.000.000.000.008.605.206.130.003.402.360.000.000.000.000.000.000.000.0026.54

Asia Total2958.491640.332434.561667.005219.375593.692218.461566.181986.352389.642227.982872.991256.612686.071445.431469.331814.50555.0210204.1652206.17

NOTE: There are rounding errors of about 0.1% in the totals.

SHARES:alkanesalkenesacetylenearomaticsald/ketonesother

China0.2910.2080.0510.1970.0760.177Note: This data set is consistent with the previous NMVOC worksheet. It includes annual biomass burning, but it does not include biogenic emissions.

Other East Asia0.3490.0930.0160.1570.0640.320

Southeast Asia0.2280.2260.0410.1620.1120.230

South Asia0.2580.2580.0530.1620.0910.178

Asia0.2670.2180.0460.1730.0910.206

streets:For calculations, see the files speciation_EAsia_2000_final.xls and speciation_SE&SAsia_2000_final.xls.

Species: BC (as C) Data: EMISSIONS 2000 Version: final Date: 07/01/2002


Anhui4.0418.342.720.124.5329.75

Beijing3.803.301.550.510.249.39This inventory was prepared by D.G. Streets, N.Y. Tsai, and K.F. Yarber, Argonne National Laboratory, and T.C. Bond, NOAA/PMEL, Seattle, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contact: dstree

Fujian1.0915.531.160.041.6819.49

Gansu1.5617.151.190.062.0422.00

Guangdong3.0913.195.160.803.5825.82

Guangxi0.7321.971.780.033.7728.28

Guizhou0.4045.780.800.031.6548.66

Hainan0.041.160.380.040.371.99

Hebei9.5544.094.740.113.7462.23

Heilongjiang1.9036.962.400.3310.2651.85

Henan3.7846.354.130.126.1760.54

Hong Kong0.250.020.440.090.000.80

Hubei6.1451.631.850.244.2464.10

Hunan1.3251.882.020.064.4559.74

Jiangsu4.4337.873.790.221.2347.53

Jiangxi0.5327.090.890.055.6934.26

Jilin2.3729.081.350.174.5937.57

Liaoning9.9642.993.061.161.7958.96This inventory is for anthropogenic sources only. It does not include any natural sources, which may be added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Nei Mongol0.3417.191.860.1212.0731.58

Ningxia0.073.410.510.030.674.69

Qinghai0.343.970.450.013.758.51

Shaanxi2.6919.001.460.081.6824.90

Shandong4.5845.174.500.735.1460.12

Shanghai4.331.480.950.650.357.76

Shanxi6.6817.041.870.121.1026.81

Sichuan7.0785.912.310.106.71102.09


Xinjiang1.4423.031.520.015.6331.62

Xizang0.050.980.160.007.368.55

Yunnan3.2231.961.730.035.3542.29

Zhejiang1.8020.221.890.232.3026.43

China Total88.94780.7359.776.79112.411048.64

Chugoku, Shikoku1.851.42

Chubu2.043.26

Hokkaido, Tohoku0.541.88No data

Kanto1.044.32

Kinki1.202.46Not applicable

Kyushu, Okinawa0.711.30

Japan Subtotal7.377.3422.3914.641.5953.34

North0.410.46

Pusan0.101.33

Seoul, Inchon4.941.00

South0.220.45


Korea, DPR1.6718.521.090.001.1422.42

Mongolia0.181.850.100.0116.4218.56

Taiwan, China3.620.700.892.600.388.19


Brunei0.020.060.000.000.000.08

Cambodia0.096.380.130.027.8314.45

Indonesia2.98136.967.090.4658.89206.38

Laos0.032.170.070.0015.3117.58

Malaysia1.685.603.260.7515.1326.41

Myanmar0.0522.072.670.0040.3265.11

Philippines1.6516.161.280.7815.9935.86

Singapore1.040.000.351.700.003.10

Thailand2.1919.2314.031.5934.8071.83

Vietnam0.7259.068.240.1419.9888.14


Bangladesh0.0837.851.070.0313.2252.26

Bhutan0.021.210.050.000.401.68

India18.26425.1072.820.9682.79599.92

Nepal0.0616.450.660.004.1521.32

Pakistan1.6764.109.091.668.9785.49

Sri Lanka0.096.451.390.022.7310.69

South Asia Total20.18551.1785.082.66112.27771.36


Asia Total138.081631.84282.0135.40453.692541.02


David Streets:Scaled to NOx emissions, using BC emission factor for diesel-fired commercial/institutional boiler.

Species: OC (as C) Data: EMISSIONS 2000 Version: final Date: 07/01/2002


Anhui0.9587.792.050.0922.45113.32

Beijing0.709.401.420.381.2213.12This inventory was prepared by D.G. Streets, N.Y. Tsai, and K.F. Yarber, Argonne National Laboratory, and T.C. Bond, NOAA/PMEL, Seattle, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contact: dstree

Fujian0.4063.651.150.0311.8677.09

Gansu0.5544.960.920.0412.6359.10

Guangdong1.7947.555.430.6018.1173.46

Guangxi0.72101.321.580.0219.99123.63

Guizhou0.33108.120.660.0211.69120.83

Hainan0.025.630.570.031.818.07

Hebei1.71114.433.930.0818.19138.34

Heilongjiang1.12129.981.910.25112.09245.35

Henan1.05148.223.240.0930.26182.86

Hong Kong0.190.100.430.060.000.78

Hubei1.87174.361.570.1822.03200.02

Hunan1.23162.441.700.0522.92188.33

Jiangsu2.02164.543.430.175.92176.09

Jiangxi0.51103.900.780.0428.90134.14

Jilin1.0293.531.100.1325.58121.36

Liaoning2.57130.892.740.879.37146.44This inventory is for anthropogenic sources only. It does not include natural sources, which may be added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Nei Mongol0.3355.261.610.09106.91164.20

Ningxia0.0710.600.410.023.8214.94

Qinghai0.117.970.350.0126.3434.78

Shaanxi0.5662.001.210.068.8772.70

Shandong1.93165.973.920.5524.66197.02

Shanghai0.834.820.860.491.678.67

Shanxi1.1023.521.520.095.6131.85

Sichuan1.29300.921.960.0836.48340.72


Xinjiang0.6134.801.240.0139.1175.78

Xizang0.023.110.130.0050.5753.83

Yunnan0.42109.171.430.0236.16147.20

Zhejiang0.9890.261.660.1711.37104.45

China Total27.612572.2051.975.10728.093384.96

Chugoku, Shikoku0.701.07

Chubu1.512.45

Hokkaido, Tohoku0.391.41No data

Kanto0.623.24

Kinki0.641.84Not applicable

Kyushu, Okinawa0.530.98

Japan Subtotal4.3922.1624.8210.9811.2373.57

North0.060.34

Pusan0.011.00

Seoul, Inchon3.710.75

South0.030.34


Korea, DPR1.6592.110.950.0011.79106.50

Mongolia0.189.110.090.01163.84173.22

Taiwan, China2.571.991.081.952.079.66


Brunei0.010.280.000.000.000.30

Cambodia0.0831.900.180.0156.8889.06

Indonesia2.27681.3510.210.35443.601137.78

Laos0.0210.870.100.00117.68128.67

Malaysia1.2827.513.940.56117.44150.73

Myanmar0.03110.092.450.00308.46421.04

Philippines1.1678.641.280.59110.69192.35

Singapore0.780.000.401.280.002.46

Thailand1.7195.7211.811.20253.21363.64

Vietnam0.58285.116.510.10139.73432.02


Bangladesh0.06186.660.880.0380.61268.24

Bhutan0.016.040.040.006.3112.40

India9.932106.8172.170.72647.732837.36

Nepal0.0582.110.580.0052.19134.93

Pakistan1.03305.887.841.2452.03368.03

Sri Lanka0.0732.011.400.0121.1054.59

South Asia Total11.152719.5282.912.00859.973675.55


Asia Total59.286746.34256.9826.553331.1010420.25


David Streets:Scaled to NOx emissions using emission factor for diesel-fired commercial/institutional boilers.

Species: NH3 (as NH3) Data: EMISSIONS 2000 Version: final Date: 07/01/2002

REGIONCattle (Gg)Pigs (Gg)Other Animals (Gg)Fertilizer Use (Gg)Biofuel Use (Gg)Biomass Burning (Gg)Other Sources (Gg)Total (Gg)

Anhui58.8985.3937.18417.2222.618.5840.63670.50Data for China and East Asia were developed by Z. Klimont (IIASA). The entire data set for China and East Asia is presented by permission of CRIEPI and IIASA. Data for Southeast Asia and South Asia were developed by D. G. Streets and S. Nelson, Argonne Na

Beijing1.8913.8211.2927.992.070.468.2665.79

Fujian11.0360.0718.96203.1915.843.3721.96334.43

Gansu34.5937.5540.2367.959.644.2115.45209.60

Guangdong38.63119.1199.30392.1211.436.7744.81712.17

Guangxi66.64132.2745.51212.7025.917.1629.72519.91

Guizhou54.3187.6814.48145.1522.033.3122.86349.80

Hainan12.1216.1610.7630.861.460.705.9277.98

Hebei59.27111.9586.69506.7524.407.0744.24840.38

Heilongjiang64.8962.1942.55129.5831.0322.7924.94377.97

Henan112.60145.4971.54696.7234.3311.6662.241134.57

Hong Kong0.030.530.730.000.030.004.005.32

Hubei35.57133.5729.26510.5041.118.0839.74797.84

Hunan39.14184.9428.02380.2437.358.4843.57721.73

Jiangsu9.97115.5793.58696.0541.522.3147.831006.83

Jiangxi31.72106.4023.79236.4225.4310.8126.45461.03

Jilin38.6641.6947.48156.8121.718.8616.67331.88

Liaoning27.9280.0859.45170.3029.803.4627.51398.52

Nei Mongol45.4741.7675.49102.9412.8226.7115.75320.94This inventory is for anthropogenic sources only. It does not include natural sources, which may be added to the gridded inventory off-line. Biomass burning values are annual average emissions typical of the mid-1990s.

Ningxia5.875.018.5727.662.431.343.7254.60

Qinghai37.155.9425.858.061.488.163.0189.64

Shaanxi24.6749.4319.47169.2414.453.2023.22303.69

Shandong117.69135.55252.11478.2840.149.6959.491092.95

Shanghai1.4711.3730.7422.511.120.669.5377.40

Shanxi24.5430.6024.29101.713.012.0922.07208.31

Sichuan92.50386.7282.81387.6871.7612.8977.101111.45

Tianjin2.354.806.6419.382.260.596.1942.23

Xinjiang40.427.3862.54113.385.0012.1812.54253.45NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Xizang44.921.0826.222.590.7215.832.0093.36

Yunnan66.21125.1732.43200.8125.8410.3726.58487.41

Zhejiang4.4570.1719.02268.9922.924.3528.24418.14

China Total1205.592409.431427.026883.76601.65226.12816.2413569.80

Chugoku, Shikoku6.873.4012.674.330.897.35

Chubu7.776.0212.236.310.8413.52

Hokkaido, Tohoku42.9711.0214.9118.080.009.00No data

Kanto12.5611.6913.416.621.0324.52

Kinki4.081.476.812.991.2014.35Not applicable

Kyushu, Okinawa17.7015.6819.277.001.129.00

Japan Subtotal91.9349.2979.3045.315.083.1977.74351.85

North18.0518.8210.6117.080.368.30

Pusan0.090.230.060.610.002.61

Seoul, Inchon0.480.590.160.330.008.61

South18.8215.958.7728.881.129.40

Korea, Rep of, Subtotal37.4435.5819.6046.891.482.3628.92172.28

Korea, DPR5.327.694.1739.9223.932.4914.5498.06

Mongolia44.270.1468.230.152.3637.522.00154.67

Taiwan, China19.2843.289.8465.550.450.7213.00152.11

Other East Asia Total198.25135.99181.13197.8233.3046.27136.20928.96

Brunei0.030.031.340.500.070.000.212.17

Cambodia34.5814.324.540.838.2916.167.0685.79

Indonesia139.5151.50221.69548.06176.99117.81134.831390.40

Laos11.376.063.650.212.8330.633.4258.18

Malaysia8.3410.0732.4143.367.1329.7414.63145.68

Myanmar126.3921.5514.8541.4028.6179.3929.03341.23

Philippines29.4357.2644.4742.5220.3531.1048.16273.28

Singapore0.001.050.630.130.000.002.514.31

Thailand70.3242.3047.0493.8324.8769.3439.83387.53

Vietnam47.69111.2062.67299.7573.6240.2850.71685.92

Southeast Asia Total467.66315.34433.281070.59342.76414.46330.393374.48

Bangladesh272.650.0063.62270.8048.4025.3981.80762.67

Bhutan5.010.410.640.031.570.991.339.98

India2522.2690.86269.443164.19546.89170.15635.687399.48

Nepal81.054.9610.0726.7421.349.5414.41168.11

Pakistan253.610.00150.72626.5578.8017.6486.671213.98

Sri Lanka18.640.412.9043.628.315.3712.2791.51

South Asia Total3153.2296.63497.404131.93705.31229.08832.159645.73


Asia Total5024.712957.392538.8212284.121683.02915.932114.9927518.97








David Streets:Estimate adjusted for fertilizer type, per Z. Klimont, 7-7-01

David Streets:Adjusted downward per Zig Klimont, 7/15/01.







David Streets:Extrapolated from China emissions, using FAO animal statistics.



David Streets:Extrapolated from China emissions using FAO statistics on use of nitrogenous fertilizers.

David Streets:Extrapolated from China emissions using UN population statistics.





David Streets:Extrapolated from China emissions using FAO statistics on use of nitrogenous fertilizers.

David Streets:Extrapolated from China emissions using UN population statistics.

VEGETATION BURNED 2000 Version: final Date: 07/01/2002

REGIONSavanna/ Grassland (Tg)Forest (Tg)Crop Residue (Tg)Total (Tg)

Anhui0.000.126.476.59

Beijing0.000.010.340.35

Fujian0.000.591.962.55This inventory was prepared by K.F. Yarber and D.G. Streets, Decision and Information Sciences Division, Argonne National Laboratory, for the TRACE-P project of the National Aeronautics and Space Administration. For further information, contact: dstreets@

Gansu2.420.031.253.70

Guangdong0.000.494.715.20

Guangxi0.000.954.565.51

Guizhou0.000.591.912.50

Hainan0.000.020.520.54

Hebei0.020.045.385.44

Heilongjiang1.179.546.3117.02

Henan0.000.128.848.96

Hong Kong0.000.000.000.00

Hubei0.000.275.936.20

Hunan0.000.256.256.50

Jiangsu0.000.011.771.78NOTE: THIS IS THE FINAL DATA SET TO BE USED IN THE TRACE-P SUMMARY PAPER FOR THE JGR SPECIAL ISSUE.

Jiangxi0.000.268.048.30

Jilin0.310.516.026.84

Liaoning0.430.052.262.74

Nei Mongol15.544.952.6623.15

Ningxia0.580.000.561.14The values for biomass burning in these tables are annual average amounts of vegetation burned typical of the mid-1990s. We recognize the very large inter-annual variability in biomass burning. These values are intended for non-year-specific studies; for

Qinghai7.440.030.237.70

Shaanxi0.010.132.322.46

Shandong0.000.017.447.45

Shanghai0.000.000.510.51

Shanxi0.040.051.521.61

Sichuan0.170.659.089.90

Tianjin0.000.000.460.46

Xinjiang10.760.050.6311.44

Xizang13.230.121.3614.71

Yunnan0.005.182.807.98

Zhejiang0.000.063.283.34

China Total52.1225.08105.34182.54

Chugoku, Shikoku

Chubu


Kanto

KinkiNot applicable

Kyushu, Okinawa

Japan Subtotal0.000.561.852.41

North

Pusan

Seoul, Inchon

South

Korea, Rep of, Subtotal0.000.081.731.81

Korea, DPR0.000.980.861.84

Mongolia23.459.180.0332.66

Taiwan, China0.000.130.420.55

Other East Asia Total23.4510.934.8939.27

Brunei0.000.000.000.00

Cambodia7.635.390.8813.90

Indonesia20.7268.065.8394.61

Laos4.8719.130.5124.50

Malaysia0.0022.070.8122.88

Myanmar1.9355.533.9861.44

Philippines0.1616.657.1423.95

Singapore0.000.000.000.00

Thailand12.0035.997.6655.65

Vietnam12.2414.996.1133.34

Southeast Asia Total59.55237.8132.92330.27

Bangladesh0.008.5011.0319.53

Bhutan0.000.680.030.71

India8.5637.4483.65129.65

Nepal0.005.001.956.95

Pakistan2.860.9210.2714.05

Sri Lanka0.003.930.204.13

South Asia Total11.4256.47107.13175.02

International Shipping0.00

Asia Total146.54330.29250.28727.11

Kristen F Yarber:Savanna grassland burning herein is defined as the sum of all burning of savanna and grasslands due to prescribed burning, natural fires, and grassland conversion. Due to limitations in the available data, exceptions to this definition may occur on a country specific basis. When this is the case, a detailed comment is provided.

Kristen F Yarber:Forest burning herein is defined as the sum of all burning of forest due to prescribed burning, natural fires, and forest conversion. Due to limitations in the available data, exceptions to this definition may occur on a country specific basis. When this is the case, a detailed comment is provided. We have also, in some cases, provided a deforestation estimate in the comments. Calculations involved in this estimate can be found in the final worksheet in this file, "deforestation." It is assumed that deforestation includes on-site burning, off-site burning and timber use, and cut forest left to decay.

Kristen F Yarber:Unless otherwise stated, crop residue burning is calculated from FAO crop production data; production-to-residue ratios from Strehler and Sttzle (1987), Lu (1993), and Reddy and Venkataraman (personal communication); crop-specific dry matter fraction from Reddy and Venkataraman (personal communication); 17% burn ratio for China, other East Asia and Southeast Asia from Hao and Liu (1994); 25% burn ratio for the Indian subcontinent from Reddy and Venkataraman (personal communication); and crop-specific burn efficiency from Reddy and Venkataraman (personal communication).When data describing crop residue burning were provided in individual country communications for the IPCC, they were considered to be more reliable, and used in place of the above methodology. When this method of calculation is used, it is noted in a comment.

yarber:see sheet "China grassland"

Kristen F Yarber:China forest data are from Wang, Feng and Zhuang (1996). In their original paper, data are compiled for a 42-year period from 1950 to 1992. The figure presented in this sheet is the annual average during this period.

Kristen F Yarber:Value derived from back calculating CH4 emissions from on site burning of forest matter after forest and grassland conversion, as reported in Japan's country communication for the IPCC. No additional dry matter was added to reflect other types of forest fires.

Kristen F Yarber:Back calculated using data on non-CO2 emissions from the "burning of agricultural residues" section of the country communication for the IPCC and a crop residue emission factor from Andreae and Merlet (2001).

Kristen F Yarber:A five year average of forest area burned (1987-1991) was gathered from the Freiburg University website. Then a dry matter fraction (213 t/ha) from the IPCC country communication worksheets for temperate broadleaf forests, and a burn efficiency of 0.6 were applied. The 0.6 burn efficiency was decided on based on the range of burn efficiencies in previous studies: 0.45 [Hao & Liu, 1994] and 0.2-1.0 [Lavou et al., 2000].

David Streets:No definitive data are available for forest fires in DPRK. This value is calculated from an estimate of 114,000 acres burned in 1997 (a very dry year) (http://www.uswaternews.com/archives/arcglobal/7norkor8.html). Extreme year is converted to an average year using the ratio of ave/max (0.1655) for neighboring Heilongjiang Province of China from Wang et al paper. Vegetation burned is then calculated using IPCC vegetation density and 0.6 burn fraction.

Kristen F Yarber:From IFFN No.

Kristen F Yarber:Total area of forest burned is from Lavoue et al. (2000). Lavoue et al. (2000) contains data for Mongolian forest and savanna fires over a 37-year period (1960-1997). The data presented in this sheet are the annual averages during this period. To the area burned, a dry matter from IPCC was applied (63.5 for mixed broadleaf/coniferous boreal forests) and a burn efficiency of 0.6. The 0.6 burn efficiency was decided on based on the range of burn efficiencies in previous studies: 0.45 [Hao & Liu, 1994] and 0.2-1.0 [Lavou et al., 2000].

Kristen F Yarber:Taiwan forest area burned estimated using Bull. Taiwan Forestry Institute (http://www.tfri.gov.tw/publish/72-7e.htm).Conversion to Tg biomass burned calculated using IPCC (1996) figures for Philippines forests.

Kristen F Yarber:This value, derived from Hao & Liu (1994), reflects the total burning from all types of forest fires.For comparison:Annual deforestation estimate from the website www.bsrsi.msu.edu/rfrc/stats/seasia7385.htmlindicates a deforestation rate of 110,000 ha/yr. If all of that were burned on site, it would yield 12.21 Tg dm burned. [Calculated using IPCC dry matter density for tropical continental Asia forest (moist with short dry season:185 t dm/ha) with a 0.6 burn efficiency applied.]We assume, however, that only a portion is actually burned on site, and the rest is taken for timber or left to decay.

Kristen F Yarber:For Comparison:ALGAS Indonesia (1998) (in IGES, 2000) listed 2,014,470 ha grassland in Indonesia. Applying IPCC default values for fraction of total savanna burned annually in tropical Asia (0.50), above ground biomass density (4.9 t dm/ha), and burn fraction (0.85), this would yield 4.20 Tg dm burned annually.9.04 Tg dm grassland burned during prescribed burning are derived by back calculating 19.52 Gg CH4 from prescribed grassland burning using IPCC worksheets and IPCC default numbers. The same is found by back calculating CH4 emissions reported by ALGAS (in ADB).It is implicitly assumed that the remaining dry matter is from natural fires, which are not addressed in the country communications.

Kristen F Yarber:Forest biomass burned was calculated using data gathered from Indonesias country communication for the IPCC. A special entry for CO2 emissions from naturally occurring forest fires was utilized to back calculate a mass of dry matter burned using a CO2 emission factor from Andreae & Merlet. Biomass burning from forest and grassland conversion was back-calculated from non CO2 emissions and the IPCC calculation worksheet (IPCC default numbers were used when necessary). Dry matter burned in natural forest fires (17.08) was added to dry matter burned on-site in forest and grassland conversion (50.98) to supply our final forest burning estimate of 68.06 Tg dm, which we used to replace our previous estimate of 76.17 Tg dm based on Hao & Liu. No burn efficiency fraction was applied because it was assumed to be included in the country communication data.Annual deforestation estimate from the website www.ran.org/info_center/factsheets/04b.html indicates a deforestation rate of 1.2 million ha/yr. If all of that were burned on site, it would yield 162.0 Tg dm burned. [Calculated using average of IPCC dry matter density for tropical insular Asia forest (moist with short dry season:175 t dm/ha and wet: 275 t dm/ ha) with a 0.6 burn efficiency applied.]We assume, however, that only a portion is actually burned on site, and the rest is taken for timber or left to decay.

Kristen F Yarber:Back calculated using data on non-CO2 emissions from the "burning of agricultural residues" section of the country communication for the IPCC and a crop res. emission factor from Andreae & Merlet (2001).Non CO2 emissions data from ALGAS (presented in ADB),back calculated using emission factors from Andreae & Merlet (2001), indicate 9.36 Tg dm crop res. is burned on-site post harvest.

Kristen F Yarber:This value is the combination of 10.58 Tg dm from forest and grassland conversion, back calculated using the Laos country communication prepared for the IPCC and the IPCC calculation worksheet (IPCC default numbers were used when necessary), and 8.55 Tg dm from naturally occurring forest fires, calculated by applying a 0.6 burn efficiency to data from the Laos Ministry of Agriculture/Forestry cited in the following report:http://www.adrc.or.jp/nations/nationframe.asp?URL=../countryreport/LAO/index.html&NationCode=418/The 0.6 burn efficiency was decided on based on the range of burn efficiencies in previous studies: 0.45 [Hao & Liu, 1994] and 0.2-1.0 [Lavou et al., 2000].This value replaced our previous estimate of 28.86 based on Hao & Liu (1994).Annual deforestation estimate from the website www.bsrsi.msu.edu/rfrc/stats/seasia7385.htmlindicates a deforestation rate of 150000 ha/yr. If all of that were burned on site, it would yield 16.65 Tg dm burned. A second annual deforestation estimate from the website www.ran.org/info_center/factsheets/04b.htmlindicates a deforestation rate of 100000 ha/yr. If all of that were burned on site, it would yield 11.10 Tg dm burned. [Calculated using IPCC dry matter density for tropical continental Asia forest (moist with short dry season:185 t dm/ha) with a 0.6 burn efficiency applied.]We assume, however, that only a portion is actually burned on site, and the rest is taken for timber of left to decay.

Kristen F Yarber:This value, derived from Hao & Liu (1994), reflects the total burning from all types of forest fires. Data in the country communication (back calculated using the IPCC worksheet and default IPCC numbers) indicate 19.13 Tg dm burned in forest and grassland conversion. It is implicitly assumed that the remaining dry matter is from natural fires, which are not addressed in the country communications.

Kristen F Yarber:Back calculated using data on non-CO2 emissions from the "burning of agricultural residues" section of the country communication for the IPCC and a crop res. emission factor from Andreae & Merlet (2001).

Kristen F Yarber:This value, derived from Hao & Liu (1994), reflects the total burning from all types of forest fires.For comparison:Data on the site www.gvm.sai.jrc.it/forest/asia/hotspots.htm indicate 1.9 million ha of forest were lost due to slash & burn agriculture from 1990-1995. When a ratio of 185 tones dm/ha (IPCC guidelines) and a 0.6 burn efficiency are applied, this yields 42.8 Tg dm/yr.Annual deforestation estimate from the website www.bsrsi.msu.edu/rfrc/stats/seasia7385.htmlindicates a deforestation rate of 320000 ha/yr. If all of that were burned on site, it would yield 35.52 Tg dm burned. [Calculated using IPCC dry matter density for tropical continental Asia forest (moist with short dry season:185 t dm/ha) with a 0.6 burn efficiency applied.]We assume, however, that only a portion is actually burned on site, and the rest is taken for timber or left to decay.

Kristen F Yarber:For Comparison:Hao & Liu (1994) indicates 0 Tg dm grassland burned. However, a value of 0.16 Tg dm burned in prescribed burning is obtained from the country communication for the IPCC (back calculated using IPCC worksheets and default numbers). It is implicitly assumed that the remaining dry matter is from natural fires, which are not addressed in the country communications.0.33 Tg dm grassland is

Chemical Weather Forecasting Perspectives & Challenges Gregory R. Carmichael Department of Chemical...

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