Reconstruction of Emissions and Suggestions to Improve Understanding

An Integrated Analysis of Asian Outflow Events Using Comprehensive Emissions in Support of ACE-Asia and TRACE-P

J.-H. Woo1, G.R. Carmichael1, D.G. Streets2, Y. Tang1, C. Jang3, J. S. Fu4, L. Pan1, N. Thongboonchoo1

1. Center for Global and Regional Environ. Research, Univ. Iowa, USA; 2. Argonne National Laboratory, USA;

3. U.S. Environmental Protection Agency, Research Triangle Park, USA; 4. Department of Civil and Environmental Engineering, University of Tennessee, USA

Reconstruction of Emissions and Suggestions to Improve Understanding

Emissions Development

Schematic methodology for the development of Asian emission estimates

Definition of the inventory domain, its constituent countries, international shipping lanes, and sub-regions

An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P and the ACE-Asia experiment. We estimate total Asian emissions as follows: 34.3 Tg SO2, 26.8 Tg NOx, 9870 Tg CO2, 279 Tg CO, 107 Tg CH4,

52.2 Tg NMVOC, 2.54 Tg black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH3. In addition,

NMVOC are speciated into 19 sub-categories according to functional groups and reactivity. Thus, we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements

Emissions Processing

To support field experiments and complex atmospheric models, highly resolved levels of spatial, temporal, and species-component detail are needed in the emission inventories. It is also important that these emission fields correctly reflect the spatial and temporal emission profiles of sources that were operating during the time period of the field campaigns, otherwise good agreement between models and experiment cannot be expected.

High-Resol (2 min)Gridded Emissions

(~ 300 maps)

Low-Resol (1 deg) Gridded Emissions

(39 Maps)

3

Months

2

Types

1(3)

Sectors

3304~830

SpeciesSpeciesSectorsSpecies

Biogenic

by grid(1deg)

Biomass Burningby Region(52)

Anthropogenic by Region(77)

Spatial Interpolation

Spatial/TemporalAllocation

Land/Sea Masking

SpatialAllocation

Spatial Aggregation

Re-arrange by sector/layer

Map projection

Unit Change

DB Export

Multi-Resol (6 & 18min)Gridded Emission

(~ 600 maps)

Multi-Resol(12 & 36km)Gridded Emission

(~ 600 maps)

Multi-Resol(12 & 36km)Gridded Emission

(22 DB files)

ClipDomain

Feed into Chemical Transport Models

Start with Tabular database

Emissions Distribution

Knowledge of source strengths and locations is also a valuable aid for interpreting observations and model results and ultimately choosing appropriate mitigation strategies.

Fossil

Biofuel

0

2000

4000

6000

8000

10000

12000

Gm

ol/

Ma

r*

0

5000

10000

15000

20000

25000

30000

Gm

ol/

Ma

r*

Fossil Biofuel Biom Total

*Note : SO2, NOx are scaled by 0.1 and CO, CH4, BC, and OC are scaled by 0.01. CO unit is Tmol/MarSource strength by emissions categoriesMarch 1~3, 2001

March 3~6, 2001

March 7~9, 2001

March 10~12, 2001

BiomassBurning

March 13~15, 2001

March 16~18, 2001

March 19~21, 2001

NMVOC Composition by Energy Sectors

In summary, for all of Asia we estimate that 27% of NMVOC emissions are alkanes, 22% alkenes, 5% ethyne, 17% aromatics, 9% aldehydes and ketones, and 20% halocarbons and other organic compounds. This kind of distribution is typical of all of Asia except the developed or industrializing parts of East Asia (e.g. Japan; S. Korea; and Taiwan). In this region, the share of alkanes is higher (~ 35%) and the share of alkenes is much lower (~ 9%). This is indicative of greater use of petroleum products, particularly for transportation, and far less vegetation burning.

Particle Composition by Different Source Categories

Ethane

10%

19%

45%

10%

16%0%

Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

Ethene

26%

10%

50%

0%

14% 0%

Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

Ethyne

26%

12%

45%

9%

8% 0%

Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

Butanes

58%31%

4%

1%

2%

4%Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

Toluene

25%

45%

11%

3%

2%14%

Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

Formaldehyde

19%

15%

6%

0%

56%

4%Transport

Industry

Biofuels

Residential coal

Biomass Burning

Other

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

Species

Fra

ctio

n o

f to

tal e

mis

sio

ns

China India

Laos Japan

Biofuel use dominates the emission profiles of ethane (45%), ethene (50%), and ethyne (45%). Emissions of butanes are primarily from transportation(58%). Formaldehyde emissions (56%) are mainly from biomass burning, and toluene emissions (45%) are mainly from industry.

Integrate Analysis using Aerosol Compositions

Satellite Observation vs. Process-based Emissions

Not done yet!

A sample analysis that combines satellite image, measured concentration (BC from C130), measured wind vector, emissions distribution, forward CTM (STEM 2K1) and backward receptor model (EPA, CMB 8.0) showed consistent features for ACE-Asia C130 Flight 7. The elemental compositions of five source categories were used as source signatures. These information is new and useful to interpret source contribution from the CMB model for each selected outflow event.

TOMS Aerosol Index

Dust

Dust & BB Emissions

April 7, 2001 April 9, 2001

April 14, 2001

From STEM 2K1 Model (ACE-Asia, Apr 12, 2001) Red : Fossil Fuel Combustion Pink : Biofuel Combustion and Biomass Burning Yellow : Wind-Blown Dust (Crustal)From Chemical Mass Balance Model (Bottom Fig.)

Background : Satellite Image (VIS channel)Red Dots : BC Emissions (Anthropogenic)Colored Arrows : Measured Wind Vector with Black Carbon Concentration (C130-7R, 2001/4/12)

Apr 12, 03:00

Apr 12, 08:00

Process-based emissions estimates reveal the source of Asian dust and Biomass burning outflow observed from NASA TOMS satellite.

Biomass Burning

Ship, Flight, and Ground Measurements

Model vs. Model

Modeling domain with countries/regions sets for source-receptor calculations,

measurement site locations ()

receptor locations (×): 1) Kangwha; 2) Komae; 3) Taichung; 4) Nanjing; 5) Jinan; 6) Beijing; 7) Nangoku; 8) Otobe; 9) Amami;

10) Kashima; 11) Yangyang; 12) Tokoro; 13) Hachijo; 14) Oki; 15) Tsushima; 16) Fukue; 17)

Miyako; 18) Taitong.

Source : MICS-Asia (Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia) Phase 1

SO2 concentrations - 11-20 Janua ry

0

5

10

15

20

ppb

SO4 concentrations - 11-20 Janua ry

0

5

10

15

20

ug/m

3

SO4 wet de positions - 11-20 Ja nuary

1

10

100

1000

ug/m

2

Kangwha (Korea) Tsushima (Japan)

Dots: MeasurementsWhiskers: Model results

The CTMs that are participated in MICS-Asia (Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia) show consistency as well as inconsistency among models and between observation vs. prediction. The CTM gives good scientific understandings with some degree of uncertainties, though.

Intercomparison with monitoring data and models for the 11-20 January period

2-D and 3-D analysis features for DC8 flight 9 (March 9th). Left: 3-D back trajectories (5-day) colored with measured trace gas mixing ratio (light purple line: 3.4GMT, purple line: 3.3GMT, red line: 2.5GMT); fossil fuel (light blue dots), biofuel (yellow dots), biomass burning (red dots) emissions; background map - topography colored by landcover. Right: Source sector contribution by CMB model and 5day 2-D back trajectories.

2-D and 3-D analysis features for DC8 flight9 (March 10th), Left: same as uppermost figure, but (light blue: 3.3GMT, red: 3.5GMT, light red: 5.2GMT, orange: 7.6GMT, yellow: 7.9GMT), Right: same as uppermost figure.

Selected DC8 flight and measurement points used in the analysis

Geographical distribution of temporally averaged (monthly) biomass burning CO emissions

Evaluation measures of the CMB model estimates

Five DC8 flights (e.g. flight 6, 8, 9, 10, and 12) with16 flight segments were selected as outflow events. Four chemical species (ethane, propane, butanes, and acetylene) out of 27 were selected as Chemical Mass Balance (CMB) model input We analyzed spatial and temporally resolved emission data, backward trajectory analysis, 3D chemical source model, and wind field information to interpret source contribution from the CMB model for each selected outflow event. Flights in the post frontal regions at high latitudes and low altitudes were found to have a high contribution of fossil fuel emissions. Flights in the warm sector of cold fronts were dominated by biomass burning contributions (about 70%). Biofuel contributions were high (about 70%) when air masses come from central China.

Integrate Analysis using Gaseous Chemical Compositions

Spatial / Temporal Surrogates

Emis. Facter

Energy/

Fuel

Geo-genic

Bio-genic

Pyro-genic

Anthropo-genic

Emissions

Spatial / Temporal Surrogates

Emis. Facter

Energy/

Fuel

Geo-genic

Bio-genic

Pyro-genic

Anthropo-genic

Emissions

UncertaintyUncertainty

Lagrangian

RemovalChemistryTransport

Eulerian

Source Models

Lagrangian

RemovalChemistryTransport

Eulerian

Source Models

Initial/ Boundary Conditions

Meteorology Initial/ Boundary Conditions

Meteorology

Ground/ Ship

AircraftSatellite

Observation

Ground/ Ship

AircraftSatellite

ObservationInverse Models

Statistical Receptor Models

Reverse Models

Back-trajectory

Source Apportionment

Re-construct Emission Field

Backward Models

Inverse Models

Statistical Receptor Models

Reverse Models

Back-trajectory

Source Apportionment

Re-construct Emission Field

Backward Models

Estimated Concentration Field

Estimated Concentration Field

Error/BiasAnalysis

Estimated Emissions Field

Estimated Emissions Field

Error/BiasAnalysis

RatioAnalysis

RatioAnalysis

> 3.7

> 3.7

1 degree

1 degree Age of Airmass

In such a case, Only count once.

Estimated Emissions Observation Based Reconstruction

Reconstruction of emissions field using measurement & trajectory

Reconstruction of emissions field can one way to re-create useful information to improve our understanding.

> 3.7 > 3.7

1 degree

1 degree Age of Airmass

In such a case, Only count once.

Can Useful Emissions Information Be Reconstructed Using Observed Ratios (or Concentrations)?

Estimated Emissions Observation Based Reconstruction

Reconstruction of emissions field using measurement & trajectory

Emission & Modeling Analysis (DC8-08, Mar. 9th , Biomass Emission)

DC8 flight track

Back trajectory

Observer’s track

Flyby1

Red : Fire Count(Mar. 6th)Blue : CO Emis(Anthro.)

Flyby2

Spatial / Temporal Surrogates

Emis. Facter

Energy/

Fuel

Geo-genic

Bio-genic

Pyro-genic

Anthropo-genic

Emissions

Spatial / Temporal Surrogates

Emis. Facter

Energy/

Fuel

Geo-genic

Bio-genic

Pyro-genic

Anthropo-genic

Emissions

UncertaintyUncertainty

Lagrangian

RemovalChemistryTransport

Eulerian

Source Models

Lagrangian

RemovalChemistryTransport

Eulerian

Source Models

Initial/ Boundary Conditions

Meteorology Initial/ Boundary Conditions

Meteorology

Ground/ Ship

AircraftSatellite

Observation

Ground/ Ship

AircraftSatellite

ObservationInverse Models

Statistical Receptor Models

Reverse Models

Back-trajectory

Source Apportionment

Re-construct Emission Field

Backward Models

Inverse Models

Statistical Receptor Models

Reverse Models

Back-trajectory

Source Apportionment

Re-construct Emission Field

Backward Models

Estimated Concentration Field

Estimated Concentration Field

Error/BiasAnalysis

Estimated Emissions Field

Estimated Emissions Field

Error/BiasAnalysis

RatioAnalysis

RatioAnalysis

PM2.5 in Beijing(Model)

PM2.5 in Beijing(Monitoring)

M3/CMAQ (12-km)Jan. & July avg., 2001

PM 2.5: 86.45 (ug/m3 )

Organic30%

Trace2%

Crustal20%Ammonium

6%

Sulfate17%

Chloride2%

EC3%

Nitrate10%

Unknow10%

Annual Average

for all sites (2000):

PM 2.5: 95.5 (ug/m3 )(Courtesy of Prof. Zhang, Beijing Univ., 2002)

PM2.5 composition in Beijing (2001 avg., 12-km)

(Jan. & July average PM2.5=86.45 (ug/m^3)

OC16%EC

16%

NO311%

SO418%

NH410% Other

29%

RF16 highly processed plumeRF7 highly processed plume

Field Experiments Employ Mobile “Super-

Sites” & Provide Opportunities to

Characterize Emissions

Modeling domain with countries/regions sets for source-receptor calculations,

measurement site locations ()

receptor locations (×): 1) Kangwha; 2) Komae; 3) Taichung; 4) Nanjing; 5) Jinan; 6) Beijing; 7) Nangoku; 8) Otobe; 9) Amami;

10) Kashima; 11) Yangyang; 12) Tokoro; 13) Hachijo; 14) Oki; 15) Tsushima; 16) Fukue; 17)

Miyako; 18) Taitong.

Source : MICS-Asia (Model InterComparison of Long-Range Transport and Sulfur Deposition in East Asia) Phase 1

MICS-Asia (Comparison with monitoring data for the 11-20 January period)

SO2 concentrations - 11-20 January

0

5

10

15

20

Kangwha

(K)

Komae (J)

Taichun

g (T)

Nanjing (C

)

Jinan (C

)

Beijing

(C)

Nangoku

(J)

Oto

be (J

)

Amam

i (J)

Kashim

a (J)

Yangyan

g (K)

Tokoro

(J)

Hachijo

(J)

Oki (

J)

Tsush

ima (J

)

Fukue (J

)

Miya

ko (J

)

Taiton

g (T)

ppb

SO4 concentrations - 11-20 January

0

5

10

15

20

Kangwha

(K)

Komae (J)

Taichun

g (T)

Nanjing (C

)

Jinan (C

)

Beijing

(C)

Nangoku

(J)

Oto

be (J

)

Amam

i (J)

Kashim

a (J)

Yangyan

g (K)

Tokoro

(J)

Hachijo

(J)

Oki (

J)

Tsush

ima (J

)

Fukue (J

)

Miya

ko (J

)

Taiton

g (T)

ug/m

3

SO4 wet de positions - 11-20 January

1

10

100

1000

Kangwha

(K)

Komae (J)

Taichun

g (T)

Nanjing (C

)

Jinan (C

)

Beijing

(C)

Nangoku

(J)

Oto

be (J

)

Amam

i (J)

Kashim

a (J)

Yangyan

g (K)

Tokoro

(J)

Hachijo

(J)

Oki (

J)

Tsush

ima (J

)

Fukue (J

)

Miya

ko (J

)

Taiton

g (T)

ug/m

2

Kangwha (Korea) Tsushima (Japan)

Dots: MeasurementsWhiskers: Model results

1 deg.

Lake Tai’s effect can be seen at 30' ×

30' resolution, but Yangtze

River’s effect cannot be

clearly seen even in 5' ×

5' resolution.

5 min.

30 sec.

0

500

1000

1500

2000

Note : India is scaled by 0.1

Gg

Industry (Gg) Pow er Generation (Gg) Transport (Gg) Dom_fos (Gg) Dom_bio (Gg) Biomass Burning (Gg)

0

2000

4000

6000

8000

10000

Note : Thailand, Indonesia & India are scaled by 0.1

Gg

Industry (Gg) Power Generation (Gg) Transport (Gg) Dom_fos (Gg) Dom_ bio (Gg) Biomass Burning (Gg)

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Fra

cti

on

of

an

nu

al e

mis

sio

ns

Central China

South China

North China

b)

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Fra

ctio

n o

f an

nu

al e

mis

sio

ns

NH3

CH4

a)0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Fra

ctio

n o

f an

nu

al e

mis

sio

ns Central China

South China

North China

b)

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul

Fra

cti

on

of

an

nu

al e

mis

sio

ns BC

CO

NMVOC

SO2

NOx

a)

An Integrated Analysis of Asian Outflow Events Using Comprehensive Emissions in

Support of ACE-Asia and TRACE-P

J.-H. Woo, G.R. Carmichael, D.G. Streets, Y. Tang, L Pan, N. Thongboonchoo

Center for Global and Regional Environ. Research, Univ. Iowa, USA Argonne National Laboratory, USA

0

500

1000

1500

2000

Gg

_C

O/d

ay

0

2000

4000

6000

8000

10000

12000

1 2 3 4 5 6 7 8 9 10 11 12

Month

CO

(Tg

)

Mongolia, N. China C. China, Korea, JapanS. China, Taiwan SE. AsiaS. Asia

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

Biomass Burned Original Normalized Moving Averaged AI adjusted

Population

Landcover

Region boundary

LPSs

Volcano Emission

Road Network

Shiplanes

Regional Emission

Urban Pop. by Regions

LPSs by Regions

Road by Regions

Shiplane by Types

Rural Pop. by Regions

Area Emissionby Fine Grid

Mobile Emissionby Fine GridShip Emission

by Fine Grid

LPSs Emission

Emission Sectors

Sectoral Aggregation

Spatial Aggregation

30 min.

Original Fire Count(FC_Org) (AVHRR, WFW)

Moving Averaged FC(FC_MA)

AI adjusted FC(FC_AIadj)Regression FC&AI

Normalized FC(FC_Norm)

3-day Fire Count(For only 0 FC)

Satellite Cov./Cloudiness(AVHRR),

Latitude adjust factor

Normalize

Moving Average

AI(TOMS) Satellite Cov./Cloudiness

(AVHRR), Precipitation(NCEP),

Landcover(ORNL)

Regional Biomass Burned (Dr. David Streets)

AI adjust

Consistency test- Biomass burned .vs. FC- Annual, typical, regional

- Change criteria for each FC adjust step

- Implement new information for adjust

Daily, Gridded

Annual, Regional

Finalize FC

Summarize FC Temporal - Monthly, Seasonal, Annual Spatial - Regional

Allocation Factor Temporal/Spatial/Type(LC)

Daily FC, Landcover class,Biomass density

Region Area Fraction (For anthro. gridding 30 min, Shifted)

LPSs & Volcano

Population

Road networkShip lanes

Landcover

0%

100%

200%

300%

400%

500%

600%

China Japan Other EastAsia

SoutheastAsia

India Other SouthAsia

Ships All Asia

95

% C

on

fid

en

ce

Inte

rva

l, ±

SO2

NOx

CO2

CO

CH4

VOC

BC

OC

NH3

NH3

xylenes

CH4

butanepentane

SO2

propane

O. alkanes

I. alkenes

O. aromatics

toluene

ketones

HCHO

Other NMVOCs

Other aldehydes

ethaneOCT. alkenes

etheneCO

BCpropene

CO2 benzene

acetylene

NOxHalocarbones

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

-0.2 0.0 0.2 0.4 0.6 0.8 1.0

Factor 1

Fac

tor

2

Development Methodology

Schematic methodology for the development of Asian emission estimates

Definition of the inventory domain, its constituent countries, international shipping lanes, and sub-regions

An inventory of air pollutant emissions in Asia in the year 2000 is developed to support atmospheric modeling and analysis of observations taken during the TRACE-P and the ACE-Asia experiment. We estimate total Asian emissions as follows: 34.3 Tg SO2, 26.8 Tg NOx, 9870 Tg CO2, 279 Tg

CO, 107 Tg CH4, 52.2 Tg NMVOC, 2.54 Tg

black carbon (BC), 10.4 Tg organic carbon (OC), and 27.5 Tg NH3. In addition, NMVOC

are speciated into 19 sub-categories according to functional groups and reactivity. Thus, we are able to identify the major source regions and types for many of the significant gaseous and particle emissions that influence pollutant concentrations in the vicinity of the TRACE-P and ACE-Asia field measurements

Regional emissions

The anthropogenic emissions are initially calculated for 64 regions of Asia. Regional emissions are then gridded at a variety of spatial resolutions for input to the atmospheric simulation models, ranging from 1º × 1º for most regional model applications down to 30 sec × 30 sec resolution for urban-scale studies. Each type of emission source is represented by a different spatially resolved surrogate parameter, using such data sets as urban and rural population, road networks, land cover, and ship lanes. Large Asian power plants are separately identified in the RAINS-Asia model and located according to their latitude/longitude coordinates.

Emissions are initially calculated for an annual time period. However, it is recognized that there is considerable seasonal variation for some species, associated with such activities as the burning of fossil fuels for home heating in winter and the temperature dependence of releases of NH 3 from fertilizer

application and CH4 from manure. Biomass burning clearly has a very high degree of seasonality,

determined by local agricultural practices and meteorological conditions. Such seasonality is important in preparing emissions for comparison with time-specific field experiments. For this reason we have apportioned annual emissions to daily and monthly emissions using a variety of techniques.

The techniques used to develop biomass burning estimates are elaborate and based on national surveys of fires, AVHRR satellite fire counts, and TOMS AI data.Efforts concentrated on realistically apportioning annual emissions to the months of March and April, which correspond to the timing of the TRACE-P and ACE-Asia field campaigns.

To account for missing data due to cloud cover, and satellite coverage, we applied a normalized factor to the fire count data to adjust for missing data. However this process does not account for no-data grid cells or data error conditions. The zero fire detection due to the lack of satellite information case, we used 3-day moving averages (only applied for zero-fire counts cells). If there was trouble in the satellite on-board system or at the receiving station, or if clouds persisted more than several days, the moving average scheme can not improve the AVHRR fire count data. In this case we used TOMS-AI data as an additional information source. However, the TOMS-AI data should be used with caution because it detects all (absorbing) aerosols, including dust and man-made smoke. So we applied several masks to help filter the information that is not caused by biomass burning. These masks include: 1) the classification of cloud conditions with and without rain using NCEP daily precipitation fields; 2) landcover maps to omit dust interference; and 3) maps of anthropogenic smoke sources including coal mine fires, oil wells, and gas drilling sites.

Fire detection from Xinjiang and Pakistan could be flares from Oil/Gas drilling site

Landcover Fraction(Crop/Grass/Forest)

Landcover Fraction estimation to allocate type of burning

Emission Inventory Features Emission-Based Analysis

We used the annual-regional emission data from Yarber et al. [manuscript in preparation, 2002] to estimate daily emissions for the period from Feb 26 to May 10, 2001. These estimates of total biomass burning emissions by regions were further analyzed to provide daily emissions. The AVHRR fire count, cloud, and satellite coverage data was used to analyze daily fire events. The basic allocation method was to distribute the emissions within a region in time and space according to the daily fire count statistics normalized by the total number of detected fires.

Comparison of country surveys with various AVHRR fire-count adjustments reveals problem areas for further investigation

Xinjiang

Mongolia

Indonesia

Vietnam

fire count > country surveys

fire count < country surveys

Regional emissions with sectoral contribution (Upper : SO2, Lower : CO)

Uncertainty (%) in pollutant emission estimates (±95% confidence intervals)

We estimate the error for each emitting sub-sector by combining the coefficients of variation (CV, or the standard deviation divided by the mean) of the contributing factors. We then combine these uncertainties to estimate the uncertainty of emission estimates for each country. We tried wherever possible to use harmonized data across different source types and regions, even though the reporting practices of countries are not always consistent and available.

The monthly biomass burning CO emissions by region

The daily biomass burning CO emissions (March, Year 2001)

A number of features stand out: the source strengths of NH3 in the agricultural areas of

central China and northern India, the relatively strong signature of NOx from transportation

sources in Japan, significant emissions of NMVOC from biomass burning in Southeast Asia, and large SO2 emissions in the Sichuan Basin and industrialized eastern China.

The CO from fossil fuel and biofuel combustion shows the contrast between source categories across developed and rural areas of China, where coal and biofuel combustion are prevalent. The distributions of four NMVOC species shows the dominant source associations of these species: alkanes and ethene with biofuel combustion, toluene with industry, and formaldehyde with biomass burning. These maps are specifically for the month of January, and therefore they emphasize emissions from domestic heating in China and biomass burning in Southeast Asia

Area source emission distributions at 30 min grid resolution: SO2 (leftmost), NOx (middle left), NMVOC (middle right), and NH3 (rightmost). The unit is Mg yr-1 per grid cell.

The same as above, but : CO from fossil fuel (left) and biofuel (right)

Area source emission at 30 min grid resolution: total alkanes (leftmost), ethene (middle left), toluene (middle right), and formaldehyde (rightmost). Unit : Moles/sec/grid

The initial allocation procedures are conducted at the highest possible resolution (30 sec × 30 sec—about 1 km × 1 km) then aggregated to lower resolution to meet the needs of modeling and analysis. In theory, any resolution is possible that is lower than or equal to 30 sec × 30 sec. Left figure shows CO emission distributions at the 1 deg. (upper right), 30 min. (upper left), 5 min. (lower left), and 30 sec. (lower right) grid cell near Shanghai area. The effect of Lake Tai could be shown in 30 min. grid resolution, whereas Yangtze river is showed only with the highest resolution. In this figure, the effects of regional and sectoral differences can be clearly seen for the four different regions. Right figure shows SO2 (Upper) and CO (Lower)

emission distributions at the highest resolution in the Pearl River Delta, as an example. In this figure, the effects of regional and sectoral differences can be clearly seen for the two different chemical species. This makes focused studies of large metropolitan air-sheds feasible with this inventory.

CO emissions by region and source-category overlayed with regional CO/CO2

emission ratios (left), BC/OC emission ratio by regions overlayed with regional BC/OC ratios by source category (right).

Source profiles from regional groups (left : major species, right : NMVOC species)

Chemical groups identified from hierarchical cluster analysis

Chemical distributions identified in rotated factor space

Regional groups identified from hierarchical cluster analysis

We analyzed regional and chemical characteristics using the region/chemical species information in our inventory. From cluster analysis SO2 shows up as a separate group. This reflects the fact that it is dominated by fossil fuel usage. NO x, CO2 and halocarbons are grouped as

one, and they relate to the stage of development. CO, BC, OC, ethane, propane, ethene, propene, terminal alkenes, internal alkenes, and acetylene have similar regional distributions and are identified as a group. CH4 and NH3 are identified as a group and represent species not highly related to combustion. The

hydrocarbon species are further clustered into four groups that reflect source category contribution. As an alternative analysis, we performed factor analysis using the rotated axis scheme. The factor loadings results are clustered into groups that are similar to those identified through the cluster analysis, and provide same confidence in the groups identified. The results of regional cluster analysis reduced the 52 regions into 11 regional groups as shown in rightmost dendrogram. Lower left f igures show the emission profiles of the regional groupings.

2-D and 3-D analysis features for DC8 flight 6 (March 4th). Left: 3-D back trajectories (5-day) colored with measured trace gas mixing ratio (light purple line: 4.6GMT, purple line: 5.2GMT, red line: 6.9GMT, green line: 7.0GMT); fossil fuel (light blue dots), biofuel (yellow dots), biomass burning (red dots) emissions; background map - topography colored by landcover. Right: Source sector contribution by CMB model and 5day 2-D back trajectories.

2-D and 3-D analysis features for DC8 flight8 (March 9th), Left: same as uppermost figure, but (light blue: 3.4GMT, purple: 3.3GMT, red: 2.5GMT); Right: same as uppermost figure.

2-D and 3-D analysis features for DC8 flight9 (March 10th), Left: same as uppermost figure, but (light blue: 3.3GMT, red: 3.5GMT, light red: 5.2GMT, orange: 7.6GMT, yellow: 7.9GMT), Right: same as uppermost figure.

2-D and 3-D analysis features for DC8 flight10 (March 13th) and DC8 flight 12 (March 18th), Left: same as uppermost figure, but (yellow: flight10-5.5GMT, orange: flight12-3.5GMT, red: flight12-5.2GMT, purple: flight12-5.7GMT), Right: same as uppermost figure.

Fl. No. DATE Time

(GMT) R2

Ratio (Cal./Obs.)

DC8-06 3/4/2001 4.6 0.85 1.08 DC8-06 3/4/2001 5.2 0.84 1.01 DC8-06 3/4/2001 6.9 0.99 1.05 DC8-06 3/4/2001 7.0 0.98 1.03 DC8-08 3/9/2001 2.5 0.77 0.89 DC8-08 3/9/2001 3.3 0.99 0.95 DC8-08 3/9/2001 3.4 0.99 0.96 DC8-09 3/10/2001 3.3 0.88 1.16 DC8-09 3/10/2001 3.5 0.68 0.92 DC8-09 3/10/2001 5.2 0.68 0.92 DC8-09 3/10/2001 7.6 0.89 1.11 DC8-09 3/10/2001 7.9 0.89 1.11 DC8-10 3/13/2001 5.5 0.87 1.14 DC8-12 3/18/2001 3.5 0.92 0.94 DC8-12 3/18/2001 5.2 0.98 1.02 DC8-12 3/18/2001 5.7 0.88 1.11

Selected DC8 flight and measurement points used in the analysis

Geographical distribution of temporally averaged (monthly) biomass burning CO emissions

Evaluation measures of the CMB model estimates

In general, Asian outflow is usually a complex mixture of biofuel, biomass and fossil sources. Flights in the post frontal regions at high latitudes and low altitudes were found to have a high contribution of fossil fuel emissions. Flights in the warm sector of cold fronts were dominated by biomass burning contributions (about 70%). Biofuel contributions were high (about 70%) when air masses come from central China. The receptor model results were shown to be consistent with 3D chemical model sensitivity studies for two common flight cases. Our receptor based approached showed consistency with biomass burning emission sensitivity tests using 3D chemical “source” models [Tang et al., 2002; Zhang et al., 2002]. In addition the results are consistent with source indicators. Ma et al., [2002] identified the contributions of bio-emission (e.g. biomass and biofuel) using the P-3B flight aerosol measurement data. They used dK+/dSO4

2- slopes from biomass(biofuel + biomass burning) and fossil plumes to analyze contribution of source categories. Both P-3B flight 10 and DC8 flight 8 flew a similar path along the 20°N latitude on the same day (March 9th). Their results using dK+/dSO 4

2- slopes showed that the bio-emission contributed 80~100% of total mass. This result is consistent with ours, i.e. 87~96%.The ratios of traditional source tracers including acetonitrile (CH3CN : biomass combustion sources), tetrachloroethene (C2Cl4: fossil fuel sources) and SOy (fossil fuel sources), and CO (general sources) were also analyzed. CH3CN/SOy ratios for the selected data points for DC8 flight 6 and flight 12 (high fossil fuel source contribution) showed lower values, whereas the selected data points for flight 8 (high biomass combustion source contribution) showed higher numbers. The SO y/CO and C2Cl4/CO ratios were anti-correlated with CH3CN/SOy ratios for the same data points. The CMB receptor model, 3D chemical model and source tracer ratios showed consistent results for the selected flight cases.

Five DC8 flights (e.g. flight 6, 8, 9, 10, and 12) with16 flight segments were selected as outflow events. Four chemical species (ethane, propane, butanes, and acetylene) out of 27 were selected as Chemical Mass Balance (CMB) model input We analyzed spatial and temporally resolved emission data, backward trajectory analysis, 3D chemical source model, and wind field information to interpret source contribution from the CMB model for each selected outflow event.

References Ma, Y., R. J. Weber, Y.-N. Lee, D. C. Thornton, A. R. Bandy, A. D. Clarke, D. R. Blake, G. W. Sachse, H. E. Fuelberg, J.-H. Woo, D. Streets, G. R. Carmichael, F. L. Eisele, The Characteristics and Influence of Biomass Burning Aerosols on Fine Particle Ionic Composition Measured in Asian Outflow During TRACE-P, J. Geophys. Res., submitted in 2002. Jung-Hun Woo, David Streets, Gregory R. Carmichael, Youhua Tang, Bongin Yoo, Won-Chan Lee, Narisara Thongboonchoo, Simon Pinnock, Gakuji Kurata, Itsushi Uno, Qingyan Fu, Stephanie Vay, Glen W. Sachse, Donald R. Blake, Alan Fried, D. C. Thornton, The Contribution of Biomass and Biofuel Emissions to Trace Gas Distributions in Asia during the TRACE-P Experiment, J. Geophys. Res., submitted in 2002. Streets, D. G., T. C. Bond, G. R. Carmichael, S. D. Fernandes, Q. Fu, D. He, Z. Klimont, S. M. Nelson, N. Y. Tsai, M.. Q. Wang, J.-H. Woo, and K. F. Yarber, A year-2000 inventory of gaseous and primary aerosol emissions in Asia to support TRACE-P modeling and analysis, J. Geophys. Res., submitted in 2002. Tang, Y., G. R. Carmichael, J.-H. Woo, N. Thongboonchoo, G. Kurata, I. Uno, and D. G. Streets, The Influences of Biomass Burning during TRACE-P Experiment Identified by the Regional Chemical Transport Model, J. Geophys. Res., submitted in 2002. Yarber, K.F., Streets, D.G., Woo, J.-H., and Carmichael, G.R., Biomass burning in Asia: annual and seasonal estimates and atmospheric emissions, in preparation, 2002. Zhang, M., I. Uno, Z. Wang, H. Akimoto, G.R. Carmichael, Y. Tang, J.-H. Woo, D.G. structure of trace gas and aerosol distributions over the Western Pacific Ocean during TRACE-P, J. Geophys. Res., submitted in 2002.

Acknowledgement : This work was supported by the NASA ACMAP and GTE Programs and the NSF Atmospheric Chemistry Program.

Fire Counts

Precipitation

Aerosol Index