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Satellite-derived PM2.5 Emissions from Wildfires for Air Quality Forecast Xiaoyang Zhang 1,2 , Shobha Kondragunta 1 , Felix Kogan 1 , Jerald D. Tarpley 1 , Wei Guo 3 , Christopher Schmidt 4 May 18, 2006, New Orleans 1 NOAA/NESDIS Center for Satellite Applications and Research; 2 Earth Resources Technology, Inc; 3 I.M. System Group, Inc; 4 Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin

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Page 1: Satellite-derived PM2.5 Emissions from Wildfires for …Satellite-derived PM2.5 Emissions from Wildfires for Air Quality Forecast Xiaoyang Zhang1,2, Shobha Kondragunta 1, Felix Kogan

Satellite-derived PM2.5 Emissions from Wildfires for Air Quality Forecast

Xiaoyang Zhang1,2, Shobha Kondragunta1, Felix Kogan1, Jerald D. Tarpley1, Wei Guo3, Christopher Schmidt4

May 18, 2006, New Orleans

1NOAA/NESDIS Center for Satellite Applications and Research; 2Earth Resources Technology, Inc; 3I.M. System Group, Inc; 4Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin

Page 2: Satellite-derived PM2.5 Emissions from Wildfires for …Satellite-derived PM2.5 Emissions from Wildfires for Air Quality Forecast Xiaoyang Zhang1,2, Shobha Kondragunta 1, Felix Kogan

Outline1. Fuel loading dataset derived from MODIS data2. AVHRR NDVI-controlled combustion and

emission factors3. GOES fire sizes4. PM2.5 emissions across the continental USA5. Summary

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Modeling Emissions from Biomass Burning

GOES fire size

Fuel type AVHRR moisture condition

MODIS vegetation properties

CMAQ model

Emissions

Fuel loading Fraction of fuel consumption

Emission FactorBurned area

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Model of Biomass Burning Emissions

AMCFE =E---biomass burning emissions (kg)A---burned area (km2)M--biomass density/fuel loading (kg.km-2)C--fraction of combustionF--fraction of emission

Standard formula (Seiler and Crutzen, 1980):

(1)

i and j define the fire (pixel) locations l is the fuel typek is the time period

Spatial-temporal-distributed formula:

(2)∑∑∑∑= = = =

=K

k

L

l

J

j

I

iijklijklijkijkl FCMAE

1 1 1 1

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Biomass (Live Fuel) Estimates from Generalized Allometric Models and MODIS Data

Shrubs GrassesForests

Broadleaf forests

Needleleafforests

Vegetation Continuous Field (MOD44B) Land Cover Type (MOD12Q1)

Percent tree

Percent non-tree vegetation

LAI (MOD15A2)

Foliage Biomass

Biomass Components

General Allometric

Models

MODIS Data

Mixed forests

Percent bare land

Savannas

Species-specific Allometric Models

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Foliage-Based Allometric Model

11

βα fc MM =

Mc – Branch biomass or aboveground biomass in a tree (kg)

Mf -- foliage biomass

α1, β1 -- coefficients.

(3)

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Foliage Biomass Derived from LAILeaf mass (Mf) (kg C/m2) is calculated using the formula (Heinsch et al., 2003):

Mf=LAI/SLA (4) SLA—the specific leaf area (projected leaf area Kg-1 leaf C) for a pixel, which is obtained from a look-up table. LAI--the leaf area index, obtaining from MODIS LAI product. Forest LAI value in sub-pixel is calculated using LAI product, land cover product, and percent tree product.

MODIS Land cover type SLAEvergreen Needleleaf

Forest (ENF)21.1

Evergreen Broadleaf Forest (EBF)

23.3

Deciduous NeedleafForest (DNF)

31

Deciduous Broadleaf Forest (DBF)

26.2

Mixed Forest (MF) 21.5Grassy woodland (WL) 33.8

Wooded Grassland (Wgrass)

33.8

Close shrub (Closed Shrubland)

12

Open shrub (Open Shrubland)

19

Grass (Grasslands) 40Crop (Croplands) 36

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Maximum monthly MODIS LAI in 2002

low high

>0 <7

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MODIS Land Cover

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MODIS Percent Tree Cover

low high

1 100%

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MODIS Percent Nontree Vegetation

low high

(shrubs, crops, and herbaceous)

0 100%

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Foliage Biomass

Foliage-based Generalized Allometric Models

Biomass Components

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Tree Biomass Components

A. Foliage biomass (tons/ha)B. Branch biomass (tons/ha)C. Aboveground biomass (tons/ha)

Zhang, X., and S. Kondragunta (2006), Estimating forest biomass in the USA using generalized allometric models and MODIS land products, Geophysical Research Letter, 33, L09402, doi:10.1029/2006GL025879.

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Biomass in Shrubs and Grasses

Total Shrub biomass

Grass biomass

2001078.002161.009.1 ccs VVM +−=

(5)

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cslscflfl VMVMM +=

Ml— litter or CWD density in a pixelMlf -- litter or CWD density (kg/m2) for forests changing with forest typesMls--average litter or CWD density (kg/m2) in shrubs and grasses Vcf -- tree coverVcs --the non-forest vegetation cover

Litter or CWD Estimated Using Land Cover Type and Percent Vegetation Cover

(6)

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Fuel Loading from MODIS — Litter and coarse woody detritus

<0.1Mg/ha >24 Mg/ha

>17Mg/ha<0.5Mg/ha

CWD

Litter

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Fuel Moisture Category Factor (MCF) for Determining Combustion and

Emission Factors

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Moisture Category Derived from AVHRR Global Vegetation Index (4km)

Vegetation health condition (VCI) index (Felix Kogan, 1997) :

VCI=100(NDVI-NDVImin)/(NDVImax-NDVImin) -------(7)

NDVI—Normalized Difference Vegetation Index in a week

NDVImax and NDVImin in a pixel are the weekly maximum and minimum values from1985 to 2005

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Temporal Variation in Moisture Category

0

20

40

60

80

100

3 45 87 12917121325529733917 59 10114318522726931135331 73 1151571992412833253 45 87 129171213255297339

Day of year

VC

I

0

1

2

3

4

5

Moi

stur

e ca

toge

ry

Needle leaf forests in the western USA Grass in the western USAShrubs in the western USA Broadleaf forests in the eastern USAMixed forests in the eastern USA

2002 200520042003

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Moisture Category --derived from AVHRR VCI

Early January in 2002 Early July in 2002

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Moisture Category Factor--mcf

Moisture category

Canopy Shrub Grass CWD

Very dry 0.33 0.25 0.125 0.08

Dry 0.5 0.33 0.25 0.12

Moderate 1 0.5 1 0.15

Moist 2 1 2 0.22

Wet 4 2 4 0.31

Very wet 5 4 5 0.75

From Fire Emission Production Simulator (FEPS)

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Fuel Combustion FactorConsumed fractions of tree canopy, shrub, grass (Anderson et al.,

2004)LCL=100*(1-e-1)mcf (8)

LCL--percent of fuel loading consumed for fuel type canopy, shrub, and grass, respectively

mcf—moisture category factor

The combustion factor for litter was assumed to be 100%.

Combustion factor for CWD:Cw=0.6(0.31+(0.03*(0.31-mcf))) (9)

Anderson et al. 2004 Fire Emission Production Simulator (FEPS) User’s Guide (version1.0)

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PM2.5 Emission Factor (Ib/ton)

WetPM2.5

ModeratePM2.5

DryPM2.5

Litter, w 7.9 7.9 7.9

Wood 1-3” 11.9 11.9 11.9

Wood >3” 22.5 18.3 16.2Herbs and

shrubs 21.3 21.3 21.3

Canopy 21.3 21.3 21.3

Anderson et al. 2004 Fire Emission Production Simulator (FEPS) User’s Guide (version1.0)

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GOES Fire Size

Fire occurrences detected from GOES satellite in 2002

•Spatial resolution: 4km•Temporal resolution: 30min• Instantaneous fire sizes in subpixels detected from 3.9 µm and 10.7 µm infrared bands

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Half-hourly PM2.5 Emissions

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Annual PM2.5 Emissions

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Variation in GOES Fire and PM2.5 Emission with Land Cover Type

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PM2.5 Emissions in Each State

0

5000

10000

15000

20000

25000

30000

35000

40000

Washington

IdahoM

ontanaM

ainem

innesotaN

. Dakota

Oregon

N.H

Michigan

V.T

Wiscons

New

York

S. D

akotaM

ichinganW

yoming

MA

CaliforniaC

onnN

evadaP

ENN

Iowa

Utah

NebraskaNew

JerseyO

hioIllinoisIndianaC

oloradoW

Virginia

MD

DEL

VirGinia

Missouri

Kansas

Kentucky

Arizona

N C

arolinaN

ewTennesseeO

klahoma

TexasA

rkansasS

Carolina

GeorG

iaAlabam

aM

ississippiLouisianaFloridaR.I.

PM

2.5

(ton)

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Fire

siz

e (k

m2

PM2.5--2002PM2.5--2003PM2.5--2004Fire-size-2002Fire-size-2003Fire-size-2004

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Comparison of Daily Emissions--April-December 2002

PM2.5 in 2002

0

5000

10000

15000

20000

25000

99 109

119

129

139

149

159

169

179

189

199

209

219

229

239

249

259

269

279

289

299

309

319

329

339

349

359

Day of year

Pm

2.5

(tons

National-Wildfire-Emissions-Inventory NFDRS fuelNESDIS fuelFCCS fuel

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Near Real Time Emission(May 15, 2006)

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Summary• MODIS data combined with foliage-based allometric

models provide a robust tool to establish fuel loading data over a large domain. This dataset is easy to update.

• PM2.5 emissions vary greatly with ecosystems, state, season, and year.

• Instantaneous fire size from GOES satellite is not the same as burned areas. Effort is needed to accurately retrieve burned areas from satellite-based fire sizes.

• Future work will derive reasonable burnt areas by investigating GOES fire size, MODIS fire counts, AVHRR fire counts, and national inventory burned areas in spatial and temporal patterns.

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THANK YOU

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Litter and Coarse Woody Detritus(CWD)

MODIS Land cover type Litter (kg/m2) CWD pool (kg/m2)

Evergreen Needleleaf Forest (ENF) 2.175 3.14Evergreen Broadleaf Forest (EBF) 1.5 1.76Deciduous Needleaf Forest (DNF) 0.85 1.6Deciduous Broadleaf Forest (DBF) 0.85 1.6

Mixed Forest (MF) 1.67 2.2Grassy woodland (WL) 0.63 0.176

Wooded Grassland (Wgrass) 0.63 0.176Cshrub (Closed Shrubland) 1.24 1.21Oshrub (Open Shrubland) 1.24 1.21

Grass (Grasslands) 0.3625 0.06Crop (Croplands) 0.3625 0.06

Non forest vegetation (average from shrub and grass)

0.5019 0.1657

Litter pool and CWD pool modified from Matthews et al. (1997).

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Fire size comparison between GOES data and NEI in 2002Matchup comparison (10' grid)

05

101520253035404550

0 200 400 600 800 1000 1200Fire size from NEI (km2)

Fire

siz

e fro

m G

OES

(km

2)

Matchup comparison (20' grid)

0

50

100

150

200

250

0 500 1000 1500 2000Fire size from NEI (km2)

Fire

siz

e fro

m G

OE

S(k

m2)

Machup comparison (30' grid)

0

50

100

150

200

250

300

350

0 500 1000 1500 2000Fire size from NEI (km2)

Fire

siz

e fro

m G

OE

S(k

m2)

Machup comparison (40' grid)

0

50

100

150

200

250

300

0 500 1000 1500 2000Fire size from NEI (km2)

Fire

siz

e fro

m G

OES

(km

2)

Matchup comparison (60' grid)

0

100

200

300

400

500

0 500 1000 1500 2000Fire size from NEI (km2)

Fire

size fro

m G

OES

(km2)

050

100150200

250300350400450

0 50 100 150 200 250 300 350 400 450

Inventory Burned Area (km2)

GO

ES F

ire S

ize

(km

2 Whole CONUS