WRAP COHA Update Seattle, WA May 25, 2006 Jin Xu

WRAP COHA Update

Seattle, WAMay 25, 2006

Jin Xu

COHA Update• 2003 and 2004 back-trajectories – done• Assess of the representativeness of worst case days of 2002 for the

2000-2004 base period – ongoing, will finish soon• Evaluate winds used for the HYSPLIT backtrajectory analyses –

ongoing, measurement data collected • 8 and 16 year trends analysis - done• PMF modeling by groups using 2000 to 2004 IMPROVE data – done• Analysis of PMF results

– General analysis and discussion: decide how many factors are reasonable for each group - done

– Sensitivity Analysis: group modeling vs. individual modeling – done?– Spatial and temporal analysis – done? – Trajectory analysis – ongoing– Smoke analysis – ongoing?

• 2002 fire database from WRAP, other years from Dr. Tim Brown’s group in DRI. Satellite data and images archived.

• Case studySimilar trajectory analysis as for the causes of dust resultant haze

8 year trends for light extinction coefficient in 20% worst days

Available from COHA Website (following the “trends analysis” link from the homepage):

http://coha.dri.edu/web/general/TrendsAnalysis/8yearTrends/8yeartrend.html


8 and 16 Year Trends

16 year trends for light extinction coefficient in 20% worst days





PMF Modeling for Groups

PMF Results Available for Download from COHA Website

• Two excel files for each group (one for all days and one for 20% worst days) including–Source profiles for the group–Daily contribution of each source factor to PM2.5 mass and aerosol light extinction coefficient for each site–Comparison between measured and predicted PM2.5 mass concentration–Pie chart for each group

Web Address (following the “PMF Modeling” link from the homepage):

http://coa.dri.edu/web/general/tools_PMFModeling.html

http://coa.dri.edu/web/general/tools_PMFModeling.html

PMF Results Page (under construction)

Source Profiles

Daily Contributions

Measured Versus Predicted PM2.5 Mass Concentration Alaska

y = 0.7849x + 0.4763

R2 = 0.7989

0

5

10

15

20

25

30

35

0 10 20 30 40

Measured PM2.5 (ug/m3)

Pre

dic

ted

PM

2.5

(ug

/m3)

California Coast

y = 0.7825x + 1.241

R2 = 0.8338

0

5

10

15

20

25

30

0 5 10 15 20 25 30


Pre

dic

ted

PM

2.5

(ug

/m3)

Central Rockies

y = 0.7946x + 0.731

R2 = 0.8611

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50


Pre

dic

ted

PM

2.5

(ug

/m3)

Columbia River Gorge

y = 0.8403x + 1.0154

R2 = 0.9028

0

5

10

15

20

25

30

35

0 10 20 30 40


Pre

dic

ted

PM

2.5

(ug

/m3)

Colorado Plateau

y = 0.851x + 0.5433

R2 = 0.8727

0

20

40

60

80

0 20 40 60 80


Pre

dic

ted

PM

2.5

(ug

/m3)

Death Valley

y = 0.9134x + 0.4558

R2 = 0.9379

0

10

20

30

40

0 10 20 30 40


Pre

dic

ted

PM

2.5

(ug

/m3)

Great Basin

y = 0.9218x + 0.2609

R2 = 0.9392

0

10

20

30

0 5 10 15 20 25


Pre

dic

ted

PM

2.5

(ug

/m3)

Hells Canyon

y = 0.7453x + 1.0077

R2 = 0.7648

0

10

20

30

40

50

60

70

0 20 40 60 80 100


Pre

dic

ted

PM

2.5

(ug

/m3)

Hawaii

y = 0.9302x + 0.2572

R2 = 0.9443

0

10

20

30

40

0 10 20 30 40


Pre

dic

ted

PM

2.5

(ug

/m3)

Measured Versus Predicted PM2.5 Mass Concentration Mogollon Plateau

y = 0.9296x + 0.3693

R2 = 0.9584

0

10

20

30

40

50

60

0 10 20 30 40 50 60


Pre

dic

ted

PM

2.5

(ug

/m3)

Northern Great Plains

y = 0.7225x + 1.2635

R2 = 0.8379

0

10

20

30

40

50

0 10 20 30 40 50


Pre

dic

ted

PM

2.5

(ug

/m3)

Northern Rockies

y = 0.8784x + 0.4607

R2 = 0.8936

0

10

20

30

40

50

60

0 10 20 30 40 50 60


Pre

dic

ted

PM

2.5

(ug

/m3)

Northwest

y = 0.9316x + 0.3598

R2 = 0.9565

0

10

20

30

40

50

60

0 10 20 30 40 50 60


Pre

dic

ted

PM

2.5

(ug

/m3)

Oregon & Northern California

y = 0.601x + 1.4867

R2 = 0.6778

0

10

20

30

40

50

0 20 40 60 80


Pre

dic

ted

PM

2.5

(ug

/m3)

Sierra Nevadas

y = 0.89x + 0.7794

R2 = 0.9307

0

10

20

30

40

50

0 10 20 30 40 50 60


Pre

dic

ted

PM

2.5

(ug

/m3)

Southern Arizona

y = 0.871x + 0.8802

R2 = 0.8923

0

10

20

30

40

50

0 10 20 30 40 50


Pre

dic

ted

PM

2.5

(ug

/m3)

Southern California

y = 0.8762x + 0.8179

R2 = 0.9067

0

10

20

30

40

0 10 20 30 40


Pre

dic

ted

PM

2.5

(ug

/m3)

West Texas

y = 0.9028x + 0.6585

R2 = 0.9274

0

10

20

30

40

50

60

0 20 40 60 80


Pre

dic

ted

PM

2.5

(ug

/m3)

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group Alaska

Urban/Diesel

6%

Secondary44%

Smoke39%

Dust4%

Mixture5% Aged Sea

Salt2%

California Coast

Sulfate-rich

Secondary/Oil

Combustion

17%

Road Dust8%

Nitrate-rich Secondary

14%

Smoke/Mobile29%

Aged Sea Salt32%

Central Rockies


8%

Dust30%

Sulfate-rich

Secondary/Coal

Combustion

8%

Mobile9%

Smoke45%


Mobile15%

Sulfate-rich

Secondary18%

Smoke26%

Dust10%

Aged Sea Salt/Paper

Mill10%


21%

Colorado Plateau

Dust120%

Smoke35%Nitrate-rich

Secondary9%

Dust211%

Urban/Diesel

3%

Sulfate-rich

Secondary22%

Death Valley

Dust118%

Sulfate-rich

Secondary25%

Mobile/Others5% Road

Dust/Mobile

11%Nitrate-rich Secondary

6% Dust223%

Coal Combustio

n4%

Smoke8%

Great Basin

Smoke39%

Dust2-Si28%

Gasoline3%

Diesel1%

Dust1-Ca10%

Sulfate-rich

Secondary15%


4%

Hawaii

Sulfate-rich

Secondary63%

Smoke16%

Dust5%

Sea Salt8%


(Iron)3%

Shipping5%

Hells Canyon

Road Dust/Mobil

le2%


/Mobile3%

Dust17%

Sulfate-rich

Secondary23%

Smoke55%

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group

Mogollon Plateau

Dust24%

Sulfate-rich

Secondary21%

Road Dust16%

Diesel1%

Smoke30%

Gasoline1%


7%



15%

Smoke44%

Road Dust/Mobil

le11%

Sulfate-rich

Secondary23%

Dust7%

Northern Rockies

Urban/Mobile1%

Dust16%

Smoke64%

Sulfate-rich

Secondary17%

Road Dust/Mobil

e2%

Northwest

Smoke41%

Sulfate-rich

Secondary20%

Mobile6%

Oil Combustio

n4%

Aged Sea Salt8%

Mixed (metals)

1%


8% Dust12%


Urban Mixture

6%

Smoke/Mobile61%

Aged Sea Salt18%

Road Dust/Mobil

e15%

Sierra Nevadas


21%

Dust17% Road

Dust/Mobile

8%

Mobile2%

Smoke35%

Sulfate-rich

Secondary17%

Southern Arizona

Dust1 (with

Nitrate)19%

Oil Combustio

n (Shipping)

3%Smoke

13%

Sulfate-rich

Secondary19%

Mobile/Road Dust

15%


10%

Mixture (Coal

Combustion, Metal

Smelting)1%

Dust220%

Southern California

Smoke/Urban

21%


25%

Sulfate-rich

Secondary21%

Road Dust/Mobil

e6%

Mobile7%

Oil Combustion/Shipping

6% Dust14%

West Texas

Mixture1%

Smoke15%

Dust220%

Sulfate-rich

Secondary36%

Dust118%


10%

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group (20% worst days)

Alaska

Urban/Diesel

5%

Secondary36%

Smoke53%

Dust3%

Mixture2%

Aged Sea Salt1%

California Coast

Sulfate-rich

Secondary/Oil

Combustion

16%Road Dust8%


20%

Smoke/Mobile30%

Aged Sea Salt26%

Central Rockies


8%

Dust34%

Sulfate-rich

Secondary/Coal

Combustion

6%

Mobile7%

Smoke45%


Mobile12%

Sulfate-rich

Secondary14%

Smoke24%

Dust6%

Aged Sea Salt/Paper

Mill5%


39%

Colorado Plateau

Dust120%

Smoke40%Nitrate-rich

Secondary7%

Dust212%

Urban/Diesel

3%

Sulfate-rich

Secondary18%

Death Valley

Dust120%

Sulfate-rich

Secondary24%

Mobile/Others4%

Road Dust/Mobil

e11%


6% Dust226%

Coal Combustio

n3%

Smoke6%

Great Basin

Smoke38%

Dust2-Si33%

Gasoline3%

Diesel1%

Dust1-Ca11%

Sulfate-rich

Secondary11%


3%

Hells Canyon

Road Dust/Mobil

le2%


/Mobile2%

Dust16%

Sulfate-rich

Secondary18%

Smoke62%

Hawaii

Sulfate-rich

Secondary82%

Smoke9%

Dust3%

Sea Salt1%


(Iron)2%

Shipping3%

Average Contributions of Major Source Factors to PM2.5 Mass for Each Group (20% worst days)

Mogollon Plateau

Dust26%

Sulfate-rich

Secondary17%

Road Dust18%

Diesel1%

Smoke32%

Gasoline0%


6%



19%

Smoke41%

Road Dust/Mobil

le10%

Sulfate-rich

Secondary24%

Dust6%

Northern Rockies

Urban/Mobile0%

Dust15%

Smoke73%

Sulfate-rich

Secondary11%

Road Dust/Mobil

e1%

Northwest

Smoke47%

Sulfate-rich

Secondary18%

Mobile6%

Oil Combustio

n4%

Aged Sea Salt6%

Mixed (metals)

1%


9% Dust9%


Urban Mixture

5%

Smoke/Mobile63%

Aged Sea Salt16%

Road Dust/Mobil

e16%

Sierra Nevadas


29%

Dust14%

Road Dust/Mobil

e6%

Mobile2%

Smoke35%

Sulfate-rich

Secondary14%

Southern Arizona

Dust1 (with

Nitrate)20%

Oil Combustio

n (Shipping)

2%

Smoke11%

Sulfate-rich

Secondary15%

Mobile/Road Dust

17%


15%

Mixture (Coal

Combustion, Metal

Smelting)0%

Dust220%

Southern California

Smoke/Urban

19%


33%

Sulfate-rich

Secondary21%

Road Dust/Mobil

e4%

Mobile6%


6%

Dust11%

West Texas

Mixture1%

Smoke12%

Dust223%

Sulfate-rich

Secondary38%

Dust116%


10%

Smoke Analysis

• General Analysis:– Summarize the contributions of PMF smoke factor to PM2.5 and OC

mass.– Compare PMF results between sites with known big contributions

from smoke and the others without.– Investigate the relationship of OC / EC and the loading of the

“smoke” factor.

• Case Study (for selected sites):– The prescreening for identification of cases where smoke is the

predominant source of fine particles at the receptor site and/or in other sites located in the region

– The retrieval of air mass backward trajectories for the receptor sites. – Compilation of detailed records of biomass burning events.– Integration of the aforementioned data types into a GIS tool.

Smoke Source Profiles

Averaged based on profiles generated for the 18 groups in WRAP. Error bar represents one standard deviation

-0.05

0

0.05

0.1

0.15

0.2

0.25

AS BR CA EC1 EC2 EC3 OC1 OC2 OC3 OC4 OP CL CR CU H FE PB MG MN NI NO3 P K RB SE SI NA SR S TI V ZN ZR

Average Contribution of PMF Smoke Factor to PM2.5 Mass during 2000 - 2004

Average Contribution of PMF Smoke Factor to PM2.5 in 2002

WRAP CMAQ Modeling Results (only modeled natural fire emissions)

PMF Results

Missing hot spot due to missing IMPROVE data because teflon filters were clogged during the peak of the Rodeo-Chediski Fire (burned 462,614 acres, the largest most severe fire in Arizona history)

PEFO1 PMF Smoke Factor Contribution to PM2.5Smoke

0

5

10

15

20

25

30

1/2/

02

2/1/

02

3/3/

02

4/2/

02

5/2/

02

6/1/

02

7/1/

02

7/31

/02

8/30

/02

9/29

/02

10/2

9/02

11/2

8/02

12/2

8/02

Sm

oke

Co

ntr

ibu

itio

n t

o P

M2.

5 (u

g/m

3)

PEFO1 PMF Smoke Factor Contribution to Bext

Smoke

0

20

40

60

80

100

Sm

oke C

on

trib

uti

on

to

Lig

ht

Exti

ncti

on

(1/M

m)

PMF source factor contributions to PM2.5 at PEFO1 in 2002

0

0.5

1

1.5

2

2.5

3

Dust Sulfate-richSecondary

Nitrate-richSecondary

Smoke Gasoline Road Dust Diesel

PMF Source Factor

Co

ntr

ibu

tio

n t

o P

M2

.5 (

ug

/m3

)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Dust Sulfate-richSecondary

Nitrate-richSecondary

Smoke Gasoline Road Dust Diesel

PMF Source Factor

Co

ntr

ibu

tio

n t

o P

M2.

5 (u

g/m

3)

Data missing on 6/22 and 6/25

Assume all (and only) OMC is from smoke on 6/22 and 6/25

Fire Detections in 2002• Web Fire Mapper displays active fires detected by the MODIS Rapid

Response System, a collaboration between the NASA Goddard Space Flight Centre (GSFC) and the University of Maryland (UMD).

Smoke Case Study

Hypothesis to-be-testedAre “smoke” concentrations associated with fire events in the vicinity and/or upwind of the site?

LimitationsSpatial variation of fire emissions and air mass trajectory, no precipitation, no plume information THUS between-cases comparison cannot be done and;no quantitative information can be obtained

MethodologyAir mass backward trajectories (at 500 m) and WRAP 2002 Fire Emissions Inventory; approximately 52 cases for Sawtooth, Badlands and San Gorgonio were analyzed (high, average and low “smoke” days)

ProductMaps of air mass trajectory and active fires during that day

Legends Trajectory Wildfire Agricultural fires no-CA Agricultural fires CA Rangeland fires MODIS Fires

Filename: YYYYMMDD_SITE_day#SITE= SAWT, BADL, SAGO#=0 Sampling Day #=1 Sampling Day-1#=2 Sampling Day-2e.g. 20020809_SAGO_day0

Wildfires Ag/NFRange 0-20 (0-1) tons

20-500 (1-5) tons

500-2000 (5-20) tons

2000-4000 tons

4000-16000 tons

16000-60000 tons

ftp.dri.edu/pub/ilias/smoke

Case Study – Sawtooth National Forest, ID (SAWT1)

0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


Road Dust/Mobile

Nitrate-rich Secondary/Mobile

Smoke

Dust

Sulfate-rich Secondary

Time Series of Factor Contributions to PM2.5 (ug/m3) at SAWT1 in 2002

0

2

4

6

8

10

12

12/30/2001 2/28/2002 4/29/2002 6/28/2002 8/27/2002 10/26/2002 12/25/2002

Road Dust/Mobille


Smoke

Dust


Average Contributions of Source Factors to PM2.5 Mass Concentration in Sawtooth National Forest in 2002

Road Dust/Mobille2%

Smoke65%

Dust14%


17% Nitrate-rich Secondary/Mobile

2%

Measured Versus Predicted OMC Concentration at SAWT1 in 2002

y = 1.1025x + 0.1758

R2 = 0.9093

0

2

4

6

8

10

0 2 4 6 8

Calculated OMC

Me

as

ure

d O

MC

SmokeOMC72%

OtherOMC10%

Unexplained OMC18%

-0.5

4.5

9.5

14.5

19.5

24.5

29.5

11/5 12/25 2/13 4/4 5/24 7/13 9/1 10/21 12/10 1/29

Date

SmokeOMC

OtherOMC

Unexplained OMC

OMC/LAC

Factor Contributions to OMC at SAWT1 in 2002

Gail Tonnesen and Tom Moore, Modeling Sensitivity Runs for Fire Emissions, White Paper for WRAP, December, 2004:

“OC/EC ratio values on order of 3-5 (OMC/LAC ~ 4.2-7) suggest fossil fuel combustion contributions, while values greater than 7 (OMC/LAC>9.8) suggest fire emissions. High OC/EC rations suggest a source mix resulting from either inefficient combustion (vegetation fires) or secondary organic formation.”

?

Biogenic Emissions (SOA), Aged Smoke Plume or Inefficient Burning (Vegetation Fires) ?

7/22

Aged Smoke PlumeSawtoothJuly 22, 2002Smoke= 10.0 μg/m3

79.4% of PM2.5

07/25/2002: Local WildfiresSmoke=9.7 μg/m3 (89.16%)

07/22/2002: Aged Wildfire Smoke PlumeSmoke=10.0 μg/m3 (79.38%)

8 Wildfire Agricultural fires8 Sampling day-2

8 Sampling day-1

8 Sampling Day

Case Study – Badlands National Park, SD (BADL1)

0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1



Smoke

Dust

Road Dust/Mobile


Time Series of Factor Contributions to PM2.5 (ug/m3) at BADL1 in 2002

0

1

2

3

4

5

6

7

8

11/5/2001 12/25/2001 2/13/2002 4/4/2002 5/24/2002 7/13/2002 9/1/2002 10/21/2002 12/10/2002 1/29/2003


Smoke

Dust

Road Dust/Mobille


Average Contributions of Source Factors to PM2.5 Mass Concentration in Badlands National Park in 2002


12%

Smoke41%

Dust8%

Road Dust/Mobille14%


25%

Measured Versus Predicted OMC Concentration at BADL1 in 2002

y = 1.1952x - 0.0837

R2 = 0.7446

0

2

4

6

0 1 2 3 4

Calculated OMC

Me

as

ure

d O

MC

SmokeOMC70%

OtherOMC20%

Unexplained OMC10%

-0.5

4.5

9.5

14.5

19.5

24.5

29.5

11/5 12/25 2/13 4/4 5/24 7/13 9/1 10/21 12/10 1/29

Date

SmokeOMC

OtherOMC

Unexplained OMC

OMC/LAC

Factor Contributions to OMC at BADL1 in 2002


6/22

5/29

Aged Rangeland and Agricultural FiresBadlandsMay 29, 2002Smoke= 1.5 μg/m3

40.5% of PM2.5

Aged Smoke PlumeBadlandsJun. 22, 2002Smoke= 5.2 μg/m3

49.0% of PM2.5

0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


0.0001

0.001

0.01

0.1

1


Case Study – San Gorgonio Wilderness, CA (SAGO1)Smoke/Urban


Dust

Mobile


Road Dust/Mobile


Average Contributions of Source Factors to PM2.5 Mass Concentration in San Gorgonio Wilderness in 2002

Smoke/Urban19%


36%

Dust13%

Mobile8%

Oil Combustion/Shippin

g4%


15%

Road Dust/Mobile5%

Time Series of Factor Contributions to PM2.5 (ug/m3) at SAGO1 in 2002

0

2

4

6

8

10

12

14

16

11/5/2001 12/25/2001 2/13/2002 4/4/2002 5/24/2002 7/13/2002 9/1/2002 10/21/2002 12/10/2002 1/29/2003

Smoke/Urban


Dust

Mobile



Road Dust/Mobile

Measured Versus Predicted OC Concentration at SAGO1 in 2002

y = 1.2496x - 0.1531

R2 = 0.8056

0

2

4

6

8

0 1 2 3 4 5

Calculated OMC

Me

as

ure

d O

MC

OtherOMC51%

Smoke/Urban OMC36%

Unexplained OMC13%

-0.5

4.5

9.5

14.5

19.5

24.5

11/5 12/25 2/13 4/4 5/24 7/13 9/1 10/21 12/10 1/29

Date

Smoke/Urban OMC

OtherOMC

Unexplained OMC

OMC/LAC

Factor Contributions to OC at SAGO1 in 2002


8/98/21

Agricultural (and Wild) FiresSan GorgonioAug. 9, 2002Smoke= 5.3 μg/m3

37.5% of PM2.5

Date Smoke %Smoke/FM Confidence-Probable sources

1/20/2002 0.27 54.25 (+) No events

1/23/2002 0.32 9.11 (+) Wildfires

3/9/2002 0.30 14.20 (+++) NFRange

3/27/2002 0.29 9.19 (+++) NFRange

4/20/2002 0.28 11.72 (+++) NFRange

4/26/2002 5.10 58.61 (+++) NFRange

5/17/2002 5.30 76.31 (+++) NFRange

6/22/2002 2.12 75.16 (++) NFRange &Wildfires

6/25/2002 2.44 45.60 (++) NFRange&Wildfires

7/22/2002 10.02 79.38 (+++) Wildfires

7/25/2002 9.67 89.16 (+++) Wildfires

7/31/2002 9.47 103.56 (++) Wildfires

8/3/2002 7.94 110.27 (+++) Wildfires&agricultural

8/6/2002 10.16 96.50 (+++) Wildfires

8/18/2002 6.33 69.55 (++) Wildfires

8/21/2002 9.24 92.07 (++) Wildfires&agricultural

9/8/2002 2.83 71.70 (+++) NFRange

9/20/2002 2.27 74.90 (+++) R NFRange &Agric.

9/23/2002 2.30 81.21 (+) Agricultural

9/29/2002 2.69 73.74 (++) Ag&Range&Wildfires

11/13/2002 4.20 82.14 (+++) Wildfires

11/16/2002 2.20 68.74 (+++) Wildfires

12/16/2002 0.27 77.56 (+) No events

Sawtooth, ID

Bold= high smoke days

Red = 20% worst days

Date Smoke % Smoke/Fine Mass Confidence and probable sources

3/21/2002 1.68 41.35 (+++) NFRange

4/20/2002 1.76 47.83 Local NFRange

4/23/2002 1.75 30.08 (+++) Rangelands&Agricultural

4/29/2002 1.70 48.90 (+++) NFRange

5/5/2002 1.67 36.00 (+++) NFRange

5/17/2002 1.66 59.09 (+++) NFRange

5/29/2002 1.47 40.49 (+++) NFRange &agricultural

6/22/2002 5.22 49.02 (+++) NFRange &Wildfires


7/25/2002 5.12 83.77 (+++) Wildfires

7/31/2002 6.90 78.98 (+++) Wildfires

8/3/2002 3.57 41.79 (+++) Wildfires

8/27/2002 5.25 114.33 (++) Wildfires

9/5/2002 4.80 49.09 (+++) NFRange&Wildfires

9/17/2002 1.74 48.46 (+) Rangelands

Badlands, SD



Date Smoke % Smoke/FM Confidence and probable sources


4/20/2002 1.34 20.36 (+) Wildfires

5/2/2002 1.30 8.82 (+++) NFRange

5/5/2002 1.45 17.30 (+) No events

5/14/2002 4.14 39.43 (+++) NFRange

6/13/2002 3.78 51.90 (+) Wildfires

6/19/2002 4.82 32.79 (+++) Wildfires

7/10/2002 5.67 55.41 (+) No events





9/5/2002 1.33 16.80 (+++) NFRange&Wildfires

10/17/2002 1.48 10.82 (+) NFRange

10/23/2002 1.51 12.65 (++) NFRange &Wildfires

San Giorgonio, CA

PMF resolved a mixed smoke/urban factor



For most of the examined cases, air masses intercepted fire events; only cases with very low PM2.5 mass (<1 μg/m3) were not associated with fire events

Based on the analysis, the contributions of the following types of fires were determined: (a) wildfires near the site (“hot” emissions); (b) wildfires upwind of the site (aged smoke); (c) agricultural emissions; (d) rangeland fires

Case Study Conclusions

Given the limitations of this analysis: Sawtooth: Spring/fall smoke events are due to rangeland fires; wildfires and local agricultural fires contribute to smoke during summerBadland: Spring/fall smoke events are due to rangeland fires; wildfires contribute to smoke during summerSan Giorgonio: Smoke is usually mixed with urban emissions (air masses normally remain over LA for at least 12 h); Summer smoke events are usually associated with agricultural fires and upwind transport from large wildfires

Summary• PMF is a useful tool for resolving aerosol source types and attributing aerosol

loading to different sources based on ambient data at a receptor site. It works better in regions where sources are more distinguishable (e.g. near urban area).

• PMF modeling results (close to CMAQ modeling results?) suggest that smoke contributed on average ~1.5 ug/m3 to PM2.5 in the Class I areas of the Western U.S. in 2002, much higher than the value of 0.46 ug/m3 assumed throughout the West in the EPA natural guidance document.

• It is hard (if not impossible) for PMF to separate the primary and secondary OC into different factors using the IMPROVE data.

• Generally, higher OC/EC ratios were observed during the fire events. A relatively significant amount of OC was not apportioned by PMF modeling for some sites. The no apportioned OC usually peaks when OC/EC ratio was high.

– Secondary OC from biogenic emissions can result in high OC/EC ratio.

– Aged smoke plumes usually contain a significant amount of OC generated from oxidation of biogenic VOCs from fires. OC/EC ratio is expected to be higher when OC is mostly from long-range transport smoke plumes than from local fires.

– The ratio also depends on the burning type (e.g. forest fire < agricultural burning) and burning conditions.

• It is possible to qualitatively (maybe even semi-quantitatively) attribute fire emissions to different fire types when detailed fire emissions inventory data are available.

Documents

WRAP COHA Update Seattle, WA May 25, 2006 Jin Xu