WRAP RMC
Phase II Wind Blown Dust Project
Results & Status
ENVIRON International Corporation
and
University of California, Riverside
Dust Emission Joint Forum Meeting
Las Vegas, NV
November 16, 2004
Phase II Project Overview
• Develop improved general methodology based on Phase I
recommendations and recent literature review
• Update gridded PM inventory of WB Dust for 2002 using
the Inter-RPO regional modeling domain
• Develop of surface friction velocities and threshold friction
velocities
• Develop improved emission flux relationships
• Improve vacant land characterization
– Disturbance
– Land use type
– Reservoirs
• Conduct model performance evaluation
General Formulation for Emissions
Estimation
• Dust = f(LULC,z0,u*,u*th,SC)
• u* = f(u,z0)
• u*th = f(z0)
• z0 = f(LULC)
Threshold Friction Velocities
• u*th determined from relations developed by Marticorena, et
al, (1997)
3
2.5
u*t = 0.31e
7.44x(Zo)
2
R = 0.60
u*t (m s -1)
2
u*t = 0.30e
7.22x(Zo)
1.5
1
0.5
0
0.00001
0.0001
wind tunnel data
Expon. (wind tunnel data)
0.001
0.01
zo (cm)
Marticorena et al. 1997
Expon. (Marticorena et al. 1997)
0.1
1
Emission Rates
• Depends on soil type; based on results of Alfaro and
Gomes (2001)
0.00001
FFS
-6
3.97
F = 2.45x10 (u*)
-2 -1
Emission Flux (F, g cm s )
0.000001
FSS
FS
MS
CS
Power (FSS)
Power (FS)
Power (MS)
Power (CS)
FS
-7
2.44
F = 9.33x10 (u*)
0.0000001
0.00000001
MS
-7
2.64
F = 1.243x10 (u*)
CS
-7
3.44
F = 1.24x10 (u*)
0.000000001
0
0.1
0.2
0.3
0.4
0.5
0.6
-1
Friction Velocity (m s )
0.7
0.8
0.9
1
NLCD Summary
NLCD Land Use Summary (Continental US)
Land Use Type
Water
Urban
Barren
Forest
Shrubland
Grasslands
Agricultural
Wetlands
Total
Total excluding water
Total Area (acres) %
% excluding
98,484,739
5.0%
35,629,865
1.8%
37,204,176
1.9%
556,424,387
28.1%
355,796,082
18.0%
302,601,621
15.3%
515,624,831
26.0%
78,127,135
3.9%
1,979,892,836
100.0%
1,881,408,097
water
1.9%
2.0%
29.6%
18.9%
16.1%
27.4%
4.2%
100.0%
Characteristics of Dust Categories
3
Dust Code
4
6
7
Land use category
Ag.
Grass
Shrubs
Barren
Surface roughness (cm)
0.031
0.1
0.05
0.002
Threshold friction Velocity
(mile/h)
8.33
13.81
9.62
6.81
44.25
32.75
28.50
Threshold wind velocity at 38m
height (mile/h)
29.50
Soil Characteristics
U.S. soil texture Chatenet (1996)
Chatenet (1996)
Soil Texture (from Chamley, 1987) Groupings
sand
sand
CS
loamy sand
sand
CS
sandy loam
silty sand
MS
sandy clay loam clayey sand
MS
sandy clay
MS
clayey sand
(medium) loam clayey silty sand
MS
clay loam
clayey silty sand
MS
silty loam
clayey sandy silt
FS
silty clay loam clayey silt
FFS
silt
silt
FFS
silty clay
silty clay
FFS
clay
sandy clay
(10-50% sand, 75-50% clay)
FS
clay
sandy silty clay
(10-45% sand, 12-45% silt,
35-75% clay)
FS
Reservoir Characteristics
• All soils assumed loose, undisturbed
• Dust events limited to 10hrs/day
– Sensitivity simulations conducted based on above
assumptions
• Rain events: Dust re-initiated after set number of
days dependent on soil texture, amount of rainfall
and season
Number of days after rain event to
re-initiate wind erosion
• Rainfall > 2 inches
Soil type
• Rainfall < 2 inches
Spring/Fall
Summer
Winter
Sand
3
2.1
4.2
Sandy Loam
3
2.1
Fine Sand
Loam
3
Loam
Soil type
Spring/Fall
Summer
Winter
Sand
1
0.7
1.4
4.2
Sandy Loam
1
0.7
1.4
2.1
4.2
Fine Sand Loam
1
0.7
1.4
4
2.9
3.8
Loam
2
1.4
2.8
Silt Loam
4
2.9
3.8
Silt Loam
2
1.4
2.8
Sandy Clay
Loam
4
2.9
3.8
Sandy Clay Loam
2
1.4
2.8
Clay Loam
5
3.6
7.2
Clay Loam
3
2
4
Silty Clay
Loam
6
4.3
8.6
Silty Clay Loam
4
2.8
5.6
Clay
7
5
10
Clay
5
3.6
7.2
Model Sensitivity Simulations
• Run a :
– No limitation on dust event duration
– All soils considered loose undisturbed
• Run b :
– Dust events limited to 10 hrs/day
– All soils considered loose undisturbed
Model Sensitivity Simulations
• Run c :
– No limitation on dust event duration
– Assume 10% of barren, grass & shrublands area is disturbed
– Threshold velocity for grass & shrublands = 0.5 * undisturbed
value
– Threshold velocity for barren lands = .27 * undisturbed value
• Run d :
– Dust events limited to 10 hrs/day for undisturbed soils
– Assume 10% of barren, grass & shrublands area is disturbed
– Threshold velocity for grass & shrublands = 0.5 * undisturbed
value
– Threshold velocity for barren lands = .27 * undisturbed value
Model Results
Scenario a: no limit on duration; all soils loose, undisturbed
Dust emissions, Scenario a
1.0%
20.4%
18.4%
Dust Code 3 - Ag
Dust Code 4 - Grasslands
Dust Code 6 - Shrublands
Dust Code 7 - Barren
60.2%
Model Results
Scenario b: event duration <=10 hrs/day; all soils loose, undisturbed
Dust emissions, Scenario b
1.1%
21.5%
15.7%
Dust Code 3 - Ag
Dust Code 4 - Grasslands
Dust Code 6 - Shrublands
Dust Code 7 - Barren
61.7%
Model Results
Scenario c: no limit on duration; assume 10% disturbed area for grass,
shrub, barren lands
Dust emissions, Scenario c
2.1%
23.3%
Dust Code 3 - Ag
Dust Code 4 - Grasslands
Dust Code 6 - Shrublands
Dust Code 7 - Barren
48.6%
26.0%
Model Results
Scenario d: event duration <= 10hrs/day for disturbed soils; assume 10%
disturbed area for grass, shrub, barren lands
Dust emissions, Scenario d
2.8%
Dust Code 3 - Ag
Dust Code 4 - Grasslands
Dust Code 6 - Shrublands
Dust Code 7 - Barren
25.7%
42.9%
28.6%
Dust Totals for WRAP States
tons/year
Scenario
WRAP States
a
2,222,219
Domain Total
(US only)
9,451,368
b
1,310,120
5,228,818
c
3,077,196
11,098,731
d
2,165,096
6,876,180
1996
2,240,288
4,366,907
Annual PM10
Dust Yearly Total by State
Western States
1800000
1600000
scen a
scen b
1400000
scen c
scen d
1200000
1000000
800000
600000
400000
State
WY
WI
WA
UT
TX
SD
OR
OK
ND
NM
NV
NE
MT
MO
MN
LA
KS
ID
CO
CA
AR
0
IA
200000
AZ
Ton/y
1996
Annual PM10
Dust Yearly Total by State
WRAP States
1000000
900000
800000
scen a
scen b
700000
scen c
scen d
1996
500000
400000
300000
200000
100000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
CA
0
AZ
Ton/y
600000
Comparison of Monthly Dust Emissions
Monthly Dust Emissions
4500000.0
4000000.0
3500000.0
scen a
scen b
scen c
scen d
Ton/Month
3000000.0
2500000.0
2000000.0
1500000.0
1000000.0
500000.0
0.0
1
2
3
4
5
6
7
month
8
9
10
11
12
Annual PM10 from Ag Land for WRAP States
Dust from Category 3 (Ag land)
PM10 Yearly Total
400000
scen a
350000
scen b
300000
scen c
scen d
200000
150000
100000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
0
CA
50000
AZ
Ton/y
250000
Annual PM10 from Grass Land for WRAP States
Dust from Category 4 (Grass land)
PM10 Yearly Total
320000
280000
scen a
scen b
240000
scen c
scen d
160000
120000
80000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
0
CA
40000
AZ
Ton/y
200000
Annual PM10 from Shrub Land for WRAP States
Dust from Category 6 (Shrub land)
PM10 Yearly Total
180000
160000
scen a
scen b
120000
scen c
100000
scen d
80000
60000
40000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
0
CA
20000
AZ
Ton/y
140000
Annual PM10 from Barren Land for WRAP States
Dust from Category 7 (Barren land)
PM10 Yearly Total
50000
45000
scen a
40000
scen b
35000
scen c
scen d
25000
20000
15000
10000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
0
CA
5000
AZ
Ton/y
30000
Scenario b Annual PM10 from All Dust Categories
for WRAP States
Dust from all Categories for Scenario b
PM10 Yearly Total
240000
Dust Code 3 - Ag
Dust Code 4 - Grasslands
210000
Dust Code 6 - Shrublands
Dust Code 7 - Barren
180000
120000
90000
60000
30000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
CA
0
AZ
Ton/y
150000
Scenario d Annual PM10 from All Dust Categories
for WRAP States
Dust from all Categories for Scenario d
PM10 Yearly Total
240000
Dust Code 3 - Ag
Dust Code 4 - Grasslands
Dust Code 6 - Shrublands
Dust Code 7 - Barren
210000
180000
120000
90000
60000
30000
State
WY
WA
UT
SD
OR
ND
NM
NV
MT
ID
CO
CA
0
AZ
Ton/y
150000
2002 Annual PMC
Scenario a: no limit on duration; all soils loose, undisturbed
2002 Annual PMC
Scenario b: event duration <=10 hrs/day; all soils loose, undisturbed
2002 Annual PMC
Scenario c: no limit on duration; assume 10% disturbed area for grass,
shrub, barren lands
2002 Annual PMC
Scenario d: event duration <=10 hrs/day; assume 10% disturbed area for
grass, shrub, barren lands
2002 Annual PMC
Scenario b: event duration <=10 hrs/day; all soils loose, undisturbed
2002 Seasonal PMC
Model Limitations
• Grid resolution
– Coarse resolution of met data can’t resolve high wind events;
wind gusts
• LULC and Soils data
– LULC not detailed enough on a regional-scale
– Soils data lacks depth of layers, moisture data
• Agricultural land adjustments
– No agricultural data for Eastern states (prepared for WRAP &
CENRAP regions only)
– Data gaps in Ag Census
Model Performance Evaluation
1. Evaluate model results for reasonableness and accuracy
–
Compare predicted WB dust emissions near IMPROVE
monitors with measured IMPROVE dust extinction (Bdust)
2. Enhancements to CMAQ to track WB and other dust
–
Evaluate model CMAQ model performance with and without
WB dust emissions
3. Refined model performance evaluation using results of
Etyemezian, et al.
–
For events characterized as wind blown dust events,
determine whether dust model predicts impacts
2002 Coarse Mass
Seasonal Coarse Mass (2002)
Annual Fine & Coarse Mass (2003)
Model Performance Evaluation (1)
•
Evaluate model results for reasonableness and accuracy
•
Compare predicted WB dust emissions near IMPROVE monitors with
measured IMPROVE dust extinction (Bdust)
–
Identify occurrences of:
1)
2)
3)
4)
•
•
•
Zero WB dust and near-zero Bdust
Enhanced WB dust and near-zero Bdust
No WB dust and elevated Bdust
Enhanced WB dust and elevated Bdust
Modeled dust averaged over 5 x 5 block of grid cells centered on
IMPROVE sites
Daily averaged model results paired (in time & space) with
monitored data
Compare modeled PM with Bextdust
–
Bextdust = [FS] + 0.6[CM]
Model Performance Evaluation (1)
YELL
100
100
80
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
YELL
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
ZION
100
100
80
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
ZION
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
YOSE
100
100
80
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
YOSE
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
LOST
100
100
80
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
LOST
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
CAPI
100
100
80
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
CAPI
60
40
20
60
40
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
KALM
100
100
80
80
Normalized PM10 (avg5x5)
Normalized PM10 (avg5x5)
MELA
60
40
20
60
40
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
GUMO
100
100
80
80
Normalized PM10 (avg5x5)
Normalized PM10 (avg5x5)
PORE
60
40
20
60
40
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
DEVA
100
100
80
80
Normalized PM10 (avg5x5)
Normalized PM10 (avg5x5)
MORA
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (1)
BADL
BADL
100
100
80
Normalized PM10 (avg3x3)
Normalized PM10 (avg5x5)
80
60
40
60
40
20
20
0
0
0
20
40
60
Normalized DUST_Bext
80
100
0
20
40
60
Normalized DUST_Bext
80
100
Model Performance Evaluation (2)
•
Enhancements to CMAQ to track WB and other dust
emissions separately
•
Run CMAQ w/ and w/o WB Dust emissions
•
Evaluate CMAQ model results with and with out WB dust
emissions
Model Performance Evaluation (2)
January, 2002
Model Performance Evaluation (2)
July, 2002
Model Performance Evaluation (2)
Model Performance Evaluation (3)
• Refined model performance evaluation using results of
Etyemezian, et al.
• For events characterized as wind blown dust events,
determine whether dust model predicts impacts
• Model and measurements agree …
– Analyze for trends
– Systematic over- or under-prediction ?
• Model and measurements disagree …
– Wind speed errors ?
– Landuse type mischaracterization ?
– Other ?
 Analyses on-going based on DRI project results
Next Steps
•
•
Complete Model Performance Evaluation (end of year)
Address deficiencies in Ag data for the Eastern States
–
–
Assume constant crop canopy %
Develop generic crop calendars, crop canopy % , etc.
–
Collect detailed Ag data from Eastern States
•
Re-run model w/ latest MM5 data
•
Make use of 12-km resolution MM5 data
•
Apply to small region for verification of methods,
assumptions
•
Apply transport fraction by county for air quality model
applications