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Presented by
Gerard E. MansellENVIRON International CorporationNovato, California
February 25, 2004
DETERMINING FUGITIVE DUST EMISSIONS FROM WIND EROSION
Project Team
• Gerard Mansell; ENVIRON
• Martinus Wolf, Paula Fields; ERG
• Jack Gillies; DRI
• Mohammad Omary; CE-CERT, UCR
• Bill Barnard; MACTEC Engr. & Consulting
• Michael Uhl; DAQM, Clark County, NV
Outline
• Project Background & Overview
• Literature Review
• Estimation Methodology
• Agricultural Considerations
• Data Sources
• Summary of Assumptions
• Program Implementation
• Results
• Sensitivity Simulations
• Recommendations
Background and Overview of Project
• Develop General Methodology to Facilitate Future Revisions and Control Strategy Development
• Develop Integrated SMOKE Processing Modules for PM10 and PM2.5 Emissions Modeling
• Develop PM10 and PM2.5 Emission Inventory Applicable to the Western Region
Overview of Technical Approach
• Categorize Vacant Land Types
• Identify Wind Tunnel Emission Factors
• Develop Meteorological Data
• Develop Threshold Wind Velocities, Wind Events, Precipitation Events
• Apply Emission Factors to Vacant Land Categories
Literature Review
• Portable field wind tunnels have been used to investigate particle entrainment thresholds, emission potentials, and transport of sediment by wind.
• Major contributions of information on: – thresholds from Gillette et al. (1980), Gillette et al.
(1982), Gillette (1988), Nickling and Gillies (1989);– emission fluxes from Nickling and Gillies (1989),
James et al. (2001), Columbia Plateau PM10 Program (CP3), Houser and Nickling (2001).
• Key information has also come from dust emission modeling (e.g., Alfaro et al., 2003) and desert soil characterization studies (e.g., Chatenet et al., 1996).
Wind Tunnel Study Results: Thresholds
u*t = 0.31e7.44x(Zo)
R2 = 0.60
u*t = 0.30e7.22x(Zo)
0
0.5
1
1.5
2
2.5
3
0.00001 0.0001 0.001 0.01 0.1 1
zo (cm)
u *t (
m s
-1)
wind tunnel data Marticorena et al. 1997Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)
Comparison between modeled relationship of threshold friction velocity and aerodynamic roughness length and wind tunnel data.
*
*(Gillette et al., 1980; Gillette et al., 1982; Gillette, 1988; Nickling & Gillies, 1989)
Wind Tunnel Study Results: Emissions
FFS
F = 2.45x10-6 (u*)3.97
FS
F = 9.33x10-7 (u*)2.44
MS
F = 1.243x10-7(u*)2.64
CS
F = 1.24x10-7 (u*)3.44
0.000000001
0.00000001
0.0000001
0.000001
0.00001
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Friction Velocity (m s-1)
Em
issi
on F
lux
(F, g
cm
-2 s
-1)
FSS
FS
MSCS
Power (FSS)
Power (FS)
Power (MS)Power (CS)
The emission flux as a function of friction velocity predicted by the Alfaro and Gomes (2001) model constrained by the four soil geometric mean diameter classes of Alfaro et al. (2003).
Wind Tunnel Study Results: Emissions as a function of texture
Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)
Clayey silt
Silty clay
sand silt
clay
Clay
Sandy siltyclay
Clayey sandysilt
Clayey siltysand
Sand
y cl
ay
Cla
yey
sand
50%
7525
7525
10
10
10
Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)
Clayey silt
Silty clay
sand silt
clay
Clay
Sandy siltyclay
Clayey sandysilt
Clayey siltysand
Sand
y cl
ay
Cla
yey
sand
50%
7525
7525
10
10
10
Sand(CS) Silty sand (MS) Sandy silt (FS) Silt (FSS)
Clayey silt
Silty clay
sand silt
clay
Clay
Sandy siltyclay
Clayey sandysilt
Clayey siltysand
Sand
y cl
ay
Cla
yey
sand
50%
7525
7525
10
10
10
Relations between the soil types deduced from aggregate size distributions of various desert soils and soil textural categories (Chatenet et al. 1996). The “gray” highlighted textural classes indicate the 4 sediment types; the arrows indicate the pathways linking these types to the other textures. These can be linked to the North American soil texture triangle.
Wind Tunnel Study Results: Emissions
1E-10
1E-09
1E-08
1E-07
1E-06
10 100u* (cm/s)
Fv
(g
/cm
2/s
)
FS
Casa Grande
Mesa (Salt River)
Hayden
Ajo
Yuma (ag)
Glendale
Tucson (Sta Cruz)
Tucson (const.site)Mesa (ag)
CS
Yuma (scrubdeser t)Yuma (distdeser t)
Comparison between model relationship for FS and CS sizes and the wind tunnel data of Nickling and Gillies (1989). Ten (out of 13) sites have a dust production potential similar to the FS model and one site (Mesa agricultural) is closely aligned with the CS model (after Alfaro et al., 2003).
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
20 - 24.9 25 - 29.9 30 - 34.9 35 - 39.9 40 - 44.9 45 - 49.9 50 - 54.9
Emission Rates by Soil Group for Stable Soils E
mis
sio
n F
ac
tor
(to
n/a
cre
/ho
ur)
Soil Group 1
Soil Group 2
Soil Group 3
Soil Group 4
Soil Group 5
10-m Wind Speed (mph)
Emission Rates by Soil Group for Unstable Soils
0
0.005
0.01
0.015
0.02
0.025
0.03
20 - 24.9 25 - 29.9 30 - 34.9 35 - 39.9 40 - 44.9 45 - 49.9 50 - 54.9
10-m Wind Speed (mph)
Em
iss
ion
Fa
cto
r (t
on
/ac
re/h
ou
r)
Soil Group 1
Soil Group 2
Soil Group 3
Soil Group 4
Soil Group 5
Agricultural Considerations
• Non-climatic factors significantly decrease soil loss from agricultural lands
• Similar approach to CARB, 1997
• Five “adjustment” factors simulate these effects:
– Bare soil within fields
– Bare borders surrounding fields
– Long-term irrigation
– Crop canopy cover
– Post-harvest vegetative cover (residue)
Agricultural Adjustment Factor Development
• New regional data collected for WRAP project:
– Crop calendars with growth curves from Revised Universal Soil Loss Equation (RUSLE2) model
– Residues remaining after harvest due to conservation tillage practices from Purdue’s Conservation Technology Information Center (CTIC)
– Irrigation events from crop budget databases
• Factors applied by county/crop type, crop management zones (CMZs)
Data Sources
• Land Use/Land Cover (LULC)
– Biogenic Emission Landcover Database (BELD3)
– North American Land Cover Characteristics
– National Land Cover Database (NLCD)
• Soils Characteristics
– State Soil Geographic Database (STATSGO)
– Soil Landscape of Canada (SLC_V2)
– International Soil Reference and Information Centre
• Meteorological Data
– 1996 MM5 36-km (Wind Velocity, Precipitation, Snow/Ice, Soil Temperature)
Land Use/Land Cover Data
• BELD3 LULC Data
Summary Total Area (Acres) % % excluding waterUrban 6,781,771 0.26% 0.34%Agriculture 531,231,552 20.54% 26.35%Shrub/grassland 720,022,464 27.84% 35.71%Forest 741,902,639 28.69% 36.80%Barren 5,801,931 0.22% 0.29%Wetlands 681,383 0.03% 0.03%Tundra 9,096,875 0.35% 0.45%Snow&Ice 603,210 0.02% 0.03%Water 569,829,853 22.04%Total 2,585,951,680 100.00%
Total excluding water 2,016,121,827 100.00%
Meteorological Data
• 1996 MM5
– 1996 Annual, hourly, gridded meteorology
– 36-km horizontal resolution
– 10-m wind speeds
– Precipitation rates
– Snow/ice cover flag
– Soil temperature
Data Compilation for Land Use and Soil Types
• Land use and soil texture aggregated to 12-km resolution
• Major land use categories
– Urban
– Agricultural
– Shrub and grasslands
– Forest
– Barren and Desert
• Land use fractions from 1-km data retained as percentages
• Dominate soil texture at 12-km resolution
Soil Texture Categorization
• Standard soil types mapped to 5 major types for dust calculations
– Silty Sand and Clay
– Sandy Silt
– Loam
– Sand
– Silt
STATSGO Soil Texture
Soil Texture Code
Soil Group Code
No Data 0 0Sand 1 4Loamy Sand 2 4Sandy Loam 3 2Silt Loam 4 1Silt 5 5Loam 6 3Sandy Clay Loam 7 2
Silty Clay Loam 8 5Clay Loam 9 3Sandy Clay 10 2
Silty Clay 11 5Clay 12 1
Vacant Land Stability
• Windblown dust emissions affected by soil stability• Stability determination based on land types• Urban lands may be stable or unstable
LULC Category Stability Urban Stable/Unstable (see below) Agricultural -- Shrubland Stable Grassland Stable Mixed Shrub/Grassland Stable Forest Stable Barren Unstable Desert Unstable
Reservoir Characteristics
• Reservoirs characteristics based on stability
– Stable = limited
– Unstable = unlimited
• Stable reservoirs are depleted within 1 hour
• Unstable reservoirs are depleted within 10 hours
• Reservoirs require 24 hours to recharge
Precipitation and Freeze Events
• No dust emissions during rain events
• Rainfall from MM5 at 36-km resolution
• No dust emissions if snow/ice cover present
• Dust emissions re-initiated:
– 72 hours after rain
– 72 hours after snow/ice meltdown
– 12 hours after thaw
Vegetative Cover Adjustments
• Vegetation cover reduces dust emissions
• Methodology developed for bare soil
• Emissions reduction factors developed from White (2000)
• Vegetation density based on land use types
Vegetative Cover Adjustments
LULC Category Vegetation Cover % Reduction Factor
Urban 55(stable)/0(unstable) 0.07/1.0
Shrubland 11 0.70
Grassland 23 0.19
Mixed Shrub/Grassland
17 0.45
Forest 55 0.07
Barren 0 1.0
Desert 0 1.0
Summary of Assumptions
• Threshold velocity = 20 mph
• Vacant land stability
• Urban lands
• Dust reservoirs
• Reservoir depletion and recharge times
• Precipitation, snow and freeze events
• Vegetation density
Program Implementation
• Daily/Hourly Meteorological Data
• State/County, Crop Management Zone, and Soil Type, For Each 12km Cell.
• Area fractions For Each 12km Cell, and Land Use For Each Area Fraction.
• Agricultural Adjustment Data
• Emission Rates by Soil and Wind Speed Categories
Summary of Annual PM10 Emissions
Annual PM10 by Landuse
urbanAg_nonadjustAgdesert,barren landforestshrubgrasslandshrub/grassland
PM10 Dust Emissions by Month
Total Dust Emission
00
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1 2 3 4 5 6 7 8 9 10 11 12
Month
(ton
s)
Monthly PM10 Emissions by Landuse Type
Total PM10 (tons)
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
tons
water/weland
shrub/grassland
grassland
shrub
forest
desert,barren land
Ag
Ag_nonadjust
urban
Monthly PM10 Emissions by Crop Type
Monthly PM10 by crops
0
50000
100000
150000
200000
250000
300000
350000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Month
PM
10(t
ons)
Wheat
Tobacco
Soybeans
Sorghum
Rye
Rice
Potatoes
Peanuts
Pasture
Oats
Misc
Hay
Grass
Cotton
Corn
Barley
Alfal
Annual PM10 and PM2.5
Sensitivity Simulation
• Evaluate impact of threshold velocity and reservoir assumptions
• Extend emissions factor relations to lower wind speeds
– 15 mph threshold velocity
• Relax reservoir recharge assumptions:
– 12 hours between wind events
– 36 hours after rain events
– 36 hours after snow/ice meltdown
– 6 hours after thaw
Comparison of PM10 Dust Emissions by Month
Monthly PM10 Fugitive Dust Emissions
0
2000000
4000000
6000000
8000000
10000000
12000000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Ton
s
PM10 (Original)
PM10 (Sensitivity)
Annual PM10
Application to Imperial Valley, CA
• Applied to Imperial County, CA
• 2-km modeling domain
• CALMET Meteorology – 15 mph threshold
• BELD3 and Dept. of Water Resources (DWR) LULC
• Reservoir recharge assumptions:
– 12 hours between wind events
– 36 hours after rain events
– 36 hours after snow/ice meltdown
– 6 hours after thaw
Monthly PM10 Emissions by DWR Landuse Type
Monthly Dust Summary
0.0
10000.0
20000.0
30000.0
40000.0
50000.0
60000.0
70000.0
80000.0
90000.0
Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec
Month
PM
10
(to
ns)
URBAN GRASS FOREST
BARREN NonAdj_Ag Ag w/Adj
Monthly PM10 Emissions by BELD3 Landuse Type
Monthly Dust Summary
0.0
2000.0
4000.0
6000.0
8000.0
10000.0
12000.0
14000.0
Jan. Feb Mar Apr May June July Aug Sept Oct Nov Dec
Month
PM
10 (
tons
)
URBAN GRASS FOREST BARREN NonAdj_AG AGw/adj
Annual PM10
Air Quality Modeling
WRAP 96 Fugitive Dust vs. Basei(IMPROVE evaluation)
• 1st half of 1996: date 1-19 and 90-109
• 2nd half of 1996: date 180-199 and 270-289
CM: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
SOIL: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
SO4: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
NO3: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
OC: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
EC: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
PM25: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
PM10: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
BEXT: Fugitive Dust vs. Basei
1st half of 1996 2nd half of 1996
Recommendations
• Methodology review and refinement
• Current, detailed data to characterize vacant lands
• Methodology validation with small-scale, high resolution domain
• Identification and evaluation of additional wind tunnel studies
• Application to other domains, years
