Gerard E. Mansell

1
DETERMINING FUGITIVE DUST EMISSIONS FROM WIND
EROSION
Presented by Gerard E. Mansell ENVIRON
International Corporation Novato,
California February 25, 2004
2
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

3
Outline

• Project Background Overview
• Literature Review
• Estimation Methodology
• Agricultural Considerations
• Data Sources
• Summary of Assumptions
• Program Implementation
• Results
• Sensitivity Simulations
• Recommendations

4
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

5
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

6
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).

7
Wind Tunnel Study Results Thresholds
(Gillette et al., 1980 Gillette et al., 1982
Gillette, 1988 Nickling Gillies, 1989)

Comparison between modeled relationship of
threshold friction velocity and aerodynamic
roughness length and wind tunnel data.
8
Wind Tunnel Study Results Emissions
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).
9
Wind Tunnel Study Results Emissions as a
function of texture
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.
10
Wind Tunnel Study Results Emissions
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).
11
Emission Rates by Soil Group for Stable Soils
0.035
0.03
0.025
Soil Group 1
Soil Group 2
0.02
Soil Group 3
Emission Factor (ton/acre/hour)
Soil Group 4
0.015
Soil Group 5
0.01
0.005
0
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)
12
Emission Rates by Soil Group for Unstable Soils
0.03
0.025
0.02
Soil Group 1
Soil Group 2
0.015
Soil Group 3
Emission Factor (ton/acre/hour)
Soil Group 4
Soil Group 5
0.01
0.005
0
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)
13
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)

14
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 Purdues
  Conservation Technology Information Center (CTIC)
• Irrigation events from crop budget databases
• Factors applied by county/crop type, crop
  management zones (CMZs)

15
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)

16
Land Use/Land Cover Data

• BELD3 LULC Data

17

18
(No Transcript)
19
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

20
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

21
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 0
Sand 1 4
Loamy Sand 2 4
Sandy Loam 3 2
Silt Loam 4 1
Silt 5 5
Loam 6 3
Sandy Clay Loam 7 2
Silty Clay Loam 8 5
Clay Loam 9 3
Sandy Clay 10 2
Silty Clay 11 5
Clay 12 1
22
Vacant Land Stability

• Windblown dust emissions affected by soil
  stability
• Stability determination based on land types
• Urban lands may be stable or unstable

23
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

24
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

25
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

26
Vegetative Cover Adjustments
 
27
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

28
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

29
Summary of Annual PM10 Emissions
30
PM10 Dust Emissions by Month
31
Monthly PM10 Emissions by Landuse Type
32
Monthly PM10 Emissions by Crop Type
33
Annual PM10 and PM2.5
34
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

35
Comparison of PM10 Dust Emissions by Month
36
Annual PM10

37
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

38

39

40
Monthly PM10 Emissions by DWR Landuse Type
41
Monthly PM10 Emissions by BELD3 Landuse Type
42
Annual PM10
43
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

44

CM Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
45

SOIL Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
46

SO4 Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
47

NO3 Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
48

OC Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
49

EC Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
50

PM25 Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
51

PM10 Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
52

BEXT Fugitive Dust vs. Basei
1st half of 1996
2nd half of 1996
53
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