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

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 PM 10 Program (CP 3 ), 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 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)

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. 0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 20 - 24.925 - 29.930 - 34.935 - 39.940 - 44.945 - 49.950 - 54.9 Emission Rates by Soil Group for Stable Soils Emission Factor (ton/acre/hour) Soil Group 1 Soil Group 2 Soil Group 3 Soil Group 4 Soil Group 5 10-m Wind Speed (mph)

12. Emission Rates by Soil Group for Unstable Soils 0 0.005 0.01 0.015 0.02 0.025 0.03 20 - 24.925 - 29.930 - 34.935 - 39.940 - 44.945 - 49.950 - 54.9 10-m Wind Speed (mph) Emission Factor (ton/acre/hour) Soil Group 1 Soil Group 2 Soil Group 3 Soil Group 4 Soil Group 5

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 Purdue’s 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

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 Data00 Sand14 Loamy Sand24 Sandy Loam32 Silt Loam41 Silt55 Loam63 Sandy Clay Loam72 Silty Clay Loam85 Clay Loam93 Sandy Clay102 Silty Clay115 Clay121

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 LULC CategoryVegetation Cover %Reduction Factor Urban55(stable)/0(unstable)0.07/1.0 Shrubland110.70 Grassland230.19 Mixed Shrub/Grassland 170.45 Forest550.07 Barren01.0 Desert01.0

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

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) 1 st half of 1996: date 1-19 and 90-109 2 nd half of 1996: date 180-199 and 270-289

44. CM: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

45. SOIL: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

46. SO4: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

47. NO3: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

48. OC: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

49. EC: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

50. PM25: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

51. PM10: Fugitive Dust vs. Basei 1st half of 19962nd half of 1996

52. BEXT: Fugitive Dust vs. Basei 1st half of 19962nd 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