International Corporation Air Sciences

Final Report 

Fugitive Wind Blown Dust Emissions 

and Model Performance Evaluation 

Phase II 

Prepared for 

Western Governors’ Association 

1515 Cleveland Place, Suite 200 

Denver, CO 80202 

Prepared by 

Gerard E. Mansell 

Steven Lau 

James Russell 

ENVIRON International Corporation 

101 Rowland Way 

Novato, CA 94945 

Mohammad Omary 

University of California at Riverside 

College of Engineering – Center for Environmental 

Research and Technology 

Riverside, CA 92507 

5 May 2006 

 101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700 
May 2006 

i 

TABLE OF CONTENTS 

Page 

PREFACE ................................................................................................................................ iv 

1. INTRODUCTION................................................................................................................ 1-1 

2. SUMMARY OF PHASE I METHODOLOGY AND LITERATURE REVIEW.......... 2-1 

3. WIND BLOWN DUST EMISSION ESTIMATION METHODOLOGY ...................... 3-1 

4. MODEL RESULTS FOR 2002 .......................................................................................... 4-1 

5. MODEL PERFORMANCE EVALUATION ................................................................... 5-1 

6. SUMMARY .......................................................................................................................... 6-1 

7. REFERENCES .................................................................................................................... 7-1 

APPENDIX 

Appendix A: Results of Preliminary Model Sensitivity Simulations 

Appendix B: Scatter Plots of Predicted Windblown Dust PM10 

 Emissions vs IMPROVE Ambient Concentrations 

TABLES 

Table 2-1. Threshold friction velocities for typical surface types 

 calculated from available data and as reported in the literature........................... 2-5 

Table 2-2. Typical surface aerodynamic roughness lengths calculated 

 from available data and as reported in the literature............................................ 2-6 

Table 2-3. Inter-comparison of wind blown dust models ..................................................... 2-9 

Table 3-1. Number of days after precipitation event to re-initiate wind 

 erosion for rainfall amounts (constant) exceeding 2 inches................................. 3-3 

Table 3-2. Number of days after precipitation event to re-initiate wind 

 erosion for rainfall amounts (constant) less than or equal to 2 inches................. 3-3 
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ii 

Table 3-3. NLCD Land Cover Classification Codes............................................................. 3-5 

Table 3-4. NALC 2000 LULC classification scheme. .......................................................... 3-7 

Table 3-5. STATSGO soil texture and soil group codes..................................................... 3-10 

Table 3-6. Dust code assignments by LULC ...................................................................... 3-11 

Table 3-7. Surface characteristics by dust code and land use category. ............................. 3-13 

Table 3-8. Comparison of 1992 and 2000 LULC data by dust code................................... 3-13 

Table 3-9. Summary of 2000 NALC LULC by dust code percentages by state................. 3-13 

Table 3-10. Summary of 2000 NALC LULC by dust code acreages by state...................... 3-15 

Table 3-11. Transport fractions as a function of landuse...................................................... 3-23 

Table 4-1. Summary of 36-km WB dust PM10 emission. .................................................. 4-17 

Table 4-2. Summary of 12-km WB dust PM10 emissions for the WRAP States............... 4-17 

Table 4-3. Monthly WB dust PM10 emissions by state and dust category – 12km 

 WRAP States. .................................................................................................... 4-19 

FIGURES 

Figure 1-1. WRAP 36-km regional modeling domain ........................................................... 1-2 

Figure 1-2. WRAP 12-km regional modeling domain. .......................................................... 1-3 

Figure 2-1. Comparison between the Marticorena et al. (1997) modeled 

 relationship of threshold friction velocity and aerodynamic 

 roughness length and wind tunnel data from Gillette et al. 

 (1980, 1982), Gillette (1988) and Nickling and Gillies (1989). .......................... 2-4 

Figure 2-2. 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).............................. 2-7 

Figure 2-3. Comparison between the Alfaro et al. (2003) model relationship 

 for FS and CS sizes and the wind tunnel flux 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).......................... 2-8 

Figure 3-1. NLCD for the Conterminous US ......................................................................... 3-5 

Figure 3-2a. North American Landuse/Landcover for 2000.................................................... 3-8 

Figure 3-2b. Legend for 2000 North American Landuse/Landcover....................................... 3-9 

Figure 3-3. STATSGO Soils Database soils texture class for conterminous U.S. ............... 3-10 

Figure 3-4. Annual average wind speeds for the WRAP 36-km regional modeling domain3-16 

Figure 3-5. Monthly average wind speeds for the WRAP 36-km regional modeling domain.3-17 

Figure 3-6. Annual average wind speeds for the WRAP 12-km modeling domain............. 3-19 

Figure 3-7. Monthly average wind speeds for the WRAP 12-km modeling domain........... 3-19 

Figure 4-1. 2002 Annual PM120 WB Dust PM10 emissions by state ................................... 4-3 

Figure 4-2. Normalized 2002 Annual PM120 WB Dust PM10 emissions 

 by state ................................................................................................................. 4-4 

Figure 4-3. 2002 Annual WB dust PM10 emissions for WRAP states.................................. 4-5 

Figure 4-4. Normalized 2002 Annual WB dust PM10 emissions for WRAP 

 states..................................................................................................................... 4-5 
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iii 

Figure 4-5. 2002 Annual 36-km PM10 emissions by dust category – no 

 TF applied ............................................................................................................ 4-6 

Figure 4-6. 2002 Annual 36-km PM10 emissions by dust category – with 

 TF applied ............................................................................................................ 4-7 

Figure 4-7. 2002 annual 12-km PM10 emissions by dust category – with TF applied.......... 4-8 

Figure 4-8. Normalized 2002 annual 12-km PM10 emissions by dust 

 category – with TF applied .................................................................................. 4-8 

Figure 4-9. Comparison of total 2002 annual PM10 emissions by dust category 

 for the WRAP states............................................................................................. 4-9 

Figure 4-10. Comparison of monthly PM10 dust emissions. 

 ............................................................................................................................ 4-10 

Figure 4-11. PM10 WB dust emissions by state for the 36-km WRAP domain.................... 4-11 

Figure 4-12. PM10 WB dust emissions by state for the WRAP states................................... 4-11 

Figure 4-13. Comparison of PM10 WB dust emissions by state for the WRAP 

 states – 12-km model run................................................................................... 4-12 

Figure 4-14. Comparison of PM WB dust emissions by state for the WRAP 

 states – 12-km model run................................................................................... 4-13 

Figure 4-15. 2002 annual 36-km PM10 emissions by dust category and state. ..................... 4-13 

Figure 4-16. 2002 annual 12-km PM10 emissions by dust category for WRAP states ......... 4-14 

Figure 4-17. 2002 annual 12-km normalized PM10 emissions by dust 

 category for WRAP states.................................................................................. 4-14 

Figure 4-18. Comparison of 2002 annual 12-km PM10 normalized agricultural 

 emissions for WRAP states................................................................................ 4-15 

Figure 4-19. Comparison of 2002 annual 12-km PM10 normalized grassland 

 emissions for WRAP states................................................................................ 4-15 

Figure 4-20. Comparison of 2002 annual 12-km PM10 normalized shrubland 

 emissions for WRAP states................................................................................ 4-16 

Figure 4-21. Comparison of 2002 annual 12-km PM10 normalized barren land 

 emissions for WRAP states................................................................................ 4-16 

Figure 4-22. Comparison of total 2002 annual PM10 emissions for WRAP states ............... 4-18 

Figure 4-23. Monthly PM10 WB dust emissions by source category across all 

 WRAP states. ..................................................................................................... 4-18 

Figure 4-24. Spatial distribution of 2002 annual gridded PM10 WB dust emissions. ........... 4-23 

Figure 4-25. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from agricultural lands....................................................................................... 4-23 

Figure 4-26. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from grasslands. ................................................................................................. 4-24 

Figure 4-27. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from shrublands. ................................................................................................ 4-24 

Figure 4-28. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from barren lands. .............................................................................................. 4-25 

Figure 4-29. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 for the WRAP 12-km domain............................................................................ 4-25 

Figure 4-30. Spatial distribution of 2002 monthly gridded PM10 WB dust emissions 

 for the WRAP 12-km domain............................................................................ 4-26 

Figure 4-31. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from agricultural lands for the WRAP 12-km domain. ..................................... 4-28 

Figure 4-32. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from grasslands for the WRAP 12-km domain.................................................. 4-28 
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Figure 4-33. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from shrublands for the WRAP 12-km domain................................................. 4-29 

Figure 4-34. Spatial distribution of 2002 annual gridded PM10 WB dust emissions 

 from barren lands for the WRAP 12-km domain............................................... 4-29 

Figure 4-35. Spatial distribution of 2002 annual county-level PM10 WB dust 

 emissions for the WRAP 12-km domain. .......................................................... 4-30 

Figure 4-36. Spatial distribution of 2002 annual county-level PM10 WB dust 

 emissions from agricultural lands for the WRAP 12-km domain...................... 4-30 

Figure 4-37. Spatial distribution of 2002 annual county-level PM10 WB dust 

 emissions from grasslands for the WRAP 12-km domain................................. 4-31 

Figure 4-38. Spatial distribution of 2002 annual county-level PM10 WB dust 

 emissions from shrublands for the WRAP 12-km domain. ............................... 4-31 

Figure 4-39. Spatial distribution of 2002 annual county-level PM10 WB dust 

 emissions from barren lands for the WRAP 12-km domain.............................. 4-32 

Figure 4-40. Spatial distribution of 2002 annual normalized county-level PM10 WB 

 dust emissions for the WRAP 12-km domain.................................................... 4-33 

Figure 4-41. Spatial distribution of 2002 annual normalized county-level PM10 WB 

 dust emissions from agricultural lands for the WRAP 12-km domain.............. 4-33 

Figure 4-42. Spatial distribution of 2002 annual normalized county-level PM10 WB 

 dust emissions from grasslands for the WRAP 12-km domain. ........................ 4-34 

Figure 4-43. Spatial distribution of 2002 annual normalized county-level PM10 WB 

 dust emissions from shrublands for the WRAP 12-km domain. ....................... 4-34 

Figure 4-44. Spatial distribution of 2002 annual normalized county-level PM10 WB 

 dust emissions from barren lands for the WRAP 12-km domain. ..................... 4-35 

Figure 5-1. 2002 Annual Coarse Mass from IMPROVE monitoring network....................... 5-3 

Figure 5-2. 2003 Annual Coarse Mass from IMPROVE monitoring network....................... 5-3 

Figure 5-3. 2004 Annual Coarse Mass from IMPROVE monitoring network....................... 5-4 

Figure 5-4. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Saguaro West, AZ IMPROVE monitor. .......................... 5-6 

Figure 5-5. Predicted PM10 dust emissions vs. observed Fine Soil concentrations 

 for the Saguaro West, AZ IMPROVE monitor.................................................... 5-7 

Figure 5-6. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Agua Tibia IMPROVE monitor.......................................5-8 

Figure 5-7. Predicted PM10 dust emissions vs. observed Fine Soil 

 concentrations for the Agua Tibia IMPROVE monitor....................................... 5-9 

Figure 5-8. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Medicine Lake IMPROVE monitor............................... 5-10 

Figure 5-9. Predicted PM10 dust emissions vs. observed Fine Soil 

 concentrations for the Medicine Lake IMPROVE monitor............................... 5-11 

Figure 5-10. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Starkey IMPROVE monitor........................................... 5-12 

Figure 5-11. Predicted PM10 dust emissions vs. observed Fine Soil 

 concentrations for the Starkey IMPROVE monitor........................................... 5-13 

Figure 5-12. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Theodore Roosevelt IMPROVE monitor....................... 5-14 

Figure 5-13. Predicted PM10 dust emissions vs. observed Fine Soil concentrations 

 for the Theodore Roosevelt IMPROVE monitor............................................... 5-15 
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Figure 5-14. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Lostwood IMPROVE monitor....................................... 5-16 

Figure 5-15. Predicted PM10 dust emissions vs. observed Fine Soil concentrations 

 for the Lostwood IMPROVE monitor. ............................................................. 5-17 

Figure 5-16. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Salt Creek IMPROVE monitor. ..................................... 5-18 

Figure 5-17. Predicted PM10 dust emissions vs. observed Fine Soil 

 concentrations for the Salt Creek IMPROVE monitor. ..................................... 5-19 

Figure 5-18. Predicted PM10 dust emissions vs. observed Coarse Mass 

 concentrations for the Bridger Wilderness IMPROVE monitor........................ 5-20 

Figure 5-19. Predicted PM10 dust emissions vs. observed Fine Soil 

 concentrations for the Bridger Wilderness IMPROVE monitor........................ 5-21 

Figure 5-20. Spatial distribution of differences in CMAQ predicted monthly 

 Fine Soil (left) and Coarse Mass (right) with and 

 without windblown dust..................................................................................... 5-23 

Figure 5-21. Spatial distribution of differences in CMAQ predicted monthly 

 Deciview (left) and Extinction (right) with and without 

 windblown dust.................................................................................................. 5-24 

Figure 5-22. Scatter plots of CMAQ predicted and IMPROVE observed 

 Fine Soil for April-May 2002 for the WRAP states only. ................................. 5-25 

Figure 5-23. Scatter plots of CMAQ predicted and IMPROVE observed 

 CM for April-May 2002 for the WRAP states only. ......................................... 5-25 
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PREFACE 

Regulatory Framework for Tribal Visibility Implementation Plans 

The Regional Haze Rule explicitly recognizes the authority of tribes to implement the provisions 

of the Rule, in accordance with principles of Federal Indian law, and as provided by the Clean 

Air Act §301(d) and the Tribal Authority Rule (TAR) (40 CFR §§49.1– .11). Those provisions 

create the following framework: 

1. Absent special circumstances, reservation lands are not subject to state jurisdiction. 

2. Federally recognized tribes may apply for and receive delegation of federal authority to 

implement CAA programs, including visibility regulation, or "reasonably severable" elements of 

such programs (40 CFR §§49.3, 49.7). The mechanism for this delegation is a Tribal 

Implementation Plan (TIP). A reasonably severable element is one that is not integrally related 

to program elements that are not included in the plan submittal, and is consistent with applicable 

statutory and regulatory requirements. 

3. The Regional Haze Rule expressly provides that tribal visibility programs are “not dependent 

on the strategies selected by the state or states in which the tribe is located” (64. Fed. Reg. 

35756), and that the authority to implement §309 TIPs extends to all tribes within the GCVTC 

region (40 CFR §51.309(d)(12). 

4. The EPA has indicated that under the TAR tribes are not required to submit §309 TIPs by the 

end of 2003; rather they may choose to opt-in to §309 programs at a later date (67 Fed. Reg. 

30439). 

5. Where a tribe does not seek delegation through a TIP, EPA, as necessary and appropriate, will 

promulgate a Federal Implementation Plan (FIP) within reasonable timeframes to protect air 

quality in Indian country (40 CFR §49.11). EPA is committed to consulting with tribes on a 

government to government basis in developing tribe-specific or generally applicable TIPs where 

necessary (See, e.g., 63 Fed. Reg.7263-64). 

 

It is our hope that the findings and recommendations of this report will prove useful to tribes, 

whether they choose to submit full or partial 308 or 309 TIPs, or work with EPA to develop 

FIPs. The amount of modification necessary will vary considerably from tribe to tribe. The 

authors have striven to ensure that all references to tribes in the document are consistent with 

principles of tribal sovereignty and autonomy as reflected in the above framework. Any 

inconsistency with this framework is strictly inadvertent and not an attempt to impose 

requirements on tribes which are not present under existing law. 

Tribes, along with states and federal agencies, are full partners in the WRAP, having equal 

representation on the WRAP Board as states. Whether Board members or not, it must be 

remembered that all tribes are governments, as distinguished from the “stakeholders” (private 

interest) which participate on Forums and Committees but are not eligible for the Board. 

Despite this equality of representation on the Board, tribes are very differently situated than 

states. There are over four hundred federally recognized tribes in the WRAP region, including 
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Alaska. The sheer number of tribes makes full participation impossible. Moreover, many tribes 

are faced with pressing environmental, economic, and social issues, and do not have the 

resources to participate in an effort such as the WRAP, however important its goals may be. 

These factors necessarily limit the level of tribal input into and endorsement of WRAP products. 

The tribal participants in the WRAP, including Board members Forum and Committee members 

and co-chairs, make their best effort to ensure that WRAP products are in the best interest of the 

tribes, the environment, and the public. One interest is to ensure that WRAP policies, as 

implemented by states and tribes, will not constrain the future options of tribes who are not 

involved in the WRAP. With these considerations and limitations in mind, the tribal participants 

have joined the state, federal, and private stakeholder interests in approving this report as a 

consensus document. 
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1. INTRODUCTION 

The Western Regional Air Partnership (WRAP) is engaged in an effort to implement Section 309 

of the Regional Haze Rule. This effort includes the compilation for the regional emission 

inventory to be used in air quality models as the basis for tracking progress in achieving the 

visibility goals under this rule. The WRAP area source emission inventory currently does not 

include windblown fugitive dust because of strong concern that the methodology previously used 

to calculate these particulate emissions yielded inaccurate and misleading results. 

The Phase I WRAP windblown fugitive dust project methodology and results have been 

documented in a project Final Report (ENVIRON, 2004) and Technical Memoranda (Mansell, 

2003a; 2003b). The results of the initial model runs and subsequent sensitivity simulations 

demonstrated a need to revise and/or update various assumptions associated with the 

development of the emission inventory. Revised estimation methodologies and algorithms were 

evaluated in order to address various shortcomings and limitations of the previous version of the 

model. Many of the assumptions employed in the Phase I methodology are related to a lack of 

detail in the underlying data used to characterize vacant land types and soil conditions. In 

addition, the Phase I methodology relied on arbitrarily assigned threshold friction velocities and 

dust reservoir characteristics. 

Various approaches for refining and enhancing the existing version of the windblown dust model 

were presented and discussed in Mansell et al., 2004. These refinements ware primarily focused 

on improving the determination of surface friction velocities and threshold friction velocities, as 

well as the calculation of dust emission fluxes. The characterization of the disturbance level of 

vacant land parcels was also considered. Land use datasets to more accurately characterize 

vacant lands were identified for use in the revised dust emission model developed under Phase II 

of the Wind Blown Dust project. In addition, a number of studies were identified for review. 

Algorithms and methodologies used in these studies were reviewed and incorporated, as 

appropriate, in the revised wind blown dust model. 

The Draft Final Report (Mansell, et al., 2005) documented the methodologies, assumptions and 

data sources for the revised wind blown dust model as implemented in Phase II of the project. A 

summary of the Phase I estimation methodology, assumptions and shortcomings was also 

presented. Draft results for calendar year 2002 were presented and discussed. 

Based on comments received regarding the daft inventory, a number of revisions were 

implemented for the final windblown dust inventory presented here. Revisions included in the 

final dust inventory include incorporation of more recent (calendar year 2000) 

Landuse/Landcover data, adjustments to the fine fraction of PM dust, and the incorporation of 

the latest dust transport fractions developed by the EPA. 

The dust model was applied with these revisions for the WRAP regional modeling domains at 

36- and 12-km resolution shown in Figures 1-1 and 1-2, respectively. An initial model 

performance evaluation, as detailed in Mansell et al., 2004, is also presented. 
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-2736 -2304 -1872 -1440 -1008 -576 -144 288 720 1152 1584 2016 2448

-2088

-1872

-1656

-1440

-1224

-1008

-792

-576

-360

-144

72

288

504

720

936

1152

1368

1584

1800

Figure 1-1. WRAP 36-km regional modeling domain. 
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-2376 -2160 -1944 -1728 -1512 -1296 -1080 -864 -648 -432 -216 0

-936

-828

-720

-612

-504

-396

-288

-180

-72

36

144

252

360

468

576

684

792

900

1008

1116

1224

Figure 1-2. WRAP 12-km regional modeling domain. 

The report is organized as follows: 

• Section 2 provides a summary of the Phase I estimation methodology, including data 

sources, assumptions and various shortcomings. A review of recently identified studies of 

relevance to the project is presented. 

• Section 3 presents the emission estimation methodology used in the development of the 

wind blown dust emission model employed in Phase II of this project. Data sources used 

for the 2002 dust emission inventory are summarized. 

• A summary and discussion of the results for 2002 are presented in Section 4. Various 

model sensitivity simulations were presented and discussed in the Draft Final Report. 

These also included herein as Appendix A. The latest revised model results are presented 

and discussed. Comparisons with the draft results are also presented. 

• Section 5 documents the results of the initial model performance evaluation conducted 

using the 2002 dust emission modeling results. 

• Section 6 provides an overall summary of the work performed during Phase II of the 

project. Recommendations for on-going improvements to the modeling approach and data 

sources are presented. 

• Section 7 includes references for this report. 
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2. SUMMARY OF PHASE I METHODOLOGY AND LITERATURE REVIEW 

The Phase I Wind Blown Dust model development and implementation is summarized in this 

section. The various assumptions and limitations associated with the emission estimation 

methodology are discussed. In addition, recent wind blown dust studies are identified and 

reviewed. Algorithms and methodologies employed in these studies are summarized. 

Summary of Phase I Methodology

The development of the Phase I Wind Blown Dust model and implementation, including various 

assumptions incorporated in the estimation methodology, has been documented previously 

(ENVIRON, 2004; 2003a; 2003b; Mansell, 2003a; 2003b). In summary, the method relies on 

the characterization of vacant land types and soil conditions, and numerous assumptions 

regarding dust reservoir characteristics. Wind erosion is initiated in the model based on an 

arbitrary wind speed assignment, independent of surface conditions. Emission factors, or dust 

fluxes, were derived from limited wind tunnel study results as a function of wind speed and soil 

texture. Adjustments were applied to the resulting emission rates based on vegetation density of 

vacant land parcels. Surface disturbance levels were based on land use types. In addition, 

adjustments were applied for agricultural lands based on non-climatic factors. Land use 

characterization was based on the Biogenic Emission Landuse Database (BELD3); soil texture 

was derived from the State Soil Geographic Database (STATSGO). 

The relative lack of detail in the data sets used for characterizing the physical conditions of land 

parcels and soils required a number of assumptions to be employed in the methodology. These 

assumptions were presented and discussed in detail by Mansell, 2003b and Mansell et al., 2004. 

The primary assumptions affecting the model results can be summarized as follows: 

• Threshold wind velocities: The threshold wind velocity is assumed to be 20 mph, 

independent of land use and soil texture. 

• Vacant land stability: The methodology developed relies on the specification of stability 

of vacant land parcels. The stability characteristics of land parcels are based solely on the 

land use type. 

• Dust Reservoirs: Reservoir properties are based on the stability characteristics of vacant 

land parcels and determine the duration of dust events. Limited reservoirs emit dust for a 

shorter duration of time than unlimited reservoirs. Assumptions are made concerning the 

amount of time a reservoir will emit wind blown dust. Also assumed are the reservoir 

recharge intervals. 

 

• Rain, Snow and Freeze Events: Assumptions are included which determine time 

intervals after which land parcels will emit dust following precipitation, snow and freeze 

events. These assumptions greatly impact the number of wind events treated in the 

methodology as well as the total dust emissions generated. 
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• Vegetation Density: The percentage of vegetative, or canopy, cover is determined by the 

general land use category of vacant land parcels. These percentages are constant for a given 

land type. Estimated emission factors, or emission rates, are attenuated based on the assumed 

canopy cover percentage. 

These various assumptions have a number of implications with respect to the estimation of 

fugitive dust from wind erosion. However, in many cases, the data necessary to address these 

issues on a regional scale domain are lacking. These issues and their implications were 

discussed in Mansell et al., 2004. The Phase II Wind Blown Dust project, documented here, 

seeks to address these assumptions and limitations and provide improvements to the overall 

estimation methodology and dust model implementation. 

Review of Recent Literature 

A number of windblown dust studies have recently been identified in the literature and are 

summarized with respect to the algorithms and physical parameters considered. Also discussed are 

the MacDougall, as implemented for the Phase I Dust Study, as well as the Project Team’s 

recommended approach. These studies are compared with each other and summarized with respect to 

improving the model application for the Phase II Dust Study. 

Recent Dust Emission Models

Draxler, et al, 2001 constructed a regional model for estimating PM10 from wind blown dust 

using the concept of threshold friction velocity which is dependent on the aerodynamic 

roughness length of the surface, Zo. PM10 vertical mass flux was calculated using the 

Marticorena, et al. 1997 algorithm. The flux is a function of wind velocity, threshold wind 

velocity, and a coefficient that relates the surface soil texture to PM10 emissions. Emissions 

start when the friction velocity is greater than the threshold friction velocity at that height. 

Friction velocity was calculated as a function of the aerodynamics roughness length. 

Threshold friction velocity was calculated as the ratio of the threshold velocity for smooth 

surface (U*ts) to feff , the efficient friction velocity. feff is defined by Marticorena and Bergamette 

(1995) as the ratio of friction velocity for a smooth surface to actual friction velocity. To 

determine this ration, the aerodynamic roughness length for a smooth surface (Zos), which is 

defined as the mean soil particle diameter (Dp) divided by 30 (Greeley and Iversen, 1985), and 

the actual aerodynamic roughness length. Soil samples were collected from the modeled area to 

determine Dp. Zos was calculated using the measured Dp. Using a mean value of 22 cm/s for 

U*ts, the actual threshold friction velocity was calculated for different values of aerodynamic 

roughness lengths. 

Using images of the area, a map of surface conditions and geomorphology, the u*t data from the 

Mojave Desert (Gillette, et al., 1980; 1982), the authors estimated the threshold friction velocity 

and surface roughness length for each surface classification. The coefficient that relates the 

surface soil texture to PM10 emissions was estimated using data for several soils from semi-arid 

areas (Gillette et al., 1997) showing the ratio of vertical flux of PM10 to total horizontal mass 

flux as function of friction velocity. 
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In their work, Draxler, et. al, 2001 considered a special case of wind blown dust. Land with 

vegetative cover has a relatively high threshold velocity and was not considered as an emission 

source. Two types of soil surface conditions were considered: loose undisturbed soil and 

disturbed soil. All the area was considered dry and therefore the effect of rain and snow was not 

considered. 

Zender, et al., 2003 developed a Dust Entrainment And Deposition (DEAD) model for studying 

dust related processes at both local and global scales. They considered three major factors that 

affect the dust flux: wind friction velocity, vegetation cover and surface soil moisture content. 

The approach developed by Marticorena and Bergamette (1995) was used to develop the model. 

For computing the threshold friction velocity they used a semi-empirical equation developed by 

Iversen and White (1982). In this equation, the friction velocity is a function of soil density and 

particle size, and air density and kinematic viscosity. The constant surface roughness length of 

0.01 cm was assumed for the entire domain. 

The change in threshold friction velocity was calculated using the equation developed by 

Marticorena and Bergamette (1995), similar to what as Draxler et al., 2001 used in their model. 

Zender used one global value of 0.0033 cm for the roughness length for a smooth surface. The 

effect of moisture content of the surface soil was considered in the Zender model. A threshold 

moisture content was calculated as a function of the mass fraction of clay as adopted from Fécan 

et al., 1999. Land covered by vegetation was not considered as dust a emitting source. 

The vertical mass flux was calculated as a function of the horizontal mass flux, a global tuning 

factor, a source erodibility factor, the fraction of bare soil, and the fraction of clay mass. The 

horizontal mass flux was calculated as a function of friction velocity and threshold friction 

velocity. 

Shao, 2001, developed an emission flux model as a function of the horizontal mass flux, 

threshold friction velocity and an empirical function of the diameters of saltating and emitted 

particles. In his work, Shao emphasized the micro-scale forces working on saltating particles 

and the impact of these particles on dust emissions. In this model a particle size distribution of 

the soil is required. 

ENVIRON/RMC

The ENVIRON/RMC model refers to the estimation methodology proposed by the Phase I Fugitive 

Wind Blown Dust project team and documented in ENIVRON, 2003a. Based on a review of wind 

tunnel studies it was noted that the two important components to characterize the dust emission 

process from an erodible surface are the threshold friction velocity that defines the inception of the 

emission process as a function of the wind speed and as influenced by the surface characteristics, and 

the strength of the emissions that follow the commencement of particle movement. The two critical 

factors affecting emission strength are the wind speed (wind friction velocity) that drives the saltation 

system, and the soil characteristics. 

Threshold Friction Velocities 

The methodology relies on the determination of threshold surface friction velocities, u*t, as a function 

of aerodynamic surface roughness length, z0. In addition to aerodynamic roughness, the degree of 

disturbance of the surface also plays a key role in the estimation of threshold friction velocities. 
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Based on the work of Marticorena et al. (1997), relationships between u*t and z0 where identified and 

compared with wind tunnel data from Gillette et al. (1980, 1982), Gillette (1988) and Nickling and 

Gillies (1989). This comparison is presented in Figure 2-1. 

Figure 2-1. Comparison between the Marticorena et al. (1997) modeled relationship of 

threshold friction velocity and aerodynamic roughness length and wind tunnel data from Gillette 

et al. (1980, 1982), Gillette (1988) and Nickling and Gillies (1989). 

Several general relationships can be described for threshold friction velocity data. Two major 

factors have the greatest influence on the threshold of wind erodible soils: the degree of 

disturbance and the aerodynamic roughness. For loose or disturbed soils the most important 

factor that controls the threshold friction velocity is aerodynamic roughness. The effect of 

surface disturbance on threshold friction velocity can be seen in Table 2-1 for data from Gillette 

et al. (1980, 1982), Gillette (1988), and Nickling and Gillies (1989) where surfaces are grouped 

by land type. For a given surface type, the effect of disturbance is to lower the threshold 

between ~90% to ~20% of the undisturbed value. 

Application of the relationship shown in Figure 2-1 to assign a threshold friction velocity to a 

surface requires information on a surface’s aerodynamic roughness length. This type of 

information is not generally available in land use databases, because they were not specifically 

developed to quantify aerodynamic properties of surfaces. Based on the designation of land use 

type, the aerodynamic roughness can be assigned based on previously reported values for similar 

surfaces. A list of surface types and reported aerodynamic roughness lengths is presented in 

Table 2-2. 

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. 1997

Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)
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Table 2-1. Threshold friction velocities for typical surface types calculated from available data 

and as reported in the literature1

. 

1

Sources include: Gillette et al. (1980, 1982), Gillette (1988), and Nickling and Gillies (1989). 

A degree of uncertainty exists upon assigning an aerodynamic roughness length to a surface, as it 

will be complicated by the individual condition of the surface, which can change through time on 

several scales. For agricultural fields, aerodynamic roughness will change as a function of plant 

height and cover through a growing season and the tillage practices. For natural surfaces, the 

aerodynamics can change through the season as well as annually through several years affecting 

dust production cycles. This is linked to plant growth in response to annual and long term 

climate variability, which will affect plant cover. 

Site Type 

Average 

u*t(m s-1) 

Undisturbed 

Std. D. 

u*t (m s-1) 

Undisturbed

No. of 

Data 

Points

Average

u*t (m s-1)

Disturbed

Std. D. 

u*t (m s-1)

Disturbed 

No. of 

Data 

Points 

% change 

[1-(dist./undist.)]

agricultural 

fields 1.29 0.74 41 0.55 0.25 37 0.57 

alluvial fan 0.72 0.09 2 0.60 0.18 2 0.17 

desert flat 0.75 0.06 4 0.51 0.19 4 0.32 

desert 

pavement 2.17 0.67 4 0.59 0.10 5 0.73 

fan surface 1.43 0.59 5 0.47 0.25 5 0.67 

play, crusted 2.13 0.67 4 0.63 0.50 15 0.70 

playa 1.46 0.98 12 0.58 0.56 25 0.60 

prairie 2.90 n/a 1 0.24 0.03 3 0.92 

sand dune 0.44 0.10 4 0.32 0.05 4 0.27 
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Table 2-2. Typical surface aerodynamic roughness lengths calculated from available data and 

as reported in the literature. 

Site Type 

Average 

zo (cm) 

Std. D. zo

(cm) 

Number of 

Data Points 

Estimated

u*t (m s-1) Source 

agricultural fields (bare) 0.031 0.039 9 0.38 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

desert flat/pavement 0.133 0.180 8 0.79 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

fan surface 0.088 0.148 5 0.57 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

play, crusted 0.059 0.099 15 0.46 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

playa 0.057 0.083 33 0.46 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

prairie 0.049 0.088 4 0.43 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

sand dune 0.007 0.006 4 0.32 

Gillette et al. (1980, 1982), Gillette 

(1988) Nickling and Gillies (1989) 

scrub desert 0.045 0.040 2 0.42 Nickling and Gillies (1989) 

sparse veg. (0.04% cover) 0.370 Wolfe (1993) 

sparse veg. (10.3% cover) 6.800 Wolfe (1993) 

sparse veg. (13.5% cover) 7.200 Wolfe (1993) 

sparse veg. (26% cover) 8.300 Wolfe (1993) 

sparse veg. (8% cover) 5.400 Wolfe (1993) 

thick grass 2.3 Sutton (1953) 

thin grass 5 Sutton (1953) 

sparse grass 0.12 Oke (1978) 

agricultural crops 2-4 Oke (1978) 

orchards 50-100 Oke (1978) 

Decid. Forests 100-600 Oke (1978) 

Conf. Forests 100-601 Oke (1978) 

agricultural crops 15 Deursen et al. (1993) 

urban 100 Deursen et al. (1993) 

Decid. Forests (closed canopy) 121 Deursen et al. (1993) 

Conif. Forests (closed canopy) 134 Deursen et al. (1993) 

Emission Factors 

Field and wind tunnel experiments suggest that the emissions are proportional to wind friction speed 

and approximate theoretical model predictions, but the considerable scatter in the available data 

make it impossible to clearly define this dependence (Nickling and Gillies, 1993). Different surfaces 

appear to have different constants of proportionality for the flux versus wind friction velocity 

relationship, implying that the flux is predictable, but surface and soil properties affect the magnitude 

of the flux. A detailed discussion of wind tunnel studies, including various limitations and measured 

data, was provided in ENVIRON, 2003a; 2003b. The findings of the various wind tunnel studies are 

briefly summarized here. 

Recently Alfaro, et al. (2003) re-analyzed the Nickling and Gillies (1989) data and found that the 

tendency of a surface to emit dust depends not primarily on its textural qualities, but on the size 

distribution of the loose soil aggregates available for saltation, and the aerodynamic roughness length 

that conditions the emission threshold. The re-analysis was based in part on the work of Chatenet, et 

al. (1996) in which they found that desert soils could be broadly divided into four populations based 

upon their soil aggregate populations. The differences between the four groups are based upon the 

estimated geometric mean diameter of the soil particles. The four size classes are 125 mm, 210 mm, 

520 mm, and 690 mm, which are labeled FFS, FS, MS, and CS by Chatenet, et al. (1996).
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Figure 2-2. 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). 

Alfaro et al., (2003) grouped the Nickling and Gillies (1989) emission data based on these 

classes then tested how well the grouped data matched predicted output of a dust production 

model developed by Alfaro and Gomes (2001) that was constrained to use the four different 

geometric mean diameters. The modeled dust emission relationships for the four size classes are 

shown in Figure 2-2. As presented in Alfaro, et al. (2003) the emission data from Nickling and 

Gillies (1989), which fall into the FS class (10 out of 13) are well explained by the model (Figure 

2-3). 

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)

Emission Flux (F, g cm-2 s-1)

FSS

FS

MS

CS

Power (FSS)

Power (FS)

Power (MS)

Power (CS)
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Figure 2-3. Comparison between the Alfaro et al. (2003) model relationship for FS and CS 

sizes and the wind tunnel flux 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). 

Using the Alfaro, et al. (2003) approach, emissions of dust for soils can be confined to four 

different emission factors, depending on the geometric mean grain size, as determined by the 

methods of Chatenet, et al. (1996). The model predictions were tested against the wind tunnel 

data set of Nickling and Gillies (1989) and found to fit the measured data satisfactorily. Of key 

importance is that Chatenet, et al. (1996) established relationships between the 12 soil types that 

are defined in the classical soil texture triangle and their four dry soil types (silt [FSS], sandy silt 

[FS], silty sand [MS], and sand [CS]). The soil texture categorization and the relationships 

among texture assignments and soil groupings are discussed below. 

The MacDougall Method

The MacDougall Method refers to the estimation methodology actually implemented as part of 

the Phase I Wind Blown Dust Project. A description of the implementation of this method, 

including the various assumptions required, was discussed above and is documented in detail in 

ENVIRON, 2004. 

A comparison of the salient features of each of the windblown dust emission estimation 

methodologies is presented in Table 2-3. 

1E-10

1E-09

1E-08

1E-07

1E-06

10 100

u* (cm/s)

Fv (g/cm2/s)

FS

Casa Grande

Mesa (Salt River)

Hayden

Ajo

Yuma (ag)

Glendale

Tucson (Sta Cruz)

Tucson (const.

site)

Mesa (ag)

CS

Yuma (scrub

desert)

Yuma (dist

desert)
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Table 2-3. Inter-comparison of wind blown dust models. 

Draxler Zender ENVIRON/RMC MacDougall 

Emissions 

The emissions are 

function of the friction 

velocity U* using 

Marticorena et al. 

(1997) equation 

The emissions are 

function of the friction 

velocity U* using White 

(1997) for saltating 

mass flux and adjusted 

equation for vertical 

dust mass flux 

The emissions are 

function of the friction 

velocity U* using 

Alfaro and Gomes 

(2001) equations for 

various soil types 

The emissions are 

function of the 10-

meter windspeed, 

U10 

Friction 

Velocity 

Friction velocity is a 

function of wind velocity 

and surface roughness 

Friction velocity is a 

function of wind 

velocity and surface 

roughness 

Friction velocity is a 

function of wind 

velocity and surface 

roughness 

Start 

Emissions 

Emission starts when 

friction velocity is 

greater than threshold 

friction velocity U*t 

Emission starts when 

friction velocity is 

greater than threshold 

friction velocity U*t

Emission starts when 

friction velocity is 

greater than threshold 

friction velocity U*t

Emission starts when 

wind velocity U10 is 

greater than 

threshold wind 

velocity Ut 

Threshold 

Friction 

Velocity 

Threshold friction 

velocity U*t is a function 

of soil particle diameter, 

and surface roughness 

length, 

Threshold friction 

velocity U*t is a function 

of soil particle diameter 

and density, and 

surface roughness 

length 

Threshold friction 

velocity U*t is a 

function surface 

roughness length 

Threshold wind 

velocity is arbitrary 

assigned to all types 

of soil and soil 

surface 

Threshold 

Friction 

Velocity 

Values 

Values of U*t

determined using 

experimental 

data from Mojave 

desert (Gillette et al 

1980, 19982), 

Geomorphological 

classification, and 

Marticorena and 

Bergametti (1995) 

method (equation) 

Empirical equations as 

function of particle 

diameter and density, 

and surface roughness 

length 

Empirical equations 

as function of surface 

roughness length 

(Marticorena et al. 

1997 equation) 

Not considered 

Surface 

Roughness 

Used data from Mojave 

desert (Gillette et al 

1980, 19982) and 

Geomorphological 

classification. Did not 

consider vegetative 

cover (land use) 

Used globally uniform 

value of 0.01 m (D/30) 

D = mean particle 

diameter. 

Developed adjustment 

factors to account for 

vegetative fractions of 

the area. 

Developed a table of 

surface roughness 

values using 

published data for 

various land use types 

Adjustment factors 

for various land use 

and vegetative 

cover 

Soil moisture 

or rain factors 

Is not considered Developed a factor to 

increase threshold 

friction velocity as a 

function of moisture 

content and threshold 

moisture content. The 

values of moisture 

content were taken 

from the National 

Center for Environment 

Prediction (NCEP) 

Need to be developed Assigned number of 

hours after the rain 

to start emissions 

flux. 

Soil conditions 

(disturbed or 

undisturbed) 

Set of equations for 

loose undisturbed and 

disturbed soils 

Is not considered Not considered Two emission 

factors, one for 

disturbed and one 

for undisturbed soils 
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3. WIND BLOWN DUST EMISSION ESTIMATION METHODOLOGY 

Based on the review of recent dust model applications, it is noted that each utilizes a similar 

approach to the determination of threshold friction velocities and dust emission flux rates. Based 

on the particular application, certain simplifying assumptions are incorporated. As with the 

ENVIRON/RMC approach, these assumptions are generally required due to a lack of detailed 

information to fully characterize physical conditions and parameters of erodible surfaces. 

Nevertheless, each of these applications generally follows the methodology of the 

ENVIRON/RMC approach, as described above. 

It was therefore recommended that the Phase II model implementation follow this general 

approach. The approach for the determination and application of each of the required elements 

of the model are presented below. In some cases, further research and/or review of existing 

datasets will be required to implement future improvements to the current estimation 

methodology. 

Friction Velocities 

Surface friction velocities will be determined from the aerodynamic surface roughness lengths 

and the 10-meter wind speeds from the MM5 model simulations. Friction velocity u*, is related 

to the slope of the velocity versus the natural logarithm of height through the relationship: 

 

* o

z

z

z ln 1

u

u

κ = 

where uz = wind velocity at height z (m s-1) 

 u* = friction velocity (m s-1) 

κ = von Karman's constant (0.4) 

 zo = aerodynamic roughness height (m) 

Threshold Friction Velocities 

The threshold friction velocities, u*t, were determined from the relationships developed by 

Marticorena as a function of the aerodynamic surface roughness length, z0. This relationship was 

presented in Figure 2-1. 

Surface Roughness Lengths 

Surface friction velocities, including the threshold friction velocity, are a function of the 

aerodynamic surface roughness lengths. The surface roughness lengths are in turn dependent on 

surface characteristics, particularly land use/land cover. While these values can vary 

considerable for a given land type, published data are available which provide a range of surface 

roughness lengths for various land use types and vegetation cover. These data are presented in 

Table 2-1. Additional information for roughness lengths as a function of land cover were 

identified and reviewed for inclusion in the Phase II model implementation (Mansell, 2003b; 
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Mansell et al., 2004). The final assignments of surface roughness for each land cover type are 

defined and presented below. 

Emission Fluxes 

Emission fluxes, or emission rates, were determined as a function of surface friction velocity and 

soil texture. The relationships developed by Alfaro and Gomes (2001) for each of the soil 

texture groups of Alfaro (2003) were applied to estimate dust emission fluxes. These 

relationships are presented in Figure 2-2. 

Reservoir Characteristics 

Dust emissions from vacant lands are limited by the amount of erodible soil available for 

suspension into the atmosphere. In addition to the amount of soil present, the condition of the 

soils, including textural and stability, as well as climatological factors influence the total wind 

blown dust emission potential of a given parcel of vacant land. The amount of soil available for 

a given land parcel is referred to as the reservoir and can be classified as limited or unlimited. 

Classification of reservoirs as limited or unlimited has implications with respect to the duration 

of time over which the dust emissions are generated. In general, the reservoirs should be 

classified in terms of the type of soils, the depth of the soil layer, soil moisture content and 

meteorological parameters. In addition, the time required for a reservoir to recharge following a 

wind event is influenced by a number of factors including precipitation and snow events and 

freezing conditions of the soils. 

Given that the soils database for use in the project does not provide information concerning the 

moisture content or the depth of the soil layer, certain assumption are made regarding the 

determination and classification of soil reservoirs. These assumptions were investigated 

previously and based primarily on the land use type and stability of the vacant land parcel. 

Based on the result of these investigations (see Appendix A) dust reservoirs were assumed 

limited and all vacant lands assumed undisturbed, or stable. 

The duration and amount of precipitation and snow and freeze events also affect the dust 

emissions from wind erosion. In the Phase I application these parameters were somewhat 

arbitrarily assigned. Barnard (2003) has compiled a set of conditions for treating these events 

based on seasons, soil characteristics and the amounts of rainfall and snow cover. These 

conditions were based on limited information found in the literature and additional assumptions. 

The results of the analysis of Barnard are summarized in Tables 3-1 and 3-2. 
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Table 3-1. Number of days after precipitation event to re-initiate wind erosion for rainfall 

amounts (constant) exceeding 2 inches.

Soil type Spring/Fall Summer Winter

Sand 3 2.1 4.2 

Sandy Loam 3 2.1 4.2 

Fine Sand Loam 3 2.1 4.2 

Loam 4 2.9 3.8 

Silt Loam 4 2.9 3.8 

Sandy Clay Loam 4 2.9 3.8 

Clay Loam 5 3.6 7.2 

Silty Clay Loam 6 4.3 8.6 

Clay 7 5 10 

Table 3-2. Number of days after precipitation event to re-initiate wind erosion for rainfall 

amounts (constant) less than or equal to 2 inches. 

Soil type Spring/Fall Summer Winter

Sand 1 0.7 1.4 

Sandy Loam 1 0.7 1.4 

Fine Sand Loam 1 0.7 1.4 

Loam 2 1.4 2.8 

Silt Loam 2 1.4 2.8 

Sandy Clay Loam 2 1.4 2.8 

Clay Loam 3 2 4 

Silty Clay Loam 4 2.8 5.6 

Clay 5 3.6 7.2 

Soil Disturbance 

It has been noted that the level of disturbance of an erodible surface is an important parameter in 

the estimation of wind blown dust emissions. Disturbed surfaces tend to generate more dust than 

un-disturbed surfaces. In the application of the Phase I model, different emissions rates were 

applied for disturbed versus un-disturbed surfaces. The assumed disturbance level of the surface 

was to be determined by the land type and invariant in time and across the modeling domain. 

Thus, assumptions were required to assign surface disturbance based on land cover type. As 

noted previously, the disturbance level of a surface more appropriately has the effect of altering 

the threshold surface friction velocity; disturbed surfaces have lower thresholds while 

undisturbed surfaces exhibit higher threshold friction velocities. 

The disturbance level of various surfaces across a regional scale modeling is difficult to 

determine given the lack of detail in both the LULC soils data available for use in the model. 

Except for agricultural lands, which are treated separately in the model as described below, 

vacant land parcels are typically un-disturbed unless some activity is present such as to cause a 

disturbance, for example, off-road vehicle activity in desert lands, or animal grazing on 

rangelands. 

In the Phase II model application, all non-agricultural land types are considered un-disturbed, 

since there is no a priori information to indicate otherwise for the regional scale modeling 

domain to be considered. Additional information concerning disturbance levels for certain land 

types was investigated to determine whether an assumed percentage of specific land types can be 
May 2006 

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considered disturbed versus un-disturbed. No definitive information to support assigning a 

disturbance level top various land types was found. For the purpose of evaluating the sensitivity 

of the model to disturbance levels, a number of sensitivity simulations were conducted. The 

definition of these sensitivity simulations and the corresponding results were documented in 

Mansell, et al., 2005, and are also included herein as Appendix A. 

Data Sources 

The various data sets required for implementation of the Phase II wind blown dust model are 

summarized in this section. 

Land Use/Land Cover

The current implementation of the wind blown dust model utilized updated land use/land cover 

(LULC) data, as noted previously. These data were developed as part of the WRAP GIS Update 

Project (Mansell and Hoats, 2005), and are based on satellite imagery from the year 2000. As in 

previous applications of the wind blown dust model, these LULC data were combined with the 

agricultural crop data from the BELD3 database in order to address non-climatic effects for 

agricultural lands. For completeness, the LULC data used previously and the currently utilize 

data are presented and discussed below. 

The Phase I model implementation used the BELD3 land use/land cover (LULC) database to 

specify vacant land types. Given that these data are based primarily on information from pre1990 and that the distinction between barren/desert lands and shrub/grasslands was not sufficient 

to adequately characterize these land types, it was recommended that the LULC data be based on 

the National Land Cover Database. An evaluation of the NLCD database conducted during 

Phase I Study revealed that the shrubland and barren land (desert) categories were better 

represented as distinct land types. 

The National Land Cover Data (1992 NLCD) was developed as part of a cooperative project 

between the U.S. Geological Survey and the U.S. Environmental Protection Agency to produce a 

consistent land cover data layer for the entire conterminous U.S. based on 30-meter Landsat 

thematic mapper (TM) data. The NLCD was developed from TM data acquired from the MultiResolution Land Characterization (MRLC) Consortium, a partnership of federal agencies that 

produce or use land cover data. The partners include USGS (National Mapping, Biological 

Resources, and Water Resources Divisions), the USEPA, the U.S. Forest Service and the 

National Oceanic and Atmospheric Administration. A more detailed discussion of the 1992 

NLCD LULC data can be found in Mansell, et al., 2005 and Mansell and Hoats, 2005. The land 

cover characteristics, defined in terms of 21 separate categories, are presented in Table 3-3. 

Figure 3-1 displays the 1992 NLCD data for the conterminous US. 
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Table 3-3. NLCD Land Cover Classification Codes.

Code Description 

11 Open Water 

12 Perennial Ice/Snow 

21 Low Intensity Residential 

22 High Intensity Residential 

23 Commercial/Industrial/Transportation 

31 Bare Rock/Sand/Clay 

32 Quarries/Strip Mines/Gravel Pits 

33 Transitional 

41 Deciduous Forest 

42 Evergreen Forest 

43 Mixed Forest 

51 Shrubland 

61 Orchards/Vineyards/Other 

71 Grasslands/Herbaceous 

81 Pasture/Hay 

82 Row Crops 

83 Small Grains 

84 Fallow 

85 Urban/Recreational Grasses 

91 Woody Wetlands 

92 Emergent Herbaceous Wetlands 

Figure 3-1. NLCD for the Conterminous US.
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The North America Land Cover (NALC) for the year 2000 (Latifovic, et al. 2002) was 

developed jointly by the Natural Resources Canada - Canada Centre for Remote Sensing and the 

USGS EROS Data Center as part of the larger Global Land Cover 2000 project implemented by 

the Global Vegetation Monitoring Unit, Joint Research Center (JRC) of the European 

Commission. The North American database was compiled using SPOT VEGETATION data 

from VGT sensors during the 2000 growing season at a spatial resolution of 1-km. Considerable 

effort was devoted to processing the data to ensure data consistency for quantitative land surface 

analysis. 

The data are available as GIS raster datasets for each continent, in a geodetic coordinate system 

and can be obtained from the project website at 

http://www.gvm.sai.jrc.it/glc2000/ProductGLC2000.htm. The land use classification scheme 

includes 29 separate categories as presented in Table 3-4. Figure 3-2 displays these data for the 

North American continent, including Central America. 

In addition the recent time period represented by the data, another advantage of this dataset over 

currently used data (i.e., the NLCD), is the inclusion of more detailed landuse classifications for 

scrub/shrub and grasslands. Barren and unconsolidated material and sparse vegetation 

classification are better represented in these data compared to the NLCD, or other continental- or 

global-scale LULC databases. Shortcomings of the data with respect to emission inventory 

development, both estimation and spatial allocation, are related to the lack of detail in the 

agricultural and cropland categories as well as the unspecified detail for urban lands. 

Nevertheless, these data provide improvements over previously used LULC data given the recent 

vintage, regional coverage, high resolution and slightly better classification scheme. 
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Table 3-4. NALC 2000 LULC classification scheme. 

Code Description 

1 Tropical or Sub-tropical Broadleaved Evergreen Forest - Closed Canopy 

2 Tropical or Sub-tropical Broadleaved Deciduous Forest - Closed Canopy 

3 Temperate or Sub-polar Broadleaved Deciduous Forest - Closed Canopy 

4 Temperate or Sub-polar Needleleaved Evergreen Forest - Closed Canopy 

5 Temperate or Sub-polar Needleleaved Evergreen Forest - Open Canopy 

6 Temperate or Sub-polar Needleleaved Mixed Forest - Closed Canopy 

7 Temperate or Sub-polar Mixed Broadleaved or Needleleaved Forest - Closed Canopy 

8 Temperate or Sub-polar Mixed Broaddleleaved or Needleleaved Forest - Open Canopy 

9 Temperate or Subpolar Broadleaved Evergreen Shrubland - Closed Canopy 

10 Temperate or Subpolar Broadleaved Deciduous Shrubland - Open Canopy 

11 Temperate or Subpolar Needleleaved Evergreen Shrubland - Open Canopy 

12 

Temperate or Sub-polar Mixed Broadleaved and Needleleaved Dwarf-Shrubland - Open 

Canopy 

13 Temperate or Subpolar Grassland 

14 Temperate or Subpolar Grassland with a Sparse Tree Layer 

15 Temperate or Subpolar Grassland with a Sparse Shrub Layer 

16 Polar Grassland with a Sparse Shrub Layer 

17 Polar Grassland with a Dwarf-Sparse Shrub Layer 

18 Cropland 

19 Cropland and Shrubland/woodland 

20 Subpolar Needleleaved Evergreen Forest Open Canopy - lichen understory 

21 Unconsolidated Material Sparse Vegetation (old burnt or other disturbance) 

22 Urban and Built-up 

23 Consolidated Rock Sparse Vegetation 

24 Water bodies 

25 Burnt area (resent burnt area) 

26 Snow and Ice 

27 Wetlands 

28 Herbaceous Wetlands 

29 Tropical or Sub-tropical Broadleaved Evergreen Forest - Open Canopy 
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Figure 3-2a. North American Landuse/Landcover for 2000.
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Figure 3-2b. Legend for 2000 North American Landuse/Landcover.

Soil Characteristics

Application of the emission factor relations described above requires the characterization of soil 

texture in terms of the 4 soil groups considered by the model. The characteristics, or type, of soil 

is one of the parameters of primary importance for the application of the emission estimation 

relations derived from wind tunnel study results. The State Soil Geographic Database 

(STATSGO) will be used to determine the type of soils present in the modeling domain for 

which the emission inventory will be developed. The STATSGO database was developed by the 

Natural Resources Conservation Service of the U.S. Department of Agricultural (USDA) and 

provides detailed information concerning the taxonomy of the soils, including soil texture class, 

percentage of sand, silt and clay, and the available water capacity of the soil. While the 

complete STATSGO database available from the USDA includes numerous additional features, 

those features relevant for this project were considered to be the soils texture class. The soils 

data are available as geospatial coverages and associated attribute tables for each state in the US. 

Soils databases were obtained from the Earth System Science Center (ESSC) at Penn State 

University (http://www.essc.psu.edu/soil_info/). 

The classification of soil textures and soil group codes is based on the standard soil triangle that 

classifies soil texture in terms of percent sand, silt and clay. Combining the soil groups defined 

by the work of Alfaro, et al. (2003) and Chatenet, et al. (1996) and the standard soil triangle 
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provides the mapping of the 12 soil textures to the 4 soil groups considered in their study. 

Combining the data from these two soil texture/soil group mappings results in the unique 

mapping of soil textures to the soil groups for which emission factor data can be applied. The 

results of combining these soil texture definitions allows the assignment of the loam soil group in 

terms of standard soil texture. The soil texture mappings are summarized in Table 3-5. Figure 3-

3 displays the STATSGO soil texture for the conterminous US. 

Table 3-5. STATSGO soil texture and soil group codes. 

STATSGO Soil 

Texture 

Soil Texture 

Code 

Soil Group Soil Group 

Code 

No Data 0 N/A 0 

Sand 1 CS 4 

Loamy Sand 2 CS 4 

Sandy Loam 3 MS 3 

Silt Loam 4 FS 1 

Silt 5 FSS 2 

Loam 6 MS 3 

Sandy Clay 

Loam 

7 MS 

3 

Silty Clay Loam 8 FSS 1 

Clay Loam 9 MS 3 

Sandy Clay 10 MS 3 

Silty Clay 11 FSS 1 

Clay 12 FS 2 

Figure 3-3. STATSGO Soils Database soils texture class for conterminous U.S. 
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Surface Roughness Lengths

Surface roughness lengths can vary considerably for a given land type, as evidenced by 

examination of the data in Table 2-2. Surface roughness lengths were assigned as a function of 

land use type based on a review of the information in Table 2-2. As noted above, the disturbance 

level of various surfaces has the effect of altering the surface roughness lengths, which in turn 

impact the potential for vacant lands to emit dust from wind erosion. The soil disturbance 

assumptions and the adjustments to threshold surface friction velocity were presented above. 

An examination of Figure 2-1, which relates the aerodynamic surface roughness lengths to the 

threshold surface friction velocity, reveals that for surface roughness lengths larger than 

approximately 0.1 cm, the threshold friction velocities increase rapidly above values that can be 

realistically expected to occur in the meteorological data used in the model implementation. 

Therefore to simplify the model implementation, only those land types with roughness length 

less than or equal to 0.1 cm are considered as potentially erodible surfaces. In addition, for a 

given surface roughness, as determined by the land use type, the threshold friction velocity has a 

constant value. Thus, the land use data is mapped to an internal dust code used within the model 

to minimize computer resource requirements and coding efforts. 

The mapping of land use types to dust codes is presented in Table 3-6, while Table 3-7 

summarizes the surface characteristics by dust code. Table 3-6 summarizes the dust code 

assignments for both the 1992 NLCD and 2000 NALC data for comparison purposes. Table 3-8 

provides a summary of the 1992 and 2000 LULC data for the entire conterminous US and for the 

WRAP states. Relevant differences between these two datasets of note include, for the 

conterminous US, an approximate 20% decrease in agricultural lands from 1992 to 2000; a slight 

increase in grasslands; and an approximate 25% increase in shrublands from 1992 to 2000. 

Barren lands remained nearly constant as did the non-dust contributing “other” land category. 

A summary by state of the percentage of land for each dust code based on the 2000 NALC data 

is presented in Table 3-9. Table 3-10 presents the total acreage of each dust category by state. 

Table 3-6. Dust code assignments by LULC. 

Dust Code 1992 NLCD 2000 NALC 

3 (from 

BELD3) Pasture/Hay Pasture/Hay 

 Row crops Row crops 

 Small grains Small grains 

 Fallow Fallow 

 Alfalfa Alfalfa 

 Barley Barley 

 Corn Corn 

 Cotton Cotton 

 Grass Grass 

 Hay Hay 

 Misc. crops Misc. crops 

 Oats Oats 

 Pasture Pasture 

 Peanuts Peanuts 

 Potatoes Potatoes 
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Table 3-6. (Continued) Dust code assignments by LULC. 

Dust Code 1992 NLCD 2000 NALC 

 Rice Rice 

 Rye Rye 

 Sorghum Sorghum 

 Soybeans Soybeans 

 Tobacco Tobacco 

 Wheat Wheat 

4 Grasslands Temperate or Subpolar Grassland 

 USGS Dryland cropland & pasture

Temperate or Subpolar Grassland with a 

Sparse Tree Layer 

 USGS Irrigated cropland 

Temperate or Subpolar Grassland with a 

Sparse Shrub Layer 

 USGS cropland/grassland mosiac Polar Grassland with a Sparse Shrub Layer 

 USGS grassland 

Polar Grassland with a Dwarf-Sparse Shrub 

Layer 

6 Shrubland 

Temperate or Subpolar Broadleaved Evergreen 

Shrubland - Closed Canopy 

 USGS shrubland 

Temperate or Subpolar Broadleaved Deciduous 

Shrubland - Open Canopy 

 USGS mixed shrub/grassland 

Temperate or Subpolar Needleleaved 

Evergreen Shrubland - Open Canopy 

 

Temperate or Sub-polar Mixed Broadleaved 

and Needleleaved Dwarf-Shrubland - Open 

Canopy 

7 Bare rock/Clay/Sand 

Unconsolidated Material Sparse Vegetation (old 

burnt or other disturbance) 

 Quarries/Strip mines Consolidated Rock Sparse Vegetation 

 Transitional Burnt area (resent burnt area) 

 USGS Barren 

Other Open water 

Tropical or Sub-tropical Broadleaved 

Deciduous Forest - Closed Canopy 

 Ice/Snow 

Temperate or Sub-polar Broadleaved 

Deciduous Forest - Closed Canopy 

 Woody wetlands 

Temperate or Sub-polar Needleleaved 

Evergreen Forest - Closed Canopy 

 Herbaceous wetlands 

Temperate or Sub-polar Needleleaved 

Evergreen Forest - Open Canopy 

 USGS savanna 

Temperate or Sub-polar Needleleaved Mixed 

Forest - Closed Canopy 

 USGS water 

Temperate or Sub-polar Mixed Broadleaved or 

Needleleaved Forest - Closed Canopy 

 USGS wooded wetlands 

Temperate or Sub-polar Mixed Broaddleleaved 

or Needleleaved Forest - Open Canopy 

 USGS wooded tundra 

Subpolar Needleleaved Evergreen Forest Open 

Canopy - lichen understory 

 USGS mixed tundra Urban and Built-up 

 USGS ice or snow Water bodies 

 Low intensity residential Snow and Ice 

 High density residential Wetlands 
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Table 3-6. (Concluded) Dust code assignments by LULC. 

Dust Code 1992 NLCD 2000 NALC 

 Commercial/Ind/Trans Herbaceous Wetlands 

 Deciduous Forest 

Tropical or Sub-tropical Broadleaved Evergreen 

Forest - Open Canopy 

 Evergreen Forest 

 Mixed Forest 

 Urban/Rec grasses 

 USGS Urban 

 USGS cropland/woodland mosiac 

 USGS deciduous forest 

 USGS evergreen broadleaf forest 

 USGS evergreen needleleaf forest 

 USGS mixed forest 

 Forest (from FIA data) 

 Orchards/Vineyards 

Table 3-7. Surface characteristics by dust code and land use category. 

Dust Code 3 4 6 7 

Landuse category Agricultural (bare field) Grassland Shrubland Barren 

Surface roughness length, Z0 (cm) 0.031 0.1 0.05 0.002 

Threshold friction velocity (mi/h) 8.33 13.81 9.62 6.81 

Threshold wind velocity at 38m 

height (mi/h) 

29.5 44.25 32.75 28.5 

Table 3-8. Comparison of 1992 and 2000 LULC data by dust code. 

 Conterminous US WRAP States 

Landuse Category % 1992 NLCD% 2000 NALC% 1992 NLCD % 2000 NALC

Forest/Urban/Wetlands/Other 39.24% 39.34% 25.53% 31.54%

Agricultural 25.62% 20.03% 13.46% 7.34%

Grasslands 15.29% 16.38% 23.48% 17.44%

Shrublands 17.97% 22.62% 34.41% 41.58%

Barren 1.88% 1.64% 3.12% 2.10%

Total 100.00% 100.00% 100.00% 100.00%

Table 3-9. Summary of 2000 NALC LULC by dust code percentages by state. 

Percent Acreage by Dust Code 

 Dust Code 

State Other 3 4 6 7 Total

Alabama 76.71% 18.85% 3.17% 0.06% 1.21% 100.00%

Arizona 26.63% 0.31% 4.45% 62.52% 6.09% 100.00%

Arkansas 64.73% 32.14% 2.95% 0.05% 0.14% 100.00%

California 59.13% 3.68% 5.68% 29.30% 2.22% 100.00%

Colorado 45.00% 4.64% 18.42% 31.75% 0.19% 100.00%

Connecticut 99.27% 0.73% 0.00% 0.00% 0.00% 100.00%

Delaware 76.73% 23.14% 0.00% 0.13% 0.00% 100.00%

District of Columbia 100.00% 0.00% 0.00% 0.00% 0.00% 100.00%
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Table 3-9. (Continued) Summary of 2000 NALC LULC by dust code percentages by state. 

Percent Acreage by Dust Code 

 Dust Code 

State Other 3 4 6 7 Total

Florida 61.34% 18.60% 12.72% 0.16% 7.18% 100.00%

Georgia 70.31% 23.31% 6.36% 0.02% 0.00% 100.00%

Idaho 46.05% 4.11% 8.89% 40.19% 0.75% 100.00%

Illinois 9.52% 90.30% 0.05% 0.07% 0.07% 100.00%

Indiana 22.97% 75.58% 0.02% 1.41% 0.01% 100.00%

Iowa 2.68% 96.78% 0.11% 0.40% 0.03% 100.00%

Kansas 2.32% 23.65% 69.49% 4.54% 0.01% 100.00%

Kentucky 52.70% 47.01% 0.28% 0.00% 0.01% 100.00%

Louisiana 69.55% 25.67% 4.13% 0.45% 0.19% 100.00%

Maine 97.18% 0.89% 0.03% 1.91% 0.00% 100.00%

Maryland 81.57% 18.10% 0.19% 0.11% 0.03% 100.00%

Massachusetts 98.93% 0.80% 0.13% 0.13% 0.00% 100.00%

Michigan 53.87% 44.41% 1.31% 0.37% 0.03% 100.00%

Minnesota 36.90% 50.70% 1.04% 11.35% 0.01% 100.00%

Mississippi 67.64% 30.09% 1.63% 0.17% 0.47% 100.00%

Missouri 38.66% 61.00% 0.19% 0.11% 0.03% 100.00%

Montana 25.55% 6.04% 28.61% 39.61% 0.19% 100.00%

Nebraska 2.99% 39.17% 23.15% 34.20% 0.49% 100.00%

Nevada 12.19% 0.30% 3.25% 82.50% 1.75% 100.00%

New Hampshire 98.46% 0.56% 0.19% 0.78% 0.00% 100.00%

New Jersey 93.40% 6.08% 0.24% 0.29% 0.00% 100.00%

New Mexico 26.21% 0.49% 10.36% 58.91% 4.03% 100.00%

New York 88.95% 10.90% 0.07% 0.08% 0.00% 100.00%

North Carolina 63.76% 34.79% 1.41% 0.01% 0.02% 100.00%

North Dakota 5.01% 60.63% 25.60% 8.37% 0.38% 100.00%

Ohio 43.72% 56.15% 0.11% 0.03% 0.00% 100.00%

Oklahoma 21.51% 16.78% 57.79% 3.85% 0.08% 100.00%

Oregon 49.63% 2.11% 4.34% 43.22% 0.71% 100.00%

Pennsylvania 92.87% 7.05% 0.08% 0.00% 0.00% 100.00%

Rhode Island 99.29% 0.71% 0.00% 0.00% 0.00% 100.00%

South Carolina 60.87% 33.30% 5.49% 0.01% 0.34% 100.00%

South Dakota 8.21% 31.62% 36.84% 22.67% 0.66% 100.00%

Tennessee 65.80% 31.98% 2.00% 0.07% 0.14% 100.00%

Texas 18.13% 2.91% 46.09% 27.11% 5.76% 100.00%

Utah 8.82% 1.02% 17.60% 65.58% 6.98% 100.00%

Vermont 97.54% 2.33% 0.01% 0.12% 0.00% 100.00%

Virginia 88.19% 11.55% 0.24% 0.00% 0.01% 100.00%

Washington 64.69% 4.19% 7.81% 21.46% 1.84% 100.00%

West Virginia 99.10% 0.85% 0.03% 0.00% 0.02% 100.00%

Wisconsin 67.78% 31.24% 0.01% 0.96% 0.00% 100.00%

Wyoming 10.17% 1.08% 68.86% 18.98% 0.91% 100.00%

Total 39.34% 20.03% 16.38% 22.62% 1.64% 100.00%
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Table 3-10. Summary of 2000 NALC LULC by dust code acreages by state.

Total Acreage by Dust Code 

 Dust Code 

State Other 3 4 6 7 Total

Alabama 24,692,490 6,067,619 1,021,589 17,792 390,702 32,190,191

Arizona 18,758,664 219,547 3,134,865 44,041,477 4,292,737 70,447,290

Arkansas 21,296,801 10,573,498 969,282 16,724 44,835 32,901,140

California 70,657,941 4,393,081 6,784,617 35,016,552 2,651,292 119,503,483

Colorado 28,378,893 2,926,349 11,615,014 20,022,217 121,338 63,063,810

Connecticut 3,001,785 22,061 0 0 0 3,023,846

Delaware 820,189 247,302 0 1,423 0 1,068,914

District of Columbia 33,092 0 0 0 0 33,092

Florida 21,459,416 6,507,425 4,451,082 54,798 2,513,585 34,986,306

Georgia 25,445,071 8,436,381 2,302,578 7,828 0 36,191,859

Idaho 23,874,792 2,131,780 4,609,782 20,838,492 391,058 51,845,903

Illinois 3,364,732 31,912,644 16,724 23,841 23,485 35,341,425

Indiana 5,165,233 16,994,101 4,626 317,401 2,135 22,483,496

Iowa 948,288 34,204,191 39,853 140,197 9,252 35,341,781

Kansas 1,173,528 11,971,912 35,175,608 2,297,596 4,626 50,623,270

Kentucky 13,250,054 11,820,684 71,166 0 2,491 25,144,395

Louisiana 18,911,671 6,980,324 1,123,001 121,694 53,019 27,189,708

Maine 18,883,204 172,578 5,337 370,775 0 19,431,895

Maryland 4,567,082 1,013,405 10,675 6,049 1,779 5,598,990

Massachusetts 4,697,672 38,074 6,405 6,405 0 4,748,556

Michigan 18,762,222 15,467,945 456,530 128,455 11,031 34,826,182

Minnesota 19,225,869 26,417,912 544,420 5,911,409 4,982 52,104,592

Mississippi 19,977,027 8,886,151 480,371 51,595 139,130 29,534,274

Missouri 16,709,437 26,364,181 83,620 49,460 12,098 43,218,797

Montana 23,090,186 5,453,456 25,847,871 35,794,753 170,443 90,356,709

Nebraska 1,409,800 18,489,300 10,928,261 16,142,599 229,866 47,199,827

Nevada 8,413,608 209,584 2,246,357 56,934,277 1,209,823 69,013,649

New Hampshire 5,535,653 31,669 10,675 44,123 0 5,622,119

New Jersey 4,076,036 265,449 10,319 12,454 0 4,364,259

New Mexico 19,532,239 365,082 7,721,162 43,892,028 3,002,496 74,513,007

New York 26,820,356 3,286,805 20,282 24,908 0 30,152,351

North Carolina 18,968,248 10,349,325 419,880 3,914 6,405 29,747,772

North Dakota 2,231,768 27,031,363 11,413,970 3,733,372 170,799 44,581,271

Ohio 11,346,718 14,572,320 27,399 7,828 0 25,954,265

Oklahoma 9,201,772 7,180,300 24,727,006 1,645,715 32,381 42,787,175

Oregon 29,960,558 1,273,161 2,618,555 26,092,683 427,352 60,372,310

Pennsylvania 26,427,519 2,005,816 23,129 0 0 28,456,464

Rhode Island 598,507 4,270 0 0 0 602,777

South Carolina 11,215,416 6,135,938 1,010,914 1,779 62,626 18,426,674

South Dakota 3,947,226 15,203,919 17,713,946 10,901,218 316,333 48,082,642

Tennessee 17,174,151 8,347,068 522,359 18,859 37,006 26,099,443

Texas 29,356,003 4,721,157 74,646,800 43,900,568 9,336,276 161,960,803

Utah 4,633,623 536,236 9,246,251 34,461,812 3,668,967 52,546,889

Vermont 5,901,446 140,909 712 7,472 0 6,050,539

Virginia 21,486,459 2,814,618 59,068 1,067 2,135 24,363,347

Washington 27,627,735 1,789,827 3,336,621 9,164,410 786,385 42,704,978

West Virginia 15,182,925 130,590 4,626 0 2,847 15,320,987

Wisconsin 22,841,817 10,528,664 4,270 323,806 0 33,698,556

Wyoming 6,127,043 650,102 41,469,891 11,428,559 550,825 60,226,419

Total 737,161,965 375,286,071 306,937,469 423,980,385 30,682,538 1,874,048,428
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Meteorology

As with the Phase I model implementation, gridded hourly meteorological data is required for 

the dust estimation methodology. For Phase II of the study, the meteorological data will be 

based on MM5 model simulation results. Data fields required include wind speeds, precipitation 

rates, soil temperatures and ice/snow cover. The 2002 MM5 model results on the National 

Unified RPO Grid, at both 12- and 36-km resolution, will be processed as in Phase I and used in 

the application of the revised wind blown dust model. 

Figure 3-4 displays the annual average wind speeds on the 36-km regional modeling domain. 

Figure 3-5 presents monthly average wind speeds for the 36-km modeling domain. Figures 3-6 

and 3-7 display the corresponding average wind speed for the WRAP 12-km modeling domain. 

Figure 3-4. Annual average wind speeds for the WRAP 36-km regional modeling domain. 
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Figure 3-5. Monthly average wind speeds for the WRAP 36-km regional modeling domain. 
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Figure 3-5 (concluded). Monthly average wind speeds for the WRAP 36-km regional modeling domain. 
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Figure 3-6. Annual average wind speeds for the WRAP 12-km modeling domain. 

Figure 3-7. Monthly average wind speeds for the WRAP 12-km modeling domain.
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Figure 3-7 (continued). Monthly average wind speeds for the WRAP 12-km modeling domain.
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Figure 3-7 (concluded). Monthly average wind speeds for the WRAP 12-km modeling domain.

Agricultural Land Adjustments 

Unlike other types of vacant land, windblown dust emissions from agricultural land are subject 

to a number of non-climatic influences, including irrigation and seasonal crop growth. As a 

result, several non-climatic correction or adjustment factors were developed for applicability to 

the agricultural wind erosion emissions. These factors included: 

• Long-term effects of irrigation (i.e., soil “clodiness”); 

• Crop canopy cover; 

• Post-harvest vegetative cover (i.e., residue); 

• Bare soil (i.e., barren areas within an agriculture field that do not develop crop 

canopy for various reasons, etc.); and 

• Field borders (i.e., bare areas surrounding and adjacent to agricultural fields). 
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The methodology used to develop individual non-climatic correction factors for the Phase I study 

was described in detail in ENVIRON, 2004. Most of these methods were based upon previous 

similar work performed by the California Air Resources Board (CARB) in their development of 

California-specific adjustment factors for USDA’s Wind Erosion Equation (WEQ) (CARB, 

1997). These correction factors were developed for specific soil textures, crop types, and 

geographic locations and then applied to the wind erosion estimates developed from the wind 

tunnel studies. Correction factors are developed only for the 17 field crops specifically identified 

in the BELD3.1 data set (i.e., alfalfa, barley, corn, cotton, grass, hay, oats, pasture, peanuts, 

potatoes, rice, rye, sorghum, soybeans, tobacco, wheat, and miscellaneous crops). Due to the 

insufficient characterization of the wind erosion emission processes for orchards and vineyards, 

correction factors for this type of agricultural land were not developed. 

For the Phase II dust model implementation, these same non-climatic adjustments are applied. 

However, because the BELD3 database will not be used, these factors will need to be related to 

the agricultural land types available in the 2000 NALC LULC data. The exiting county-level 

crop percentages from the BELD3 database are linked to the aggregated agricultural land parcels 

from the NALC data used. 

For states outside the WRAP and CENRAP regions, the non-climatic agricultural adjustments 

described above were applied based on less detailed agricultural information. The data required 

for agricultural adjustments within the WRAP and CENRAP regions were developed during 

Phase I of the project that utilized a modeling domain that did not extend much farther east than 

the Mississippi River. For Phase II of the project, resource constraints limited the level of detail 

of these data for the Eastern states. Therefore, while agricultural adjustment are applied to the 

eastern portion of the domain, results for that region may still be somewhat overestimated and/or 

uncertain in comparison with the estimates in the western portion of the modeling domain. 

Fugitive Dust Transport Fractions 

The concept of fugitive dust transport fractions has been considered and refined in recent years. 

It has been recognized that, due to various mechanisms, dust particles are subject to near source 

removal. These mechanisms include gravitational settling, particle deposition to the ground and 

impaction and removal due to particle capture by the surrounding vegetation canopy and other 

physical structures. The EPA for many years had promoted the “divide by four” approach for 

reducing the emission from fugitive dust sources to account for these processes. The idea is that 

only a limited amount of the dust emitted by a particular source is transported significantly to 

affect the total available emissions in the atmosphere for air quality grid modeling. 

Recent research has shown that the amount of fugitive dust captured in the surround canopy or 

on physical structures can be related to the physical characteristics of the land surface, i.e., land 

use/land cover. The EPA recently developed county-level transport fractions for use in 

emissions inventory development for air quality modeling (Pace, 2003). The county-level 

transport fractions were based on the percentage of land use in each county derived from the 

BELD3 LULC database. The transport fractions were calculated as a weighted sum of landusespecific fractions for each landuse type. 

Within the wind blown dust model, rather than applying county-level fractions, landusedependent transport fractions were calculated based on the gridded landuse data used in the 
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estimation methodology. The fractions used for each of the relevant land use types are presented 

in Table 3-11. In the draft wind blown dust inventory, the transport fractions labeled “original” 

in Table 3-11 were applied. Since the time of the development of the draft dust inventory, these 

fractions have been revised and updated (Pace, 2005). The current implementation of the wind 

blown dust model, therefore applied these revised fractions, labeled “Revised” in Table 3-11.

Table 3-11. Transport fractions as a function of landuse. 

LULC 

Original Transport 

Fractions 

Revised Transport 

Fractions 

Barren & Water 0.97 1.00 

Agricultural 0.85 0.75 

Grasses 0.70 0.75 

Scrubland & Sparsely Wooded 

(Shrublands) 0.60 0.75 

Urban 0.30 0.00 

Forested 0.30 0.00 

PM Fine Fraction 

The dust estimation methodology implemented within the model provides fugitive dust 

emissions of PM10. For the application of air quality models, the PM10 emissions must be 

apportioned between coarse and fine PM. This apportionment was applied uniformly for all 

regions and soil textures throughout the domain for the Phase I and Draft Phase II inventories. 

The fraction of fine PM was based on limited field studies, as documented in the Phase I report 

(ENVIRON, 2004). Both the Phase I and Draft Phase I inventories used a ratio of coarse PM to 

PM10 of 0.78. 

For the current model implementation, this ratio was revised based on recent work by the 

Midwest Research Institute (MRI, 2005). The WRAP Dust Emission Joint Forum (DEJF) 

funded a project to evaluate the fine fraction of particulate matter in fugitive dust, due to the 

recognition that EPA’s AP-42 guidance documents were based on analysis procedures which 

may have been biased by as much as a factor of 2. It was anticipated that the work of MRI 

might provide some indication of the variability of the fine fraction with soil texture and/or 

geographic region. However, the study was unable to provide any definitive result to support 

application of a variable fine fraction of PM dust. Nevertheless, the study did provide 

evidence supporting a revision of the fine fraction of PM for use in the wind blown dust 

model. The revised dust inventory presented herein used a constant value of 0.9 for the fine 

fraction of PM dust. 
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4. MODEL RESULTS FOR 2002 

Sensitivity Simulations for 2002 

The revised windblown dust emission model was run using the estimation methodologies and 

data described above. A total of four scenarios were conducted to evaluate the sensitivity of the 

model to the various assumptions incorporated in the methodology. The four scenarios were 

designed to consider the effects of soil disturbance and reservoir characteristics. These scenarios 

were as follows: 

a. No limitation on dust event duration; all soils loose and undisturbed 

b. Dust event duration limited to 10 hours per day; all soils loose and undisturbed 

c. No limitation on dust event duration for disturbed or undisturbed soils; 10% of grassland, 

shrubland, and barren land area assumed disturbed; threshold friction velocity for 

disturbed grass and shrubland = 0.5 ·(undisturbed value); threshold friction velocity for 

barren land = 0.27 · (undisturbed value) 

d. Dust event duration limited to 10 hours per day for undisturbed soils; no limitation on 

dust event duration for disturbed soils; 10% of grassland, shrubland and barren land area 

assumed disturbed; threshold friction velocity for disturbed grass and shrubland = 0.5 ·

(undisturbed value); threshold friction velocity for barren land = 0.27 · (undisturbed 

value) 

Since these original sensitivity simulations were performed, a minor error was discovered in the 

meteorological data processor. The effect of the error was to shift the wind fields a fixed number 

of grid cells to the west. Therefore, the model was re-run to correct this error as well as to utilize 

the latest MM5 simulation results. Nevertheless, the results of the sensitivity scenarios described 

below, are still relevant because in each case the wind field shift is the same. Results of these 

scenarios can still be compared with each other to provide some insight into the sensitivity of the 

model and estimation methodology to various assumptions. A discussion of these results is 

provided in Appendix A. 

Based on a review and comparison of the results of the sensitivity simulations, Scenario b was 

recommended for further evaluation and air quality modeling. Scenario b assumes that all soils 

are loose and undisturbed, and imposes a 10-hour-per-day limit on the duration of dust events. 

Given the limitations of the data used for this task, this scenario appears to be the most 

appropriate. 

• Dust Event Duration: It is reasonable to expect that a limit on the dust event duration is 

related to the type and amount of spoil available for erosion due to winds. Therefore, the 

assumed limit of 10 hours per day, although only an assumption, is more appropriate than 

imposing no limit at all on the dust reservoirs. 

• Soil Disturbance: Although clearly not all soils are undisturbed, there is no information 

available to assign disturbance levels to various areas throughout the region. Scenarios c 

and d assumed a 10% level of disturbance, but that was only for conducting sensitivity 

simulations. Clearly the percent disturbance will vary by region, soils type, and season. In 

addition, the threshold surface velocities for the disturbed soil cases are based on only 

very limited test results and are therefore not likely to apply region-wide. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-2 

Given the lack of supporting data for the dust reservoir and soil disturbance characteristics, we 

felt that the results of Scenario b would be more universally applicable on a regional scale for the 

WRAP’s air quality modeling purposes. All subsequent model runs were performed using the 

assumptions associated with Scenario b. 

Draft Results for 2002 

The windblown dust model was implemented using the latest MM5 meteorological data (Refs) 

for both the 36-km and 12-km regional modeling domains (Figures 1-1 and 1-2, respectively). 

For the preliminary model runs, agricultural adjustments were not applied in the Eastern states 

due to a lack of detailed information concerning crop calendars and agricultural practices. Since 

these initial model runs, additional data for the Eastern states was assembled and applied for the 

updated 2002 model runs. Note that the updated agricultural information for the East were not as 

detailed as for the Western US (WRAP and CENRAP states), so there are still likely some overestimations for this region o the modeling domain. 

In addition to updating the meteorology and applying agricultural adjustments in the Eastern US, 

fugitive dust transport fractions were also applied in the draft 2002 model runs. The dust 

transport fractions were used to model the effects of near-source removal of dust particles 

through impaction and filtration by surface cover. Other mechanisms of removal exist but are 

not as well understood or quantified. While the development of the transport fractions is based 

on the work of the EPA (Pace, 2003), the actual implementation within the context of the 

windblown dust model is slightly modified. The EPA has developed transport fractions by 

county based on the percentage of each landuse type within the county. For each type of 

landuse, a corresponding transport fraction is specified. County-level fractions are then 

developed based on weighted sums by landuse type. These fractions are then applied to the dust 

emissions at the county level. While this may appropriate in most of the Eastern US, where 

county tend to be rather small, it does not hold up well in the Western US. Therefore in the 

windblown dust model, these fractions are applied to each dust estimate at the as a function of 

the landuse as represented in the data used for the model. For both the 36-km and 12-k dust 

emissions estimate these are applied at a spatial resolution of 12-km, the same as the underlying 

landuse data used to drive the emission estimates within the model. 

The transport fractions as defined by the EPA were mapped to the landuse and dust categories 

considered in the WRAP windblown dust model. In some cases, averages between the landuse 

classes defined by the EPA data and those defined by the WRAP WB Dust model were 

necessary. The dust categories and assigned transport fractions as used in the draft 

implementation were presented in Table 3-11. 

The draft dust emissions estimates obtained with the revised estimation methodology for the both 

36-km and 12-km WRAP regional modeling domains were presented and discussed in the Draft 

Final Report (Mansell, et al., 2005). In all cases, the assumptions associated with Scenario b, as 

discussed above, were used. Model runs were made both with and without application of the 

transport fractions in order to investigate the effects of near source removal of dust emissions. 

The 2002 draft results are briefly summarized below. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-3 

The draft results of the windblown dust model for the 36-km modeling domain are presented 

below. Figure 4-1 displays the state-level total PM10 dust emissions both with and without the 

application of the transport Fractions (TF). As expected, application of the TF reduces the 

resulting dust emissions. The effects of the transport fractions vary due to their dependence on 

land use types. Figure 4-2 displays these results normalized by land area. Evaluation of the 

normalized emissions allows a direct comparison between the different domain resolutions and 

across states with varying land cover characteristics. The largest contributions to the total PM10 

emissions are seen to be from states with a large percentage of agricultural lands and brush- and 

grass-lands. 

Annual WB Dust PM10 Emissions

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

 Alabama  Arizona  Arkansas  California  Colorado  Connecticut  Delaware  D. of Columbia Florida  Georgia  Idaho  Illinois  Indiana  Iowa  Kansas  Kentucky  Louisiana  Maine  Maryland  Massachusetts  Michigan  Minnesota  Mississippi  Missouri  Montana  Nebraska  Nevada  New New Jersey  New Mexico  New York  North Carolina North Dakota  Ohio  Oklahoma  Oregon  Pennsylvania  Rhode Island  South Carolina South Dakota  Tennessee  Texas  Utah  Vermont  Virginia  Washington  West Virginia  Wisconsin  Wyoming 

State

tons/year

36-km

36-km w/ TF

Figure 4-1. 2002 Annual PM120 WB Dust PM10 emissions by state. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-4 

Annual WB Dust PM10 Emissions

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

 Alabama  Arizona  Arkansas  California  Colorado  Connecticut  Delaware  D. of Columbia Florida  Georgia  Idaho  Illinois  Indiana  Iowa  Kansas  Kentucky  Louisiana  Maine  Maryland  Massachusetts  Michigan  Minnesota  Mississippi  Missouri  Montana  Nebraska  Nevada  New New Jersey  New Mexico  New York  North Carolina North Dakota  Ohio  Oklahoma  Oregon  Pennsylvania  Rhode Island  South Carolina South Dakota  Tennessee  Texas  Utah  Vermont  Virginia  Washington  West Virginia  Wisconsin  Wyoming 

State

tons/year/km^2

36-km

36-km w/ TF

Figure 4-2. Normalized 2002 Annual PM120 WB Dust PM10 emissions by state.

Figure 4-3 presented the results from both the 36-km and 12-km model runs for the WRAP 

states. Results with and without application of transport fractions are displayed. Figure 4-4 

presents the corresponding results normalized by land area. As can be see, the emission 

estimates at 12-km resolution are greater than those at 36-km resolution for all states. The 

reductions due to the application of transport fractions are also displayed. Application of the 

model at 36-km resolution, requires the specification of wind speeds at 36-km, but the 

underlying landuse, and soils, data are available at 12-km resolution. At 12-km resolution, the 

meteorological model is likely to capture more small-scale features and higher wind speeds, 

leading to higher dust emission estimates than at 36-km resolution. In addition to wind speed 

effects, the higher resolution land use and matching resolution of the wind speeds for the 12-km 

model runs contributes to much of the differences seen between these two cases. For example, at 

36-km resolution, the same wind speed must be applied to all land use types within the nine 12-

km cells that make up the region covered by the 36-km grid cells. At 12-km, the land use and 

wind speeds are match with respect to spatial resolution and geographic regions, so there are 

cases where different wind speeds are applied to different lands use types (each with their own 

erosion potential) resulting in the differences seen between the 36-km and 12-km model 

estimates.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-5 

Annual WB Dust PM10 Emisisons

0

100,000

200,000

300,000

400,000

500,000

600,000

 AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

tons/year

36-km 

36 km w/ TF

12-km

12-km w/ TF

Figure 4-3. 2002 Annual WB dust PM10 emissions for WRAP states. 

Annual WB Dust PM10 Emissions

0.0

0.5

1.0

1.5

2.0

2.5

3.0

 AZ CA CO ID MT NV NM ND OR SD UT WA WY

State

tons/yr/km^2

36-km

36-km w/ TF

12-km

12-km w/ TF

Figure 4-4. Normalized 2002 Annual WB dust PM10 emissions for WRAP states. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-6 

The dependence of the estimated PM10 dust emissions on land use types, or dust categories, is 

displayed in Figure 4-5, which presents the results at 36-km resolution for each WRAP state by 

dust category. The relative contributions by land use type for each state can be seen and 

provides some insight into the relative magnitude of the predicted emissions across each state 

within the WRAP region. 

Annual WB Dust PM10 Emissions 

WRAP States

0

100,000

200,000

300,000

400,000

500,000

600,000

 Arizona  California  Colorado  Idaho  Montana  Nevada  New Mexico  North Dakota  Oregon  South Dakota  Utah  Washington  Wyoming 

State

tons/yr

Barren lands

Shrublands

Grasslands

Agricultural lands

Figure 4-5. 2002 Annual 36-km PM10 emissions by dust category – with TF applied 

Figures 4-6 presents the normalized predicted dust emissions for the 36-km model runs by state 

and land use type. The variation across the region as a function of the dust category, or land use 

type, can be seen. 

Corresponding results on the 12-km modeling domain are presented in Figures 4-7 and 4-8, 

which display the absolute magnitude of predicted PM10 dust emissions as a function of landuse 

for each state, and normalized by land area, respectively. Displayed in this way, the relative 

contributions to total dust emission can be seen as well as the variation across the domain due to 

varying regions of high winds, differing soil types and regions prone to large amounts of annual 

precipitation. 

Comparison of the results of the 36-m and 12-km model runs presented in these figures, illustrate 

the effects of spatial resolution on the estimated emissions. The differences are due primarily to 

the higher resolution of the meteorology, as noted previously. The effect of applying the 12-km 

wind fields with the underlying 12-km surface characteristics (land use and soil textures) is seen 

to be an overall increase in estimated emissions for all states within the WRAP region. 

The results of the model runs with and without application of the transport fractions and at 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-7 

resolutions of 36- and 12-km are summarized in Figure 4-9, which presents the total annual 

PM10 dust emissions by land use type for all WRAP states. 

Annual WB Dust PM10 Emissions 

WRAP States

0

2

4

6

8

10

12

 Arizona  California  Colorado  Idaho  Montana  Nevada  New Mexico  North Dakota  Oregon  South Dakota  Utah  Washington  Wyoming 

State

tons/yr/km^2

Barren lands

Shrublands

Grasslands

Agricultural lands

Figure 4-6. Normalized 2002 annual 36-km PM10 emissions by dust category – with TF applied
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-8 

Annual WB Dust PM10 Emissions 

WRAP States

0

100,000

200,000

300,000

400,000

500,000

600,000

 Arizona  California  Colorado  Idaho  Montana  Nevada  New Mexico  North Dakota  Oregon  South Dakota  Utah  Washington  Wyoming 

State

tons/yr

Barren lands

Shrublands

Grasslands

Agricultural lands

Figure 4-7. 2002 annual 12-km PM10 emissions by dust category – with TF applied 

Annual WB Dust PM10 Emissions 

WRAP States

0

2

4

6

8

10

12

 Arizona  California  Colorado  Idaho  Montana  Nevada  New Mexico  North Dakota  Oregon  South Dakota  Utah  Washington  Wyoming 

State

tons/yr/km^2

Barren lands

Shrublands

Grasslands

Agricultural lands

Figure 4-8. Normalized 2002 annual 12-km PM10 emissions by dust category – with TF applied
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-9 

Annual WB Dust PM10 Emissions 

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

Total Agricultural

lands

Grasslands Shrublands Barren lands

Category

tons/Year

36-km

36-km w/ TF

12-km

12-km w/ TF

Figure 4-9. Comparison of total 2002 annual PM10 emissions by dust category for the WRAP 

states.

The monthly variation of total PM10 dust emissions for all WRAP states combined is presented 

in Figure 4-10. The seasonal variation of predicted dust emissions can be discerned from this 

display. The dust emissions tend to peak in the spring months due to generally higher winds 

throughout most of the domain. In addition, for agricultural areas this time period corresponds to 

the spring planting when the crop canopy is relative small or absent. A second, smaller peak 

occurs in the fall for similar reasons. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-10 

Monthly WB Dust PM10 Emissions

WRAP States

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

tons/month

36-km

36-km w/TF

12-km

12-km w/TF

Figure 4-10. Comparison of monthly PM10 dust emissions. 

Final 2002 Windblown Dust PM10 Emission Inventory 

The final 2002 windblown PM dust emission inventory was generated using the reservoir 

assumptions as used in the draft inventory and incorporating updated LULC data, transport 

fractions and a fine fraction of PM10 dust of 0.1, as described previously. Figure 4-11 presents 

the 2002 annual PM10 dust emission estimates for all states in the US, based on the results of the 

36-km model simulations. Figure 4-12 presents these results for the WRAP states only. Note 

that the model was only run with the application of transport factors, so no comparison of these 

results without transport factors is available. Figure 4-13 presents the results of the 12-km model 

runs for the WRAP states. Also included in Figure 4-13 are the results of the draft inventory 

based on the 1992 NLCD LULC data. 

 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-11 

WB Dust PM10 Emissions 

WRAP 36-km Domain

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

 AL

 AR

 AZ

 CA

 CO

 CT

 DC

 DE

 FL

 GA

 IA

 ID

 IL

 IN

 KS

 KY

 LA

 MA

 MD

 ME

 MI

 MN MO

 MS

 MT

 NC ND

 NE

 NH

 NJ

 NM

 NV

 NY

 OH OK OR

 PA

 RI

 SC SD

 TN

 TX

 UT VA

 VT

 W

 WI

 W

 WY

State

PM10 (tons/year)

Figure 4-11. PM10 WB dust emissions by state for the 36-km WRAP domain. 

WB Dust PM10 Emissions 

WRAP 36-km Domain

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

 AZ

 CA

 CO

 ID

 MT

 ND

 NM

 NV

 OR

 SD

 UT

 WA

 WY

State

PM10 (tons/year)

Figure 4-12. PM10 WB dust emissions by state for the WRAP states.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-12 

Comparison of WB Dust PM10 Emissions

WRAP 12-km Domain

0

100,000

200,000

300,000

400,000

500,000

600,000

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year)

1992 NLCD

2000 NALC

Figure 4-13. Comparison of PM10 WB dust emissions by state for the WRAP states – 12-km 

model run.

Figure 4-14 presents a comparison of the windblown dust PM emissions on the WRAP 12-km 

modeling domain between the draft inventory (1992 NLCD) and the final inventory (2000 

NALC). Displayed are the total PM10 emissions and the split between fine and coarse PM dust 

for the two cases for the WRAP states only. The Final inventory uses a PM10/PM2.5 ratio of 

0.9/0.1 as suggested by the work of MRI. 

The variation of PM10 dust emission by land use category is displayed in Figure 4-15. These 

results are based on the 36-km model runs and are displayed by state for the conterminous US. 

Figure 4-16 present the results of the 12-km annual model runs by WRAP state and land use 

category. The corresponding results, normalized by land area for each contributing land type are 

displayed in Figure 4-17. The variation of PM10 dust emissions by land type across each of the 

WRAP states can be seen and appears to be consistent with the 2000 NALC data used in the 

model runs. 

Figures 4-18 though 4-21 display comparisons of the 12-km PM10 dust emissions for the WRAP 

states using the 1992 NLCD and the 2000 NALC normalized by each of the four land types 

contributing to the windblown dust emissions. The differences seen are due primarily to the 

differences in land use distributions across each state. Note that differences in the fugitive dust 

transport fractions are also reflected in these displays. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-13 

Comparison of 2002 Annual WB Dust PM Emissions 

WRAP 12-km Domain

0

100,000

200,000

300,000

400,000

500,000

600,000

Arizona

California

Colorado

Idaho

Montana

Nevada

New Mexico

North Dakota

Oregon

South Dakota

Utah

Washington

Wyoming

State

PM (tons/year)

PMC (Pre02d) PMF (Pre02d)

PMC (Base02a) PMF (Base02a)

Figure 4-14. Comparison of PM WB dust emissions by state for the WRAP states – 12-km 

model run.

2002 Annual WRAP 36-km WB Dust

2000 LULC (TFs Applied)

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

 AL

 AR

 AZ

 CA

 CO

 CT

 DC

 DE

 FL

 GA

 IA

 ID

 IL

 IN

 KS

 KY

 LA

 MA

 MD

 ME

 MI

 MN

 MO

 MS

 MT NC ND

 NE

 NH

 NJ

 NM

 NV

 NY

 OH OK OR

 PA

 RI

 SC SD

 TN

 TX

 UT

 VA

 VT

 W

 WI

 W

 WY

States

PM10 (tons/year)

Agricultural Grasslands

Shrublands Barren

Figure 4-15. 2002 annual 36-km PM10 emissions by dust category and state.
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2002 Annual WRAP 12-km WB Dust 

2000 NALC 

0

100,000

200,000

300,000

400,000

500,000

600,000

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

States

PM10 (tons/year)

Agricultural Grasslands

Shrublands Barren

Figure 4-16. 2002 annual 12-km PM10 emissions by dust category for WRAP states.

2002 Annual WRAP 12-km WB Dust 

 2000 NALC

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year/km^2)

Agricultural Grasslands

Shrublands Barren

Figure 4-17. 2002 annual 12-km normalized PM10 emissions by dust category for WRAP 

states.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-15 

Comparison of Agricultural Dust Emissions 

WRAP 12-km Domain

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year/km^2)

1992 NLCD

2000 NALC

Figure 4-18. Comparison of 2002 annual 12-km PM10 normalized agricultural emissions for 

WRAP states.

Comparison of Grassland Dust Emissions 

WRAP 12-km Domain

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year/km^2)

1992 NLCD

2000 NALC

Figure 4-19. Comparison of 2002 annual 12-km PM10 normalized grassland emissions for 

WRAP states.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-16 

Comparison of Shrubland Dust Emissions 

WRAP 12-km Domain

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year/km^2)

1992 NLCD

2000 NALC

Figure 4-20. Comparison of 2002 annual 12-km PM10 normalized shrubland emissions for 

WRAP states.

Comparison of Barren Land Dust Emissions 

WRAP 12-km Domain

0.0

1.0

2.0

3.0

4.0

5.0

6.0

ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

State

PM10 (tons/year/km^2)

1992 NLCD

2000 NALC

Figure 4-21. Comparison of 2002 annual 12-km PM10 normalized barren land emissions for 

WRAP states.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-17 

The draft (Pre02d) and final (Base02a) windblown dust PM10 emissions are summarized by 

landuse category in Table 4-1 and 4-2. Table 4-1 presents the 2002 annual PM10 dust emissions 

for the conterminous US and for the WRAP states based on the results of the 36-km modeling 

runs. Table 4-2 provides a comparison of the 2002 annual PM10 emissions for the WRAP states 

based on the 12-km modeling results. For the WRAP states, both the 36-km and 12-km results 

for the Base02a inventory are slightly larger than for the Pre02d inventory. For the 36-km 

results across the conterminous US, the Pre02d results are somewhat higher than for the Base02a 

inventory. Note also that for both the pre02d and Base02a inventories, the 12-km modeling 

results for the WRAP states are significantly higher that the results of the 36-km model runs. 

Figure 4-22 presents a comparison of the 2002 annual total PM10 emissions by landuse category 

for the Base02a and Pre02d inventories based on the results on the WRAP 12-km modeling 

domain. 

Table 4-1. Summary of 36-km WB dust PM10 emission. 

2002 Annual WB Dust PM10 Emissions 

 Conterminous US WRAP States 

Landuse Category 

Base02a 

(2000 NALC)

Pre02d 

(1992 NLCD)

Base02a 

(2000 NALC) 

Pre02d 

(1992 NLCD) 

Agricultural 1,847,407 3,316,276 346,870 803,581

Grasslands 664,722 620,087 231,033 333,982

Shrublands 1,942,422 939,528 1,342,740 537,539

Barren Lands 145,878 101,823 44,784 80,199

Total 4,600,431 4,977,715 1,965,427 1,755,301

Table 4-2. Summary of 12-km WB dust PM10 emissions for the WRAP States. 

2002 Annual WBD PM10 Emissions (WRAP States) 

Landuse Category 

Base02a 

(2000 LULC) 

Pre02b 

 (1992 LULC) 

Agricultural 393,306 988,264

Grasslands 308,099 411,595

Shrublands 1,950,862 820,937

Barren 79,435 143,292

Total 2,731,702 2,364,089
May 2006 

 

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2002 Annual WB Dust PM10 Emissions 

WRAP 12-km Domain

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

Agricultural Grasslands Shrublands Barren Total

Category

PM10 (tons/year)

1992 NLCD

2000 NALC

Figure 4-22. Comparison of total 2002 annual PM10 emissions for WRAP states

The monthly variation of total PM10 dust emissions for the Base02a inventory is presented in 

Figure 4-23 by landuse category and state for the WRAP states. As expected, the dust emissions 

peak in the late spring and early summer months and are dominated by emissions from 

shrublands. Table 4-3 presents the monthly PM10 dust emissions by WRAP states and landuse 

category for the 12-km resolution Base02a emission inventory. 

Monthly WB Dust PM10 Emission by Dust Category 

WRAP States

0

100,000

200,000

300,000

400,000

500,000

600,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

PM10 (tons)

Agricultural Grasslands

Shrublands Barren

Figure 4-23. Monthly PM10 WB dust emissions by source category across all WRAP states.
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 Table 4-3. Monthly WB dust PM10 emissions by state and dust category – 12km WRAP States. 

Total WB dust emissions for WRAP States - 12-km (tons) 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Annua2002 

Arizona 5990 7507 15501 19125 17532 14198 8631 5471 4638 2503 13215 3143 117California 7239 10602 21951 24497 24159 26122 19628 11702 7080 5715 15762 15641 190Colorado 4037 9853 14335 27876 32074 33438 13254 15237 14697 6970 4967 3440 180Idaho 1790 1612 4512 10571 19082 9239 8877 9272 3924 3568 4163 8521 85Montana 31764 22689 25070 72935 92373 55727 34727 38549 27876 20628 34108 10248 466Nevada 3923 6895 16169 26114 34612 27483 13273 13853 8769 4727 14201 15001 185New Mexico 16418 21393 41485 37534 38875 33358 14146 10663 10127 8401 16154 9736 258North Dakota 6178 11370 16068 34463 46003 17872 13037 22409 14138 8821 7455 569 198Oregon 15480 9066 14865 17388 23593 18803 12878 10571 5381 5238 12647 39669 185South Dakota 17179 43529 16124 84461 108224 60298 30298 58755 35530 27294 28280 11753 521Utah 1331 2998 11765 21707 32354 23097 15390 17781 7050 2192 7138 6980 149Washington 16712 4933 11583 13768 12133 8269 9015 3206 2694 1531 4274 12848 100Wyoming 2424 2433 5315 17485 22412 11583 5832 10003 4224 4580 3811 2300 92WRAP Total 130465 154878 214744 407924 503427 339486 198984 227473 146128 102170 166176 139847 2731

Agricultural land WB dust emissions for WRAP States - 12-km (tons) 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Annua2002 

Arizona 7 9 36 44 61 65 58 16 17 18 18 4 California 560 958 1819 1014 1425 3005 1384 907 838 547 1521 4224 18Colorado 188 416 518 1200 1220 1346 754 669 607 402 232 94 7Idaho 406 298 807 971 1324 568 639 609 355 366 444 1137 7Montana 1521 863 1185 3510 5090 2585 1181 1900 2106 1419 1487 368 23Nevada 5 14 29 50 64 63 26 27 20 7 23 35 New Mexico 134 186 404 625 636 606 236 315 449 294 175 50 4North Dakota 3744 5580 8454 20959 27734 8953 6495 12618 9323 6054 4584 520 115Oregon 1469 1082 1374 542 364 531 514 499 372 834 1610 3188 12South Dakota 3402 20903 9188 36944 37510 19350 7799 13951 13231 14414 11042 3822 191Utah 6 8 55 99 121 73 65 68 36 20 33 16 Washington 1791 722 1290 1341 1105 722 694 324 278 156 776 1705 10Wyoming 41 48 49 153 206 116 68 116 71 71 63 33 1WRAP Total 13276 31087 25208 67452 76860 37983 19912 32018 27703 24601 22008 15197 393
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 Table 4-3. (Continued) Monthly WB dust PM10 emissions by state and dust category – 12km WRAP States. 

Grassland WB dust emissions for WRAP States - 12-km (tons) 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Annua2002 

Arizona 63 83 378 337 296 191 130 58 43 33 74 42 1California 58 342 1122 1111 948 1021 610 156 142 220 639 1805 8Colorado 501 1432 1275 3882 3472 3135 710 1136 635 580 613 200 17Idaho 48 50 175 938 1728 599 597 609 145 55 221 491 5Montana 6163 4675 5710 12069 20623 9454 4834 5647 3599 1437 4482 1561 80Nevada 33 85 213 357 465 238 118 94 49 12 219 265 2New Mexico 878 1286 2446 2104 2279 1471 413 398 490 233 672 279 12North Dakota 610 1499 2724 3656 5431 2667 744 2581 715 220 372 0 21Oregon 1435 828 1056 829 832 414 339 86 118 41 683 4048 10South Dakota 4243 6088 2789 12780 22656 6659 1455 10393 2686 3105 3496 901 77Utah 76 112 455 1329 1462 1036 521 330 129 40 259 130 5Washington 1491 365 854 1143 698 259 167 23 96 138 164 1207 6Wyoming 1604 1691 4278 12257 14920 6765 3144 5434 1421 2364 2473 1607 57WRAP Total 17203 18537 23474 52791 75809 33908 13783 26944 10269 8478 14366 12537 308

Shrubland WB dust emissions for WRAP States - 12-km (tons) 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Annua2002 

Arizona 5705 7180 14071 17085 15836 13029 7598 5111 4300 2340 12635 2957 107California 6064 8343 16762 19781 19301 19979 16358 9882 5560 4542 12229 9169 147Colorado 3345 7998 12516 22735 27291 28888 11767 13410 13426 5974 4117 3143 154Idaho 1304 1250 3480 8538 15831 7998 7555 7981 3385 3119 3440 6807 70Montana 24028 17119 18143 57248 66530 43609 28654 30964 22127 17743 28099 8298 362Nevada 3759 6577 15401 24779 33049 26201 12594 13304 8367 4612 13548 14248 176New Mexico 14541 18999 36805 32989 34029 29599 12729 9470 8768 7437 14616 8920 228North Dakota 1807 4268 4847 9763 12676 6165 5756 7151 4054 2536 2488 47 61Oregon 12044 6920 12015 15751 22108 17610 11830 9836 4804 4324 10141 31250 158South Dakota 9418 16246 4066 34096 47226 33699 20806 33932 19280 9566 13569 6938 248Utah 936 2563 9707 17749 26467 18623 12476 14369 5991 2043 5871 5090 121Washington 12595 3621 9065 10730 9741 6943 7888 2810 2251 1187 2959 9147 78Wyoming 778 689 889 4775 6954 4495 2511 4272 2634 2068 1264 660 31WRAP Total 96325 101772 157768 276018 337039 256839 158520 162492 104948 67490 124975 106675 1950
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 Table 4-3. (Concluded) Monthly WB dust PM10 emissions by state and dust category – 12km WRAP States. 

Barren land WB dust emissions for WRAP States - 12-km (tons) 

State Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Annua2002 

Arizona 215 235 1016 1660 1339 913 845 285 278 111 488 139 7California 556 959 2247 2591 2486 2117 1276 757 539 407 1374 442 15Colorado 2 7 26 59 91 68 23 23 29 14 5 3 Idaho 33 14 49 124 199 74 86 74 40 28 58 86 Montana 51 32 32 108 130 79 58 39 44 30 40 21 Nevada 126 219 526 929 1034 981 535 428 333 97 410 452 6New Mexico 864 921 1830 1816 1931 1682 768 481 420 438 692 486 12North Dakota 18 23 43 85 162 88 42 59 46 12 11 2 Oregon 532 236 419 267 289 248 196 151 86 40 213 1182 3South Dakota 115 292 81 642 832 590 238 479 332 209 173 90 4Utah 313 315 1548 2529 4305 3366 2328 3013 894 89 975 1743 21Washington 834 225 375 554 589 344 266 49 70 50 375 789 4Wyoming 2 5 99 300 331 206 109 181 98 76 11 0 1WRAP Total 3661 3483 8294 11662 13719 10756 6769 6019 3208 1601 4826 5438 79
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-22 

Spatial Distributions 

The spatial distributions of modeled annual PM10 dust emissions for the 36-km modeling 

domain is displayed in Figure 4-24. Figures 4-25 through 4-28 display these results by landuse 

category for agricultural lands, grasslands, shrublands and barren lands, respectively. These 

results are based on the 2000 NALC data and include the application of the updated fugitive dust 

transport fractions. As seen in the figures, the dust emissions are highest in agricultural areas in 

the central portion of the US and in parts of the West dominated by shrub- and grasslands. 

The results on the 12-km WRAP modeling domain are shown in Figure 4-29. The monthly 

variation of predicted dust emissions for the 12-km domain are presented in Figures 4-30. The 

highest emissions are seen to occur during the spring season when winds are typically stronger 

throughout the region and agricultural crops have recently been planted. Lower overall 

emissions are predicted in the summer months corresponding to higher crop canopy cover and, in 

the southwest, increased periods of precipitation. 

The gridded windblown PM10 dust emissions for the 12-km modeling domain by landuse 

category are presented in Figures 4-31 through 4-34. County-level modeling results are 

displayed in Figures 4-35 through 4-39, which present the total PM10 dust emissions, as well as 

the dust emissions from each of the contributing landuse categories. Figure 4-40 through 4-44 

display these results normalized by land area for each of the landuse categories. Presentation of 

the results in terms of normalized emissions, rather than absolute magnitudes, allows a direct 

comparison of the results at the different spatial resolutions in addition to illustrating the 

variation ny landuse for each county across the region. 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-23 

Figure 4-24. Spatial distribution of 2002 annual gridded PM10 WB dust emissions. 

Figure 4-25. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

agricultural lands.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-24 

Figure 4-26. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

grasslands.

Figure 4-27. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

shrublands.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-25 

Figure 4-28. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from barren 

lands.

Figure 4-29. Spatial distribution of 2002 annual gridded PM10 WB dust emissions for the 

WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-26 

Figure 4-30. Spatial distribution of 2002 monthly gridded PM10 WB dust emissions for the WRAP 12-km 

domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-27 

Figure 4-30. (Concluded) Spatial distribution of 2002 monthly gridded PM10 WB dust emissions for the 

WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-28 

Figure 4-31. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

agricultural lands for the WRAP 12-km domain.

Figure 4-32. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

grasslands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-29 

Figure 4-33. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from 

shrublands for the WRAP 12-km domain.

Figure 4-34. Spatial distribution of 2002 annual gridded PM10 WB dust emissions from barren 

lands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-30 

Figure 4-35. Spatial distribution of 2002 annual county-level PM10 WB dust emissions for the 

WRAP 12-km domain.

Figure 4-36. Spatial distribution of 2002 annual county-level PM10 WB dust emissions from 

agricultural lands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-31 

Figure 4-37. Spatial distribution of 2002 annual county-level PM10 WB dust emissions from 

grasslands for the WRAP 12-km domain.

Figure 4-38. Spatial distribution of 2002 annual county-level PM10 WB dust emissions from 

shrublands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-32 

Figure 4-39. Spatial distribution of 2002 annual county-level PM10 WB dust emissions from 

barren lands for the WRAP 12-km domain.
May 2006 

 

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Figure 4-40. Spatial distribution of 2002 annual normalized county-level PM10 WB dust 

emissions for the WRAP 12-km domain.

Figure 4-41. Spatial distribution of 2002 annual normalized county-level PM10 WB dust 

emissions from agricultural lands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-34 

Figure 4-42. Spatial distribution of 2002 annual normalized county-level PM10 WB dust 

emissions from grasslands for the WRAP 12-km domain.

Figure 4-43. Spatial distribution of 2002 annual normalized county-level PM10 WB dust 

emissions from shrublands for the WRAP 12-km domain.
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 4-35 

Figure 4-44. Spatial distribution of 2002 annual normalized county-level PM10 WB dust 

emissions from barren lands for the WRAP 12-km domain.
May 2006 

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5. MODEL PERFORMANCE EVALUATION 

The 2002 wind blown dust emissions were evaluated different ways to assess their 

reasonableness and accuracy. The results of an initial evaluation analysis are presented in this 

section, while a more refined analysis may occur at a later date. One of the challenges in this 

evaluation is that measurements generally do not distinguish between wind blown dust and other 

forms of dust (e.g., from paved and unpaved roads). Moreover, measurements of coarse matter 

(CM), that are typically assumed to be dust, can contain other non-dust compounds (e.g., sulfate 

and nitrate compounds). 

The IMPROVE monitoring network measures a fine Soil and Coarse Matter (CM) species that 

are generally assumed to be due to dust emissions. The fine Soil is built up of mineral elements 

so in the absence of interference with fires is probably a fairly good estimate of the fine dust 

contribution. The CM measurement, on the other hand, is the difference of the PM10 and PM2.5 

mass measurements so can contain other compounds besides dust. 

WRAP is performing an "Assessment of the Major Causes of Dust-Resultant Haze in the 

WRAP" (Etyemezian et al., 2004) that will characterize dust events at IMPROVE monitors for 

2001-2003. This study is using extinction due to fine Soil (FS) and CM to define extinction due 

to dust: Bdust = [FS] + 0.6 [CM]. Dust events at the WRAP IMPROVE monitors are being 

characterized as follows: 

• Transcontinental Events; 

• Regional Windblown Dust Events; 

• Wildfire-Related Events; 

• Local Windblown Dust; 

• Other Events; and 

• Unknown Source. 

This analysis will be very valuable in the evaluation of the wind blown and other dust emissions. 

However, initial results from this study have been delayed; when completed they would provide 

additional data and information to conduct the more refined evaluation of the windblown dust 

modeling results. Thus, we have broken up the evaluation of the windblown dust emission 

estimates into an initial analysis, presented herein, that examines the dust emissions for 

reasonableness, and a more refined analysis to be completed at a later date, at the discretion of 

the WRAP DEJF, that would make use of the Etyemezian and co-workers WRAP dust 

characterization study. 

The initial model performance first examines the occurrence of model predicted wind blown dust 

emissions in the vicinity of an IMPROVE monitor with the measured IMPROVE dust extinction 

(Bdust) to determine whether there is a correlation between the two. It was initially planned to 

conduct an analysis using the CMAQ modeling system to investigate the impact of the predicted 

wind blown dust emissions on the modeled air quality across the WRAP region. The CMAQ 

model has been modified to separately track fine and coarse windblown and other dust 

emissions. We performed a preliminary operational evaluation of the CMAQ dust and other fine 

and coarse PM estimates against the IMPROVE Soil and CM measurements using the 

preliminary 2002 predicted dust emissions 
May 2006 

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Comparisons of Wind Blown Dust Emissions with the Occurrence of Enhanced "Dust" at 

IMPROVE Monitors 

For each 24-hour IMPROVE sample during 2002, the IMPROVE Coarse Mass and fine Soil 

were matched with predicted 12-km resolution windblown dust emissions for the concurrent day 

in the grid cell centered on the IMPROVE monitor and the surrounding eight grid cells. Scatter 

plots of PM dust emissions and IMPROVE observed concentrations were generated to identify 

the occurrence of: 

(1) zero wind blown dust and near-zero Bdust (measured and modeled nonoccurrence of dust); 

(2) enhanced wind blown dust with near-zero Bdust (no measured dust with wind blown dust 

in vicinity of monitor); 

(3) no windblown dust with elevated Bdust (measured dust without modeled windblown dust); 

and 

(4) enhanced windblown dust emissions and Bdust (enhanced models and measured dust). 

These classifications allow a qualitative evaluation of the windblown dust emissions. It is 

expected that type (1) would occur most frequently, especially at more northern sites and during 

the winter months, and there should be a lot of agreement between the model and measurements. 

Type (2) may not indicate a problem with the windblown dust model, as the dust cloud may not 

have impacted the monitor. Likewise, the elevated Bdust in type (3) may be due to other sources 

besides windblown dust. Type (4) may provide the most information, especially if a positive 

correlation can be established between windblown dust emissions and Bdust. If the above 

analysis provides useful information it would be repeated for the fine and coarse components of 

the measured (FS and CM) "dust" and modeled windblown dust emissions. 

To illustrate the spatial distribution of PM concentrations across the US, coarse mass 

concentrations were calculated and displayed using the VIEWS website. Figures 5-1, 5-2 and 5-

3 display the 2002, 2003 and 2004 coarse mass from the IMPROVE ambient monitoring 

network, respectively. Elevated observations of coarse mass are general confined to the 

southwest US in 2002 while increased concentrations are apparent throughout the central states 

in 2003 and 2004. The 2003 and 2004 monitoring networks were significantly increased in the 

central US region from the 2002 network and show these elevated concentrations that were not 

apparent in 2002. The location of the ambient monitors is also shown in these figures. These 

monitoring locations were paired in space and time with the predicted windblown dust emission 

estimates from the 12-km model runs in order to assess the reasonableness of the estimated dust 

emissions. 
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 5-3

Figure 5-1. 2002 Annual Coarse Mass from IMPROVE monitoring network. 

Figure 5-2. 2003 Annual Coarse Mass from IMPROVE monitoring network.
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 5-4

Figure 5-3. 2004 Annual Coarse Mass from IMPROVE monitoring network.

An Excel macro was developed to generate the scatter plots for all paired of predicted dust and 

observed concentrations within the 12-km WRAP modeling domain. Selected results are 

presented in Figures 5-4 through 5-19. A complete set of scatter plots for all sites across the 

domain are presented in Appendix B. In each display, the grid cell in which the monitor is 

located is shown in the center (labeled (0,0)) with the surrounding cells displayed relative to this 

grid cell. The predicted dust emissions in each of the surrounding model grid cells is paired with 

the monitored value to provide some indication, or insight, into the directional aspects of the win 

blown dust emissions. The monitors selected for presentation include those from a variety of 

areas across the western portion of the modeling domain. The results of the analysis are 

normalized by the maximum values for both the observed concentrations and predicted 

emissions and presented in terms of percentages. 

The results of the analysis indicate a wide range of modeled dust emissions versus observed 

concentrations. One of the difficulties in this analysis is related to what is considered elevated, 

or enhanced, observed concentrations. As the monitors do not distinguish among the sources of 

observed dust it is difficult to assess the results in a quantitative manner. Qualitatively, the 

results show many instances where the model predicts windblown dust emissions and enhanced 

concentrations at the monitors but it is still uncertain whether the monitored values are in fact 

due to other dust sources. In addition, as the dust model does not take into consideration wind 

direction, it is possible that even for cases where windblown dust emissions are predicted, they 

may not impact the monitor (i.e., case (2) above). Including the analysis for the grid cells 

surrounding the monitor, where the predicted emission in the surrounding cells are compared 

with the monitored values in the cell in which the monitor is located provides some additional 

insight into the evaluation. 

In general, the results seem to indicate that in many cases, windblown dust emissions are 

predicted for the same time periods and locations as enhanced measurements. Care should be 

taken in interpreting these displays, in particular the magnitude of the predicted emissions in 

relation to the observed ambient concentrations. This analysis is strictly qualitative, and serves 

to provide some measure of the reasonableness of the ability of the windblown dust emission 
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 5-5

model to capture the spatial and temporal variation across the regional modeling domain. A 

review of the displays presented in Appendix B provides an indication of the variation of the 

modeling results across the western states. 

While it may appear that the model does a fairly decent job in many places, the results may still 

be misleading. Likewise, for cases that appear to show no correlation may not necessarily 

indicate a problem with the model predictions. If the analyses are restricted to monitors and time 

periods for which significant windblown dust events are known to have occurred, a more 

definitive assessment of the model can be made. A more comprehensive evaluation of these 

results may provide further insight to the overall performance of the windblown dust model. 
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Saguaro West, AZ – SOIL 

SAWE(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

SAWE(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

SAWE(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

SAWE(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

SAWE(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

SAWE(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-4. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Saguaro West, A
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

 Saguaro West, AZ – SOIL 

SAWE(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

SAWE(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

SAWE(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

SAWE(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

SAWE(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

SAWE(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-5. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Saguaro West, AZ IM
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Agua Tibia, CA – CM 

AGTI(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

AGTI(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

AGTI(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

AGTI(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

AGTI(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

AGTI(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-6. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Agua Tibia IMPR
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Agua Tibia, CA – SOIL 

AGTI(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

AGTI(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

A

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

AGTI(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

AGTI(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

A

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

AGTI(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

AGTI(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

A

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-7. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Agua Tibia IMPROVE 
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Medicine Lake, MT – CM 

MELA(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

MELA(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

MELA(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

MELA(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

MELA(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

MELA(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

M

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-8. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Medicine Lake IM
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Medicine Lake, MT – SOIL 

MELA(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

MELA(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

M

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

MELA(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

MELA(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

M

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

MELA(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

MELA(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

M

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-9. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Medicine Lake IMPR
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Starkey, OR – CM 

STAR(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

STAR(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

STAR(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

STAR(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

STAR(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

STAR(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-10. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Starkey IMPRO
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Starkey, OR – SOIL 

STAR(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

STAR(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

STAR(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

STAR(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

STAR(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

STAR(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-11. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Starkey IMPROVE m
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Theodore Roosevelt, ND – CM 

THRO(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

THRO(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

THRO(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

THRO(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

THRO(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

THRO(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

T

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-12. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Theodore Roos

monitor.
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Theodore Roosevelt, ND – SOIL 

THRO(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

THRO(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

T

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

THRO(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

THRO(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

T

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

THRO(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

THRO(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

T

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-13. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Theodore Roosevel
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Lostwood, ND - CM

LOST(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

LOST(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

LOST(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

LOST(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

LOST(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

LOST(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-14. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Lostwood IMPR
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Lostwood, ND – SOIL 

LOST(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

LOST(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

L

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

LOST(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

LOST(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

L

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

LOST(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

LOST(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

L

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-15. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Lostwood IMPROVE 
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Salt Creek, NM – CM 

SACR(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

SACR(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

SACR(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

SACR(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

SACR(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

SACR(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-16. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Salt Creek IMP
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Salt Creek, NM – SOIL 

SACR(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

SACR(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

SACR(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

SACR(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

SACR(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

SACR(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

S

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-17. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Salt Creek IMPROV
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Bridger Wilderness, WY – CM 

BRID(CM vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,1) Emission

BRID(CM vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

BRID(CM vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,0) Emission

BRID(CM vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,0) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

BRID(CM vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(-1,-1) Emission

BRID(CM vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized CM Concentration

Normalized pm10(0,-1) Emission

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-18. Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Bridger Wildernmonitor.
May 2006 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/2006 

Bridger Wilderness, WY – SOIL 

BRID(SOIL vs pm10(-1,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,1) Emission

BRID(SOIL vs pm10(0,1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,1) Emission

B

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,1) Emission

BRID(SOIL vs pm10(-1,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,0) Emission

BRID(SOIL vs pm10(0,0))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,0) Emission

B

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,0) Emission

BRID(SOIL vs pm10(-1,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(-1,-1) Emission

BRID(SOIL vs pm10(0,-1))

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 30% 40% 50% 60% 70% 80%

Normalized SOIL Concentration

Normalized pm10(0,-1) Emission

B

0%

10%20%30%40%50%60%70%80%

0% 10% 20% 3

Norm

Normalized pm10(1,-1) Emission

Figure 5-19. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Bridger Wilderness 
May 2006 

 

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc 5-22 

Operational Evaluation of the CMAQ Model 

In order to assess the impact of wind blown PM dust emissions on modeled air quality, the 

CMAQ modeled was applied both with and without the predicted wind blown dust emissions. 

CMAQ currently tracks all fine particulate that is not SO4, NO3, EC or OC as a single Species 

(A25). Similarly, all coarse PM is typically tracked as one species (ACORS) (note that there are 

other coarse species for sea salt and soil but they are not typically used). Therefore, CMAQ was 

modified to add two additional fine and coarse PM species, one set to track fine and coarse 

windblown dust emissions and the other set to track fine and coarse other dust emissions. This 

allows the separate accounting of the CMAQ other fine and coarse PM species into windblown 

dust, other dust and other primary fine and coarse PM emissions species. 

The modified CMAQ model was run for the months of April through May and the model 

estimates compared against the IMPROVE measures for fine Soil and CM. The model 

performance evaluation was performed for dust (windblown and other) species alone and with 

and without the windblown dust to determine whether the addition of the windblown dust 

emissions improve model performance. 

The spatial distribution of the differences in predicted Coarse Matter (CM) and fine Soil with 

and without the inclusion of the wind blown dust emissions are displayed in Figure 5-20. 

Differences in the monthly CM and Fine Soil are displayed. The differences due to the predicted 

dust PM emissions are generally small and are located in the regions corresponding to the 

majority of predicted wind blown dust. Large differences are apparent throughout the central 

US, the upper Midwest, and in the northern Plains states. The displays of differences by month 

highlight the seasonal variation of predicted windblown dust. 

Figure 5-21 displays the difference in predicted reconstructed deciview and extinction for the 

case with and without the windblown dust emissions for the months of April and May. As 

shown the differences are fairly small and confined to the regions for which the majority of 

windblown dust emissions are estimated. Note that these displays represent the monthly 

averages for the month. The differences for each day and hour of the simulation may be more or 

less than those depicted in the figures. 
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Figure 5-20. Spatial distribution of differences in CMAQ predicted monthly Fine Soil (left) and 

Coarse Mass (right) with and without windblown dust. 
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Figures 5-22 and 5-23 display scatter plots of predicted and measured fine Soil and CM, for each 

of the two months simulated, using only those IMPROVE sites in the WRAP states. 

The results of the CMAQ model runs for each of the months simulated with CMAQ, with and 

without the inclusion of windblown dust, are display together to illustrate the improvement or 

degradation of the modeled results. Note that all sites (within each specified region) and all days 

(within the specified month) are included in these displays and the corresponding statistics. In 

general, the model performance shows only slight changes, although there are some exceptions 

for particular months throughout the year. The results of the performance analysis for the 20% 

worst days alone may provide additional insight into these air quality impacts of the modeled 

windblown PM dust emissions. 

Figure 5-21. Spatial distribution of differences in CMAQ predicted monthly Deciview (left) and 

Extinction (right) with and without windblown dust. 
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Figure 5-22. Scatter plots of CMAQ predicted and IMPROVE observed Fine Soil for 

April-May 2002 for the WRAP states only. 

Figure 5-23. Scatter plots of CMAQ predicted and IMPROVE observed CM for AprilMay 2002 for the WRAP states only. 
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6. SUMMARY 

The major findings of the study, as well as various limitations of the modeling performed for this 

study, are summarized in this section. Recommendations are made with respect to 

improvements in data quality as well as enhancements and refinements of the estimation 

methodology. 

Results 

The Phase II estimation methodology for PM dust emissions from wind erosion, as described 

above, was implemented for calendar year 2002 on the 36-km ands 12-km WRAP regional 

modeling domains. A number of preliminary sensitivity simulations were performed to 

investigate the effects of the various assumptions regarding dust reservoir characteristics as well 

as the disturbance levels of the soils across the domain. Model runs were completed with and 

without the application of fugitive dust transport fractions in order to assess their impact on the 

resulting dust emission estimates. The final version of the windblown dust emission inventory 

was generated with the latest transport fractions. These transport fractions, as defined by the 

EPA, were calculated and applied within the emission estimation model based on the updated 

2000 LULC data. 

Based on a review of recent studies concerning dust emission from wind erosion, a general 

estimation methodology was developed. Emission rates were developed by soil type and 

stability. Land use/land cover was based initially on the 1992 NLCD database and used to 

determine the surface roughness lengths necessary in the estimation of threshold surface friction 

velocities. The final windblown PM dust emission inventory was developed using updated 

LULC data on the 2000 North America Land Characteristics (NALC) database. Wind speed, 

precipitation rates and soil temperatures were based on MM5 model simulation results at spatial 

resolutions of 12-km and 36-km. Although the overall estimation methodology was updated and 

improved upon from the Phase I methodology, as well as the draft Phase II methodology, a 

number of assumptions were required to implement the methodology as discussed in previous 

sections of this report. 

• Apply the transport fraction as developed by the EPA. This should be implemented from 

within the dust model to make use of the higher resolution of the land use data as 

opposed to application at the county level. 

The results were presented and discussed in the previous section. Qualitatively the results appear 

to be consistent with the various assumptions made. Variations in estimated dust emissions by 

land type and season were presented. 

Limitations 

A number of limitations and shortcomings still exist within the revised estimation methodology. 

As with the Phase I implementation, many of the limitations are related to a lack of detail or 
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knowledge concerning the characterization of soils and land types. Specifically, the following 

major limitations should be noted: 

• Threshold surface friction velocities are determined as a function of the aerodynamic 

surface roughness lengths. In the current implementation, surface roughness lengths are 

assigned as a function of land types. However, only a limited number of land types are 

available to characterize vacant lands across the entire domain. A large degree of 

variation can be found within a given land type which is not being captured by the model 

due to a lack of detail in the land use data used for the model. 

• The implementation of the current model assumed that all soils are loose and undisturbed. 

While a number of sensitivity scenarios were considered to evaluate the impact of this 

assumption, no robust data sources where available to determine the actual disturbance 

level of the various soils across various regions. No temporal variation of disturbance 

levels is assumed. In addition, the effect that disturbance of soils and vacant lands has on 

the emission rates of dust due to wind erosion is not well characterized or fully 

understood. 

• The treatment of dust reservoirs is too simplistic in the model. The reservoir 

characteristics determine the duration of wind blown dust events as well as the effects of 

precipitation on the erosion potential of exposed surfaces. It has been documented in the 

literature that depending on the type of soils, a small amount of precipitation can cause a 

crust to form on the surface effectively preventing dust emissions due to wind erosion. 

Only after these crusts have been broken does the surface again have the potential to emit 

dust emissions. These affects can also vary to some degree even for the same types of 

soils depending on soil moisture content among other factors. The amount of soil 

available for erosion is also important with respect to determining the duration of 

emissions during wind events. 

• An evaluation of the modeled results on a seasonal and monthly basis seems to indicate 

that the expected reduction in dust emission from wind erosion during the winter months 

in the northern tier of states is not being modeled adequately. The model implementation 

relies on the predicted precipitation and snow cover fields from the MM5 meteorological 

model. MM5 does not always predict these fields adequately and their impact on the 

estimated windblown dust emissions may be understated. 

• While not discussed explicitly here, the windblown dust estimation methodology predicts 

PM10 emissions directly. For air quality modeling purposes, these emissions must be 

apportioned between PMCOARSE (= PM10 – PM2.5) and PMFINE (PM2.5). The 

current version of the model makes this apportionment assuming a constant ratio of 0.90 

PMCOARSE/PM10. This ratio, which was updated from the previous of value of 0.78, 

is applied across the entire domain, although it is expected to vary by soil texture as well 

as other soil characteristics, such as soil moisture. 
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Recommendations 

While the results of the project are still being reviewed and evaluated and are dependent on 

various assumptions incorporated into the methodology, based on an assessment of the results of 

the fugitive windblown emission estimation methodology implemented for this study, the 

following recommendations are presented: 

• Although updated for the final version of the windblown dust emission inventory, an 

attempt should be made to identify more detailed data regarding land use types and soil 

characteristics for the regional domain. In particular, the disturbance level of the soil has 

a large influence on the estimated emissions due to wind erosion. An on-going WRAP 

Dust Forum project to evaluate and refine the dust definition in terms of natural and 

anthropogenic sources will review data and methods of determining the natural range of 

vacant lands. The results of this project may provide valuable information for use in the 

further characterizing the soil disturbance levels for the wind blown dust model. 

• While information needed to apply agricultural adjustments to the Eastern US was 

incorporated, the level of detail of these data is considerably less than for the WRAP and 

CENRAP regions of the modeling domain. An effort should be made to obtain and 

incorporate agricultural information in the Eastern US with at least as much detail as in 

the Western US. The impacts associated with this limitation are most important in the 

agricultural regions of the upper Midwest and the Southeast. 

• To validate the methodology, the model should be applied to a smaller local domain for 

which detailed data are available 

• A more refined treatment of reservoirs and reservoir characteristics should be 

investigated further. 

• The application of the MM5-derived snow cover fields should be re-evaluated. A review 

of the results on a monthly basis indicates that the treatment of snow cover during the 

winter months, particularly in the Northern Plains regions of the modeling domain, may 

not be correctly implemented. This may be due to problems in the MM5 modeling 

results used to specify these data. One possible approach to this issue would be to apply 

the snow cover data based on monthly, or seasonal, regional climate data in a postprocessing step to appropriately reduce the emission estimates from the windblown dust 

model. 
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