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 415.899.0700May 2006



### i TABLE OF CONTENTS

Page

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

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

2. SUMMARY OF PHASE I METHODOL OGY 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 ve locities for typical surface types

calculated from available data a nd as reported in th e 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 pr ecipitation event to re-initiate wind

erosion for rainfall amounts (constant) exceeding 2 inches................................. 3-3 Table 3-2. Number of days after pr ecipitation event to re-initiate wind erosion for rainfall amounts (constant ) less than or equal to 2 inches................. 3-3May 2006



### ii Table 3-3. NLCD Land Cove r Classification Codes............................................................. 3-5

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

Table 3-5. STATSGO soil text ure 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 emi ssions 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 re gional 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 func tion of friction velocity predicted by the Alfaro and Gomes (2001) model constrained by the four soil geometric mean diameter cl asses of Alfaro et al. (2003).............................. 2-7 Figure 2-3. Comparison between the Alfa ro 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 mode l 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 Am erican Landuse/Landcover....................................... 3-9 Figure 3-3. STATSGO Soils Database soil s 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 regiona l modeling domain.3-17 Figure 3-6. Annual average wind speeds fo r the WRAP 12-km modeling domain............. 3-19 Figure 3-7. Monthly average wind speeds fo r 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..................................................................................................................... 4-5May 2006



### 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 annua l 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 dus t emissions by state for the WRAP

states – 12-km model run................................................................................... 4-12 Figure 4-14. Comparison of PM WB dust emi ssions by state for the WRAP

states – 12-km model run................................................................................... 4-13 Figure 4-15. 2002 annual 36-km PM10 emi ssions 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 annua l PM10 emissions for WRAP states ............... 4-18 Figure 4-23. Monthly PM10 WB dust emi ssions by source category across all

WRAP states. ..................................................................................................... 4-18 Figure 4-24. Spatial distribution of 2002 annua l gridded PM10 WB dust emissions. ........... 4-23 Figure 4-25. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions from agricultural lands....................................................................................... 4-23 Figure 4-26. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions from grasslands. ................................................................................................. 4-24 Figure 4-27. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions

from shrublands. ................................................................................................ 4-24 Figure 4-28. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions

from barren lands. .............................................................................................. 4-25 Figure 4-29. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions

for the WRAP 12-km dom ain............................................................................ 4-25 Figure 4-30. Spatial distribution of 2002 m onthly gridded PM10 WB dust emissions

for the WRAP 12-km dom ain............................................................................ 4-26 Figure 4-31. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions from agricultural lands for the WRAP 12-km domain. ..................................... 4-28 Figure 4-32. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions from grasslands for the WRAP 12-km domain.................................................. 4-28May 2006



### iv Figure 4-33. Spatial distribution of 2002 a nnual gridded PM10 WB dust emissions

from shrublands for the WRAP 12-km domain................................................. 4-29 Figure 4-34. Spatial distribution of 2002 a nnual 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 la nds 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 a nnual normalized county-level PM10 WB

dust emissions for th e WRAP 12-km domain.................................................... 4-33 Figure 4-41. Spatial distribution of 2002 a nnual 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 a nnual normalized county-level PM10 WB dust emissions from grasslands for the WRAP 12-km domain. ........................ 4-34 Figure 4-43. Spatial distribution of 2002 a nnual normalized county-level PM10 WB

dust emissions from shrublands for the WRAP 12-km domain. ....................... 4-34 Figure 4-44. Spatial distribution of 2002 a nnual 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 fr om IMPROVE monitoring network....................... 5-3 Figure 5-2. 2003 Annual Coarse Mass fr om IMPROVE monitoring network....................... 5-3 Figure 5-3. 2004 Annual Coarse Mass fr om IMPROVE monitoring network....................... 5-4 Figure 5-4. Predicted PM10 dust em issions vs. observed Coarse Mass concentrations for the Sagua ro 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 em issions 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 em issions vs. observed Coarse Mass

concentrations for the Medi cine Lake IMPROVE monitor............................... 5-10 Figure 5-9. Predicted PM10 dust emissions vs. observed Fine Soil

concentrations for the Medi cine Lake IMPROVE monitor............................... 5-11 Figure 5-10. Predicted PM10 dust emi ssions 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 emi ssions vs. observed Coarse Mass

concentrations for the The odore Roosevelt IMPROVE monitor....................... 5-14 Figure 5-13. Predicted PM10 dust emissions vs. observed Fine Soil concentrations

for the Theodore Roosevelt IMPROVE monitor............................................... 5-15May 2006



### v Figure 5-14. Predicted PM10 dust emi ssions 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 emi ssions 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 emi ssions vs. observed Coarse Mass

concentrations for the Bri dger Wilderness IMPROVE monitor........................ 5-20 Figure 5-19. Predicted PM10 dust emissions vs. observed Fine Soil

concentrations for the Bri dger Wilderness IMPROVE monitor........................ 5-21 Figure 5-20. Spatial distribution of di fferences in CMAQ predicted monthly Fine Soil (left) and Co arse Mass (right) with and

without windblown dust..................................................................................... 5-23 Figure 5-21. Spatial distribution of di fferences in CMAQ predicted monthly Deciview (left) and Exti nction (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. ......................................... 5-25May 2006

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### iv PREFACE

Regulatory Framework for Tribal Visibility Implementation Plans

The Regional Haze Rule explicitly recognizes the au thority 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 su bject to state jurisdiction.

2. Federally recognized tribes may apply for a nd receive delegation of federal authority to implement CAA programs, including visibility regul ation, or "reasonably se verable" elements of such programs (40 CFR §§49.3, 49.7). The mechanism for this delegation is a Tribal Implementation Plan (TIP). A reasonably severabl e 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 regulator y requirements.

3. The Regional Haze Rule expressly provides that tribal visibility programs are “not dependent on the strategies selected by th e state or states in which th e tribe is located” (64. Fed. Reg. 35756), and that the authority to implement §309 TI Ps extends to all tribes within the GCVTC region (40 CFR §51.309(d)(12).

4. The EPA has indicated that under the TAR trib es 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). EP A 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 recommendati ons 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 soverei gnty and autonomy as reflected in the above framework. Any inconsistency with this framework is strict ly 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 fr om the “stakeholders” (private interest) which participate on Forums and Comm ittees but are not eligible for the Board. Despite this equality of representation on the Bo ard, tribes are very differently situated than states. There are over four hundred federally re cognized tribes in the WRAP region, includingMay 2006

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### v Alaska. The sheer number of tribes makes full pa rticipation 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 importa nt 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, includ ing Board members Forum and Committee members and co-chairs, make their best effort to ensure th at WRAP products are in the best interest of the tribes, the environment, and the public. One inte rest is to ensure that WRAP policies, as implemented by states and tribes, will not constr ain 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 stak eholder interests in appr oving this report as a

consensus document.May 2006

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### 1-1 1. INTRODUCTION

The Western Regional Air Partnershi p (WRAP) is engaged in an effort to implement Section 309 of the Regional Haze Rule. This effort incl udes 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 emissi on inventory currently does not include windblown fugitive dust because of strong concern that the methodology previously used to calculate these particulate emissions yi elded inaccurate and misleading results.

The Phase I WRAP windblown fugitive dust project methodology and results have been documented in a project Final Report (ENVIRO N, 2004) and Technical Memoranda (Mansell, 2003a; 2003b). The results of the initial model runs and subseq uent sensitivity simulations demonstrated a need to revise and/or update various assumptions associated with the development of the emission inventory. Revise d estimation methodologies and algorithms were

evaluated in order to address va rious 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 us ed to characterize vacant land types and soil conditions. In addition, the Phase I methodology relied on arbitrar ily 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 th e 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 st udies 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 y ear 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 incorporati on of more recent (calendar year 2000) Landuse/Landcover data, adjustments to the fine fr action of PM dust, and the incorporation of the latest dust transport fract ions 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 Mans ell et al., 2004, is also presented.May 2006

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### 1-2 -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.May 2006

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### 1-3 -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 Ph ase I estimation met hodology, 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 Phas e 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. Th e latest revised model results are presented and discussed. Comparisons with the draft results are also presented.

• Section 5 documents the results of the in itial 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 improve ments to the modeling approach and data sources are presented.

• Section 7 includes references for this report.May 2006

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

The Phase I Wind Blown Dust model developmen t and implementation is summarized in this section. The various assumptions and limitati ons associated with the emission estimation methodology are discussed. In addition, recent wind blown dust studies are identified and reviewed. Algorithms and me thodologies employed in th ese studies are summarized.

Summary of Phase I Methodology

The development of the Phase I Wind Blown Du st model and implementation, including various assumptions incorporated in the estimati on 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 st udy results as a function of wind speed and soil texture. Adjustments were applied to the result ing 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 la nds based on non-climatic factors. Land use characterization was based on the Biogenic Em ission Landuse Database (BELD3); soil texture

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

The relative lack of detail in th e data sets used for characterizing the physical conditions of land parcels and soils required a num ber 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 develope d relies on the specification of stability of vacant land parcels. The stability characteri stics of land parcels are based solely on the land use type.

• Dust Reservoirs: Reservoir prope rties 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 reserv oirs. Assumptions are made concerning the

amount of time a reservoir will emit wind bl own 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 th e number of wind events treated in the methodology as well as the total dust emissions generated.May 2006

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### 2-2 • 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 im plications 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 thei r implications were discussed in Mansell et al., 2004. The Phase II Wind Blown Dust project, documented here, seeks to address these assumptions and limitatio ns 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 m odel for estimating PM10 from wind blown dust using the concept of thres hold friction velocity which is dependent on the aerodynamic roughness length of the surface, Z

o 

### . 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 su rface 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 f unction of the aerodynamics roughness length.

Threshold friction velocity was calculated as th e ratio of the threshol d velocity for smooth surface (U *ts 

### ) to f eff



### , the efficient friction velocity. f eff



### 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 (Z

os 

### ), which is defined as the mean soil particle diameter (D p 

### ) divided by 30 (Greeley and Iversen, 1985), and the actual aerodynamic roughness length. Soil sample s were collected from the modeled area to

determine D p 

### . Z
os 

### was calculated using the measured D p 

### . Using a mean value of 22 cm/s for

U *ts 

### , the actual threshold friction velocity was ca lculated 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 au thors estimated the thre shold friction velocity and surface roughness length for each surface classification. The co efficient that relates the

surface soil texture to PM10 emissions was estimate d using data for several soils from semi-arid areas (Gillette et al., 1997) show ing the ratio of vertical flux of PM10 to total horizontal mass flux as function of friction velocity.May 2006

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### 2-3 In their work, Draxler, et. al, 2001 considered a special case of wind bl own 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: loos e undisturbed soil and

disturbed soil. All the area was c onsidered dry and therefore the e ffect of rain and snow was not

considered.

Zender, et al., 2003 developed a Dust Entrai nment And Deposition (DEAD) model for studying dust related processes at both loca l and global scales. They consid ered three major factors that affect the dust flux: wind friction velocity, ve getation cover and surface so il moisture content. The approach developed by Marticorena and Bergam ette (1995) was used to develop the model. For computing the threshold fricti on velocity they used a semi-empirical equation developed by Iversen and White (1982). In th is equation, the friction velocity is a functi on of soil density and particle size, and air density and kinematic viscosity. The co nstant surface roughness length of

0.01 cm was assumed for the entire domain.

The change in threshold friction velocity wa s calculated using the equation developed by Marticorena and Bergamette (1995), similar to what as Draxler et al., 2001 us ed in their model. Zender used one global value of 0.0033 cm for th e 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 calcula ted 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 mi cro-scale forces worki ng on saltating particles and the impact of these particles on dust emissions. In this mode l a particle size distribution of the soil is required.

ENVIRON/RMC

The ENVIRON/RMC model refers to the estimati on 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 a nd 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, z 0 

### . In addition to aerodynamic roughness, the degree of disturbance of the surface also plays a key role in the estimation of threshold friction velocities.May 2006

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### 2-4 Based on the work of Marticorena et al. (1997), relationships between u *t 

### and z 0 

### 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 fric tion velocity data. Two major factors have the greatest influence on the th reshold of wind erodible soils: the degree of disturbance and the aerodynamic roughness. For l oose or disturbed soils the most important

factor that controls the thre shold friction velocity is aerodyn amic 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 (1 989) 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 relatio nship shown in Figure 2-1 to assign a threshold friction velocity to a surface requires information on a surface’s ae rodynamic 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 reporte d aerodynamic roughness lengths is presented in

Table 2-2. u *t = 0.31e 7.44 x (Zo)

R 2 = 0.60

u *t = 0.30e 7.22 x (Zo)

0 0.5

1 1.5

2 2.5

3

0.00001 0.0001 0.001 0.01 0.1 1

z o (cm)u*t(m s-1)

wind tunnel data Marticorena et al. 1997 Expon. (wind tunnel data) Expon. (Marticorena et al. 1997)May 2006

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### 2-5 Table 2-1. Threshold friction velocities for typical surf ace types calculated from available data and as reported in the literature 1



### .

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, aerodyna mic roughness will change as a function of plant height and cover through a grow ing season and the tillage practi ces. For natural surfaces, the aerodynamics can change through the season as well as annually through se veral years affecting dust production cycles. This is linked to plant growth in resp onse 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 0.27May 2006

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### 2-6 Table 2-2. Typical surface aerodynamic roughness lengths calculated from available data and as reported in the literature.

Site Type Average

z o (cm) Std. D. z
o (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 dependenc e (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 st udies, 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-analy sis was based in part on the work of Chatenet, et al. (1996) in which they found that desert soils c ould 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).May 2006

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### 2-7 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 a nd Gillies (1989) emission data based on these

classes then tested how well th e grouped data matched predic ted 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 emi ssion relationships for th e 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 = 2.45 x 10 -6 (u

* ) 3.97

FS

F = 9.33 x 10 -7 (u

* ) 2.44

MS

F = 1.243 x 10 -7 (u
* ) 2.64

CS

F = 1.24 x 10 -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-2s-1)

FSS

FS

MS

CS

Power (FSS)

Power (FS)

Power (MS)

Power (CS)May 2006

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### 2-8 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) a pproach, emissions of dust for soils can be confined to four different emission factors, depe nding 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 measur ed 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 thei r four dry soil types (silt [FSS], sandy silt [FS], silty sand [MS], and sand [CS]). The so il texture categorization and the relationships among texture assignments and soil gr oupings are discussed below.

The MacDougall Method

The MacDougall Method refers to the estimati on methodology actually implemented as part of the Phase I Wind Blown Dust Project. A desc ription 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)May 2006

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### 2-9 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,

U 10

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 U t

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 soilsMay 2006

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

Based on the review of recent dust model applications, it is note d that each utilizes a similar approach to the determination of threshold fric tion velocities and dust emi ssion flux rates. Based on the particular applic ation, certain simplifying assumptions are incorporated. As with the ENVIRON/RMC approach, these assumptions are ge nerally required due to a lack of detailed information to fully characterize physical condi tions 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 Phas e II model implementation follow this general approach. The approach for the determination a nd application of each of the required elements of the model are presented below. In some cas es, further research and/or review of existing datasets will be required to implement futu re 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 m odel simulations. Friction velocity u * 

### , is related to the slope of the velocity versus the natura l logarithm of height through the relationship:

o * z



### z z ln 1

u u

κ =



### where u

z 

### = wind velocity at height z (m s -1 

### )

u * 

### = friction velocity (m s -1 

### )

κ = von Karman's constant (0.4)

z o 

### = 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, z 0 

### . This relationship was presented in Figure 2-1.

Surface Roughness Lengths

Surface friction velocities, including the thres hold 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 vege tation cover. These data are presented in Table 2-1. Additional information for roughness lengths as a functi on of land cover were

identified and reviewed for inclusion in th e Phase II model implem entation (Mansell, 2003b;May 2006

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### 3-2 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 determin ed 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 presen ted 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 presen t, the condition of the soils, including textural and stab ility, 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 cla ssified as limited or unlimited.

Classification of reservoirs as limited or unlimite d has implications with respect to the duration of time over which the dust emissions are genera ted. In general, the reservoirs should be classified in terms of the type of soils, the de pth 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 factor s including precipitation and snow events and freezing conditions of the soils.

Given that the soils database for use in the pr oject does not provide information concerning the moisture content or the depth of the soil laye r, certain assumption are made regarding the

determination and classification of soil reserv oirs. These assumptions were investigated previously and based primarily on the land use ty pe and stability of th e vacant land parcel.

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

limited and all vacant lands assu med undisturbed, or stable.

The duration and amount of precipitation and sn ow 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 characteri stics 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.May 2006

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### 3-3 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. Dist urbed 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-di sturbed surfaces. The assumed di sturbance level of the surface was to be determined by the land type and i nvariant in time and acro ss the modeling domain. Thus, assumptions were required to assign surf ace disturbance based on land cover type. As noted previously, the disturbance level of a surface more appropria tely has the effect of altering the threshold surface friction velocity; distur bed 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 LU LC 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 act ivity in desert lands, or animal grazing on rangelands.

In the Phase II model application, all non-agricult ural land types are cons idered un-disturbed, since there is no a priori information to i ndicate otherwise for the regional scale modeling

domain to be considered. Additional informati on concerning disturbance levels for certain land types was investigated to determine whether an assumed percentage of specific land types can beMay 2006

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### 3-4 considered disturbed versus un-disturbed. No definitive information to support assigning a disturbance level top various la nd 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 a nd 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 implemen tation of the Phase II wi nd blown dust model are

summarized in this section.

Land Use/Land Cover

The current implementation of the wind blown dus t model utilized updated land use/land cover (LULC) data, as noted previously. These data we re 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 mode l, these LULC data were combined with the agricultural crop data from the BELD3 database in order to ad dress non-climatic effects for agricultural lands. For completeness, the LULC da ta used previously and the currently utilize data are presented and discussed below.

The Phase I model implementation used the B ELD3 land use/land cover (LULC) database to specify vacant land types. Given that these da ta are based primarily on information from pre1990 and that the distinction betw een barren/desert lands and shr ub/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 evalua tion of the NLCD database conducted during Phase I Study revealed that the shrubland a nd barren land (desert) categories were better represented as distinct land types.

The National Land Cover Data (1992 NLCD) was de veloped 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 enti re conterminous U.S. based on 30-meter Landsat thematic mapper (TM) data. The NLCD was deve loped from TM data acquired from the MultiResolution Land Characterization (MRLC) Consortiu m, a partnership of federal agencies that produce or use land cover data. The partners include USGS (National Mapping, Biological Resources, and Water Resources Divisions), th e USEPA, the U.S. Forest Service and the National Oceanic and Atmospheric Administrati on. A more detailed discussion of the 1992 NLCD LULC data can be found in Mansell, et al., 2005 and Mansell a nd Hoats, 2005. The land cover characteristics, defined in terms of 21 se parate categories, are pr esented in Table 3-3. Figure 3-1 displays the 1992 NLCD data for the conterminous US.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec3.doc5/5/2006 

### 3-5 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.May 2006

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### 3-6 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 pa rt 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 th e data to ensure data consiste ncy for quantitative land surface analysis.

The data are available as GIS raster datasets for each continent, in a geod etic 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, in cluding Central America.

In addition the recent time period represented by the data, another advantage of this dataset over currently used data (i.e., the NL CD), is the inclusion of more de tailed 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 deta il 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.May 2006

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### 3-7 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 Needleleav ed Evergreen Forest - Closed Canopy 5 Temperate or Sub-polar Needleleav ed Evergreen Forest - Open Canopy 6 Temperate or Sub-polar Needleleav ed Mixed Forest - Closed Canopy 7 Temperate or Sub-polar Mixed Broadleav ed or Needleleaved Forest - Closed Canopy 8 Temperate or Sub-polar Mixed Broaddlel eaved 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 Broadl eaved 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 Fore st 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 CanopyMay 2006

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### 3-8 Figure 3-2a. North American Landuse/Landcover for 2000.May 2006

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



### Soil Characteristics

Application of the emission factor relations described above requi res the characte rization 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 importan ce for the application of the emission estimation relations derived from wind tunnel study resu lts. 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 develope d. The STATSGO database was developed by the

Natural Resources Conservation Service of the U. S. Department of Agricultural (USDA) and provides detailed information con cerning the taxonomy of the soils , including soil texture class, percentage of sand, silt and clay, and the availa ble water capacity of the soil. While the complete STATSGO database available from th e USDA includes numerous additional features, those features relevant for this project were cons idered to be the soils texture class. The soils data are available as geospatial coverages and associ ated 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 c odes is based on the standard soil triangle that classifies soil texture in terms of percent sa nd, 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 triangleMay 2006

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### 3-10 provides the mapping of the 12 soil textures to the 4 soil groups considered in their study. Combining the data from these two soil text ure/soil group mappings results in the unique mapping of soil textures to the soil groups for wh ich 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 text ure 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.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec3.doc5/5/2006 

### 3-11 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 informati on in Table 2-2. As noted above, the disturbance

level of various surfaces has th e effect of altering the surf ace 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 su rface 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 velociti es increase rapidly above values that can be realistically expected to occur in the meteorol ogical data used in the model implementation. Therefore to simplify the model implementa tion, only those land type s 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 determ ined by the land use type, the th reshold 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 characte ristics 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 a nd 2000 LULC data for the entire conterminous US and for the

WRAP states. Relevant differe nces 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 di d 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 PotatoesMay 2006

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### 3-12 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 WetlandsMay 2006

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### 3-13 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, Z 0 (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/Othe r 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 Othe r 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%May 2006

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### 3-14 Table 3-9. (Continued) Summary of 2000 NALC LULC by dust code percentages by state. Percent Acreage by Dust Code

Dust Code

State Othe r 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%May 2006

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### 3-15 Table 3-10. Summary of 2000 NALC LULC by dust code acreages by state. Total Acreage by Dust Code

Dust Code

State Othe r 3 4 6 7 Total

Alabama 24,692,490 6,067,619 1,021,58 9 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 10,573,498 969,28 2 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 4,451,08 2 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 35,175,60 8 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 1,123,00 1 121,694 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 15,467,945 456,53 0 128,455 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 8,886,151 480,37 1 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 10,349,325 419,88 0 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 24,727,00 6 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 8,347,068 522,35 9 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 1,874,048,428May 2006

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### 3-16 Meteorology

As with the Phase I model implementation, gridde d hourly meteorological da ta 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 fiel ds required include wind speeds, precipitation rates, soil temperatures a nd ice/snow cover. The 2002 MM5 model results on the National Unified RPO Grid, at both 12- a nd 36-km resolution, will be proces sed as in Phase I and used in the application of the revised wind blown dust model.

Figure 3-4 displays the annual average wind sp eeds on the 36-km regional modeling domain. Figure 3-5 presents monthly average wind speed s for the 36-km modeli ng 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.May 2006

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### 3-17 Figure 3-5. Monthly average wind speeds for the WR AP 36-km regional modeling domain.May 2006

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### 3-18 Figure 3-5 (concluded). Monthly average wind speeds for the WRAP 36-km regional modeling domain.May 2006

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### 3-19 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.May 2006

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### 3-20 Figure 3-7 (continued ). Monthly average wind speeds for the WRAP 12-km modeling domain.May 2006

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### 3-21 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, includi ng irrigation and seasonal crop growth. As a result, several non-climatic correc tion or adjustment factors were developed for applicability to the agricultural wind erosion emi ssions. These factors included:

• Long-term effects of irriga tion (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).May 2006

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### 3-22 The methodology used to develop individual non-c limatic 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 Res ources Board (CARB) in their development of California-specific adjustment factors for USDA’s Wind Erosion E quation (WEQ) (CARB, 1997). These correction factors were developed for specific so il textures, crop types, and geographic locations and then applied to the wi nd erosion estimates developed from the wind

tunnel studies. Correction factors are developed only for the 17 fiel d crops specifically identified in the BELD3.1 data set (i.e., alfa lfa, barley, corn, cotton, grass, hay, oats, pasture, peanuts, potatoes, rice, rye, sorghum, soybeans, tobacc o, 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 la nd 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 n eed 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 aggr egated agricultural land parcels

from the NALC data used.

For states outside the WRAP a nd CENRAP regions, the non-climatic agricultural adjustments described above were applied base d 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 utiliz ed a modeling domain that did not extend much farther east than the Mississippi River. For Phase II of the projec t, 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 regi on 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 partic les are subject to near source removal. These mechanisms include gravitatio nal 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 partic ular source is transported significantly to

affect the total available emi ssions in the atmosphere for ai r quality grid modeling.

Recent research has shown that the amount of fugitive dust captured in the surround canopy or on physical structures can be rela ted to the physical characteristic s 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 qu ality modeling (Pace, 2003). The county-level transport fractions were based on the percenta ge of land use in each county derived from the

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

Within the wind blown dust model, rather th an applying county-lev el fractions, landusedependent transport fractions we re calculated based on the gridded landuse data used in theMay 2006

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### 3-23 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 inve ntory, the transport fracti ons 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 revi sed fractions, labeled “R evised” 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 w ithin the model provides fugitive dust

emissions of PM 10 

### . For the application of air quality models, the PM 10 

### 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 fiel d 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

PM 10 

### of 0.78.

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

Midwest Research In stitute (MRI, 2005). The WRAP Dust Emission Joint Forum (DEJF) funded a project to evaluate the fine fraction of particulate matter in fu gitive 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 frac tion of PM for use in the wind blown dust model. The revised dust inventory presented here in used a constant value of 0.9 for the fine

fraction of PM dust.May 2006

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

Sensitivity Simulations for 2002

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

data described above. A total of four scenarios we re 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 dura tion for disturbed or undistur bed soils; 10% of grassland, shrubland, and barren land area assumed disturbed; thres hold friction velocity for disturbed grass and shrubland = 0.5

· (undisturbed value); thres hold 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 velo city for disturbed grass and shrubland = 0.5

· (undisturbed value); threshold frictio n 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. Th e effect of the error was to shif t 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 th e 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 assump tions. 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 qualit y modeling. Scenario b assumes that all soils are loose and undisturbed, and imposes a 10-hour-p er-day limit on the dur ation 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 al l soils are undisturbed, there is no information available to assign disturbance levels to va rious 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 wi ll vary by region, soils type, and season. In addition, the threshold surface velocities fo r the disturbed soil cases are based on only very limited test results and are ther efore not likely to apply region-wide.May 2006

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### 4-2 Given the lack of supporting data for the dust re servoir and soil disturba nce 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 subs equent 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 meteor ological data (Refs) for both the 36-km and 12-km regional modeling do mains (Figures 1-1 and 1-2, respectively). For the preliminary model runs, agricultural adjust ments 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 agri cultural 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 ap plied in the draft 2002 model runs. The dust transport fractions were used to model the e ffects 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 applie d at a spatial resolution of 12-km, the same as the underlying

landuse data used to drive the emi ssion estimates with in 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 tr ansport fractions as used in the draft implementation were presented in Table 3-11.

The draft dust emissions estimates obtained wi th 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 wit hout application of the transport fractions in order to investigate the e ffects 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 to tal 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 tr ansport fractions vary due to their dependence on land use types. Figure 4-2 displays these resu lts 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 characteristic s. The largest contributions to the total PM10

emissions are seen to be from st ates 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 1,400,000AlabamaArizonaArkansasCaliforniaColoradoConnecticutDelawareD. of ColumbiaFloridaGeorgiaIdahoIllinoisIndianaIowaKansasKentuckyLouisianaMaineMarylandMassachusettsMichiganMinnesotaMississippiMissouriMontanaNebraskaNevadaNewNew JerseyNew MexicoNew YorkNorth CarolinaNorth DakotaOhioOklahomaOregonPennsylvaniaRhode IslandSouth CarolinaSouth DakotaTennesseeTexasUtahVermontVirginiaWashingtonWest VirginiaWisconsinWyoming

Statetons/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 4.0AlabamaArizonaArkansasCaliforniaColoradoConnecticutDelawareD. of ColumbiaFloridaGeorgiaIdahoIllinoisIndianaIowaKansasKentuckyLouisianaMaineMarylandMassachusettsMichiganMinnesotaMississippiMissouriMontanaNebraskaNevadaNewNew JerseyNew MexicoNew YorkNorth CarolinaNorth DakotaOhioOklahomaOregonPennsylvaniaRhode IslandSouth CarolinaSouth DakotaTennesseeTexasUtahVermontVirginiaWashingtonWest VirginiaWisconsinWyoming

Statetons/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 th e 36-km and 12-km model runs for the WRAP

states. Results with and without application of transport frac tions 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 th ose at 36-km resolution for all states. The reductions due to the application of transport fr actions are also displayed. Application of the model at 36-km resolution, requires the speci fication 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 matc hing 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 a pplied to all land use types within the nine 12- km cells that make up the regi on 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 differen ces 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

Statetons/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

Statetons/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 resu lts at 36-km resolution for each WRAP state by dust category. The relative contributions by la nd use type for each state can be seen and provides some insight into the relative magnitude of the predicted emi ssions across each state within the WRAP region.

Annual WB Dust PM10 Emissions

WRAP States

0 100,000 200,000 300,000 400,000 500,000 600,000ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

Statetons/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 du st 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 doma in are presented in Figures 4-7 and 4-8, which display the absolute magnitude of predic ted PM10 dust emissions as a function of landuse for each state, and normalized by land area, respec tively. 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 type s and regions prone to large amounts of annual precipitation.

Comparison of the results of the 36-m and 12-km m odel 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 charac teristics (land use and soil textures) is seen

to be an overall increase in estimated emi ssions for all states within the WRAP region.

The results of the model runs with and without application of the transport fractions and atMay 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 12ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

Statetons/yr/km^2 Barren lands

Shrublands

Grasslands

Agricultural lands



### Figure 4-6. Normalized 2002 annual 36-km PM10 emissi ons by dust category – with TF appliedMay 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 

### 4-8 Annual WB Dust PM10 Emissions

WRAP States

0 100,000 200,000 300,000 400,000 500,000 600,000ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

Statetons/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 12ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

Statetons/yr/km^2 Barren lands

Shrublands

Grasslands

Agricultural lands



### Figure 4-8. Normalized 2002 annual 12-km PM10 emissi ons by dust category – with TF appliedMay 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

Categorytons/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 predic ted dust emissions can be discerned from this display. The dust emissions tend to peak in th e spring months due to generally higher winds throughout most of the domain. In addition, for ag ricultural areas this time period corresponds to the spring planting when the cr op 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

Monthtons/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 PM 10 Emission Inventory

The final 2002 windblown PM dust emission inve ntory 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 thes e 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 Fi gure 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

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000ALARAZCACOCTDCDEFLGAIAIDILINKSKYLAMAMDMEMIMNMOMSMTNCNDNENHNJNMNVNYOHOKORPARISCSDTNTXUTVAVTWWIWWY

StatePM10 (tons/year)



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



### WB Dust PM10 Emissions

WRAP 36-km Domain

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000AZCACOIDMTNDNMNVORSDUTWAWY

StatePM10 (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

0 100,000 200,000 300,000 400,000 500,000 600,000ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (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 wi ndblown dust PM emissions on the WRAP 12-km modeling domain between the draft inventory (1992 NLCD) a nd the final inventory (2000 NALC). Displayed are the total PM10 emissions a nd 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 ar e 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. Th e differences seen are due primarily to the

differences in land use distributions across each stat e. 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

A r i z o n a C a l i f o r n i a C

o l o r a d o I d a h o M o n t a n a

N e v a d a N e w

M

e x i c o N o r t h

D a k o t a O

r e g

o n S o u t h

D a k o t a U t a h Wa

s h i n g

t o n Wy

o m

i n g
StatePM (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)

0 100,000 200,000 300,000 400,000 500,000 600,000 700,000ALARAZCACOCTDCDEFLGAIAIDILINKSKYLAMAMDMEMIMNMOMSMTNCNDNENHNJNMNVNYOHOKORPARISCSDTNTXUTVAVTWWIWWY

StatesPM10 (tons/year) Agricultural Grasslands

Shrublands Barren



### Figure 4-15. 2002 annual 36-km PM10 emissions by dust category and state.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 

### 4-14 2002 Annual WRAP 12-km WB Dust

2000 NALC

0 100,000 200,000 300,000 400,000 500,000 600,000ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatesPM10 (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 18.00ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (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 4.0ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (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 1.4ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (tons/year/km^2) 1992 NLCD

2000 NALC



### Figure 4-19. Comparison of 2002 annual 12-km PM10 normaliz ed 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 7.0ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (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 6.0ArizonaCaliforniaColoradoIdahoMontanaNevadaNew MexicoNorth DakotaOregonSouth DakotaUtahWashingtonWyoming

StatePM10 (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) windbl own 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 stat es 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 re sults 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 high er that the results of the 36-km model runs. Figure 4-22 presents a comparison of the 2002 a nnual 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 2,364,089May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/2006 

### 4-18 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

CategoryPM10 (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 emissi ons for the Base02a inventory is presented in Figure 4-23 by landuse category and st ate for the WRAP states. As expected, the dust emissions peak in the late spring and early summer m onths and are dominated by emissions from shrublands. Table 4-3 presents the monthly PM10 dust emissi ons 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

MonthPM10 (tons) Agricultural Grasslands

Shrublands Barren



### Figure 4-23. Monthly PM10 WB dust emissions by s ource category across all WRAP states.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/20064-19Table 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)StateJanFebMarAprMayJunJulAugSepOctNovDecAnnual2002Arizona5990 7507 15501 19125 17532 14198 8631 5471 4638 2503 13215 3143117452California7239 10602 21951 24497 24159 26122 19628 11702 7080 5715 15762 15641190096Colorado4037 9853 14335 27876 32074 33438 13254 15237 14697 6970 4967 3440180178Idaho1790 1612 4512 10571 19082 9239 8877 9272 3924 3568 4163 852185132Montana31764 22689 25070 72935 92373 55727 34727 38549 27876 20628 34108 10248466693Nevada3923 6895 16169 26114 34612 27483 13273 13853 8769 4727 14201 15001185020New Mexico16418 21393 41485 37534 38875 33358 14146 10663 10127 8401 16154 9736258290North Dakota6178 11370 16068 34463 46003 17872 13037 22409 14138 8821 7455 569198384Oregon15480 9066 14865 17388 23593 18803 12878 10571 5381 5238 12647 39669185579South Dakota17179 43529 16124 84461 108224 60298 30298 58755 35530 27294 28280 11753521724Utah1331 2998 11765 21707 32354 23097 15390 17781 7050 2192 7138 6980149786Washington16712 4933 11583 13768 12133 8269 9015 3206 2694 1531 4274 12848100966Wyoming2424 2433 5315 17485 22412 11583 5832 10003 4224 4580 3811 230092402WRAP Total 130465 154878 214744 407924 503427 339486 198984 227473 146128 102170 166176 139847 2731702Agricultural land WB dust emissions for WRAP States - 12-km (tons)StateJanFebMarAprMayJunJulAugSepOctNovDecAnnual2002Arizona7 9 36 44 61 65 58 16 17 18 18 4353California560 958 1819 1014 1425 3005 1384 907 838 547 1521 422418203Colorado188 416 518 1200 1220 1346 754 669 607 402 232 947647Idaho406 298 807 971 1324 568 639 609 355 366 444 11377924Montana1521 863 1185 3510 5090 2585 1181 1900 2106 1419 1487 36823214Nevada5 14 29 50 64 63 26 27 20 7 23 35365New Mexico134 186 404 625 636 606 236 315 449 294 175 504111North Dakota3744 5580 8454 20959 27734 8953 6495 12618 9323 6054 4584 520115017Oregon1469 1082 1374 542 364 531 514 499 372 834 1610 318812377South Dakota3402 20903 9188 36944 37510 19350 7799 13951 13231 14414 11042 3822191557Utah6 8 55 99 121 73 65 68 36 20 33 16601Washington1791 722 1290 1341 1105 722 694 324 278 156 776 170510903Wyoming41 48 49 153 206 116 68 116 71 71 63 331036WRAP Total 13276 31087 25208 67452 76860 37983 19912 32018 27703 24601 22008 15197 393306May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/20064-20Table 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)StateJanFebMarAprMayJunJulAugSepOctNovDecAnnual2002Arizona63 83 378 337 296 191 130 58 43 33 74 421727California58 342 1122 1111 948 1021 610 156 142 220 639 18058173Colorado501 1432 1275 3882 3472 3135 710 1136 635 580 613 20017570Idaho48 50 175 938 1728 599 597 609 145 55 221 4915657Montana6163 4675 5710 12069 20623 9454 4834 5647 3599 1437 4482 156180254Nevada33 85 213 357 465 238 118 94 49 12 219 2652148New Mexico878 1286 2446 2104 2279 1471 413 398 490 233 672 27912950North Dakota610 1499 2724 3656 5431 2667 744 2581 715 220 372 021218Oregon1435 828 1056 829 832 414 339 86 118 41 683 404810709South Dakota4243 6088 2789 12780 22656 6659 1455 10393 2686 3105 3496 90177251Utah76 112 455 1329 1462 1036 521 330 129 40 259 1305880Washington1491 365 854 1143 698 259 167 23 96 138 164 12076604Wyoming1604 1691 4278 12257 14920 6765 3144 5434 1421 2364 2473 160757958WRAP Total 17203 18537 23474 52791 75809 33908 13783 26944 10269 8478 14366 12537 308099Shrubland WB dust emissions for WRAP States - 12-km (tons)StateJanFebMarAprMayJunJulAugSepOctNovDecAnnual2002Arizona5705 7180 14071 17085 15836 13029 7598 5111 4300 2340 12635 2957107849California6064 8343 16762 19781 19301 19979 16358 9882 5560 4542 12229 9169147970Colorado3345 7998 12516 22735 27291 28888 11767 13410 13426 5974 4117 3143154609Idaho1304 1250 3480 8538 15831 7998 7555 7981 3385 3119 3440 680770686Montana24028 17119 18143 57248 66530 43609 28654 30964 22127 17743 28099 8298362561Nevada3759 6577 15401 24779 33049 26201 12594 13304 8367 4612 13548 14248176439New Mexico14541 18999 36805 32989 34029 29599 12729 9470 8768 7437 14616 8920228901North Dakota1807 4268 4847 9763 12676 6165 5756 7151 4054 2536 2488 4761558Oregon12044 6920 12015 15751 22108 17610 11830 9836 4804 4324 10141 31250158633South Dakota9418 16246 4066 34096 47226 33699 20806 33932 19280 9566 13569 6938248843Utah936 2563 9707 17749 26467 18623 12476 14369 5991 2043 5871 5090121885Washington12595 3621 9065 10730 9741 6943 7888 2810 2251 1187 2959 914778938Wyoming778 689 889 4775 6954 4495 2511 4272 2634 2068 1264 66031989WRAP Total 96325 101772 157768 276018 337039 256839 158520 162492 104948 67490 124975 106675 1950862May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec4.doc5/5/20064-21Table 4-3.(Concluded) Monthly WB dust PM10 emissionsby state and dust category – 12km WRAP States.Barren land WB dust emissions for WRAP States - 12-km (tons)StateJanFebMarAprMayJunJulAugSepOctNovDecAnnual2002Arizona215 235 1016 1660 1339 913 845 285 278 111 488 1397524California556 959 2247 2591 2486 2117 1276 757 539 407 1374 44215751Colorado2 7 26 59 91 68 23 23 29 14 5 3351Idaho33 14 49 124 199 74 86 74 40 28 58 86866Montana51 32 32 108 130 79 58 39 44 30 40 21663Nevada126 219 526 929 1034 981 535 428 333 97 410 4526069New Mexico864 921 1830 1816 1931 1682 768 481 420 438 692 48612328North Dakota18 23 43 85 162 88 42 59 46 12 11 2592Oregon532 236 419 267 289 248 196 151 86 40 213 11823859South Dakota115 292 81 642 832 590 238 479 332 209 173 904073Utah313 315 1548 2529 4305 3366 2328 3013 894 89 975 174321419Washington834 225 375 554 589 344 266 49 70 50 375 7894520Wyoming2 5 99 300 331 206 109 181 98 76 11 01418WRAP Total 3661 3483 8294 11662 13719 10756 6769 6019 3208 1601 4826 5438 79435May 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, gr asslands, shrublands and barr en 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 du st emissions are highest in agricultural areas in the central portion of the US and in parts of th e West dominated by shrub- and grasslands.

The results on the 12-km WRAP modeling domai n are shown in Figure 4-29. The monthly variation of predicted du st 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 ha ve 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 th rough 4-34. County-level modeling results are displayed in Figures 4-35 through 4-39, which pr esent the total PM10 dust emissions, as well as

the dust emissions from each of the contributi ng landuse categories. Figure 4-40 through 4-44 display these results normalized by land area for each of the landuse categorie s. Presentation of the results in terms of normalized emissions, rath er than absolute magnitudes, allows a direct comparison of the results at the different spat ial resolutions in addi tion 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

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### 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

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### 4-27 Figure 4-30. (Concluded) Spatial distribution of 2002 monthly gr idded 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

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### 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

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### 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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### 4-33 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

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

The 2002 wind blown dust emissions were eval uated different ways to assess their reasonableness and accuracy. The results of an in itial evaluation analysis are presented in this section, while a more refined anal ysis may occur at a later date. One of the challenges in this evaluation is that measurements generally do not distinguish between wi nd blown dust and other forms of dust (e.g., from paved and unpaved road s). Moreover, measurements of coarse matter (CM), that are typically assumed to be dust, can contain other non-dus t 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 interferen ce with fires is probably a fair ly good estimate of the fine dust contribution. The CM measurement, on the ot her 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 Ma jor Causes of Dust-Resultant Haze in the WRAP" (Etyemezian et al., 2004) that will char acterize dust events at IMPROVE monitors for 2001-2003. This study is using extinc tion due to fine Soil (FS) and CM to define extinction due

to dust: B dust 

### = [FS] + 0.6 [CM]. Dust events at the WRAP IM PROVE 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 be en 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 th e 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 meas ured IMPROVE dust extinction (B dust 

### ) 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 ai r quality across the WRAP region. The CMAQ model has been modified to separately track fine and coarse wi ndblown and other dust emissions. We performed a preliminary operati onal evaluation of the CMAQ dust and other fine and coarse PM estimates against the IMPR OVE Soil and CM measurements using the preliminary 2002 predicted dust emissionsMay 2006

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### 5-2 Comparisons of Wind Blown Dust Emissions wi th 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 monito r 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 B dust 

### (measured and modeled nonoccurrence of dust); (2) enhanced wind blown dust with near-zero B dust 

### (no measured dust with wind blown dust in vicinity of monitor); (3) no windblown dust with elevated B dust 

### (measured dust without modeled windblown dust);

and (4) enhanced windblown dust emissions and B dust 

### (enhanced models and measured dust).

These classifications allow a qualitative evaluation of the windblown dust emissions. It is expected that type (1) would o ccur 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 probl em with the windblown dust m odel, as the dust cloud may not have impacted the monitor. Likewise, the elevated B dust 

### 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 B dust 

### . If the above analysis provides useful information it would be repeated for the fine a nd coarse components of the measured (FS and CM) "dust" a nd 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 we bsite. 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 concentrati ons are apparent throug hout the central states in 2003 and 2004. The 2003 and 2004 monitoring netw orks 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 mon itors is also shown in these figures. These monitoring locations were paired in space and ti me 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

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### 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

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



### An Excel macro was developed to generate the s catter plots for all paired of predicted dust and

observed concentrations within the 12-km WR AP modeling domain. Selected results are presented in Figures 5-4 through 5- 19. A complete set of scatte r 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 directio nal 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 th e observed concentrations and predicted emissions and presented in terms of per centages.

The results of the analysis indicate a wide ra nge of modeled dust emissions versus observed

concentrations. One of the difficulties in this anal ysis 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 result s in a quantitative manner. Qualitatively, the results show many instances where the model pr edicts windblown dust emissions and enhanced

concentrations at the monitors but it is still un certain whether the monitored values are in fact due to other dust sources. In addition, as th e dust model does not take into consideration wind direction, it is possible that ev en 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 em ission 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 th at 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 displa ys, in particular the magnitude of the predicted emissions in

relation to the observed ambient conc entrations. This analysis is st rictly qualitative, and serves to provide some measure of the reasonablene ss of the ability of th e windblown dust emissionMay 2006

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### 5-5 model to capture the spatial a nd temporal variation across th e regional modeling domain. A review of the displays presente d in Appendix B provides an indi cation 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 appe ar 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 ev ents 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 overa ll performance of the windblown dust model.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-6Saguaro West, AZ – SOILSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionFigure 5-4.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Saguaro West, AZ IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-7Saguaro West, AZ – SOILSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionSAWE(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) EmissionFigure 5-5.Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Saguaro West, AZ IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-8Agua Tibia, CA – CMAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionFigure 5-6.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Agua Tibia IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-9Agua Tibia, CA – SOILAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionAGTI(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) EmissionFigure 5-7. Predicted PM10 dust emissions vs. observed Fine Soilconcentrations for the Agua Tibia IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-10Medicine Lake, MT – CMMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionFigure 5-8.Predicted PM10 dust emissions vs. observed Coarse Massconcentrations for the Medicine Lake IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-11Medicine Lake, MT – SOILMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionMELA(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) EmissionFigure 5-9. Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Medicine Lake IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-12Starkey, OR – CMSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionFigure 5-10.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Starkey IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-13Starkey, OR – SOILSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionSTAR(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) EmissionFigure 5-11.Predicted PM10 dust emissions vs. observed Fine Soilconcentrations for the Starkey IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-14Theodore Roosevelt, ND – CMTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionFigure 5-12.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Theodore Roosevelt IMPROVEmonitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-15Theodore Roosevelt, ND – SOILTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionTHRO(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) EmissionFigure 5-13.Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Theodore Roosevelt IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-16Lostwood, ND - CMLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionFigure 5-14.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Lostwood IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-17Lostwood, ND – SOILLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionLOST(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) EmissionFigure 5-15.Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Lostwood IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-18Salt Creek, NM – CMSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionFigure 5-16.Predicted PM10 dust emissions vs. observed Coarse Mass concentrations for the Salt Creek IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-19Salt Creek, NM – SOILSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionSACR(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) EmissionFigure 5-17.Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Salt Creek IMPROVE monitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-20Bridger Wilderness, WY – CMBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionFigure 5-18.Predicted PM10 dust emissions vs. observed Coarse Massconcentrations for the Bridger Wilderness IMPROVEmonitor.May 2006X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc5/5/20065-21Bridger Wilderness, WY – SOILBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionBRID(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) EmissionFigure 5-19.Predicted PM10 dust emissions vs. observed Fine Soil concentrations for the Bridger Wilderness IMPROVE monitor.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 with out 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 track ed 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 othe r fine and coarse PM species into windblown dust, other dust and other primary fi ne 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 (w indblown and other) sp ecies 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 di fferences in predicted Coarse Ma tter (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 ar e 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 differen ce in predicted recons tructed deciview a nd extinction for the

case with and without the windbl own dust emissions for the mont hs of April and May. As shown the differences are fairly small and confin ed 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.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc 

### 5-23 Figure 5-20. Spatial distribution of differences in CMAQ predicted monthly Fine Soil (left) and Coarse Mass (right) with and without windblown dust.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc 

### 5-24 Figures 5-22 and 5-23 display scat ter plots of predicted and measur ed fine Soil and CM, for each of the two months simulated, using only t hose 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 thes e displays and the corresponding statistics. In general, the model performance shows only slight changes, a lthough there are some exceptions for particular months throughout the year. The re sults 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.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec5.doc 

### 5-25 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.May 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec6.doc5/5/2006 

### 6-1 6. SUMMARY

The major findings of the study, as well as variou s limitations of the mode ling performed for this study, are summarized in this section. Recommendations are made with respect to improvements in data quality as well as e nhancements 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 th e 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 th e emission estimation model based on the updated

2000 LULC data.

Based on a review of recent studies concerni ng dust emission from wind erosion, a general estimation methodology was developed. Emissi on rates were developed by soil type and stability. Land use/land cover was based initial ly 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 emi ssion inventory was developed using updated

LULC data on the 2000 North America Land Char acteristics (NALC) database. Wind speed, precipitation rates and soil temperatures were ba sed on MM5 model simulati on results at spatial resolutions of 12-km and 36-km. Although th e 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 impl ement the methodology as discussed in previous sections of this report.

• Apply the transport fraction as developed by th e EPA. This should be implemented from

within the dust model to make use of th e higher resolution of the land use data as opposed to application at the county level.

The results were presented and discussed in the pr evious section. Qualitatively the results appear

to be consistent with the vari ous assumptions made. Variations in estimated dust emissions by land type and season we re 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 orMay 2006

X:\WRAP RMC\PhaseII_WBDust\Final_Report\sec6.doc5/5/2006 

### 6-2 knowledge concerning the characteri zation of soils and land types. Specifically, the following major limitations should be noted:

• Threshold surface friction velocities are de termined 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 lim ited number of land types are

available to characterize vacant lands acro ss 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 assume d 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 availa ble to determine the actual disturbance

level of the various soils acro ss various regions. No tempor al 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 er osion is not well char acterized or fully

understood.

• The treatment of dust reservoirs is too simplistic in the model. The reservoir

characteristics determine the duration of wind bl own dust events as well as the effects of precipitation on the erosion poten tial 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 prev enting dust emissions due to wind erosion. Only after these crusts have been broken doe s 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 w ith respect to determining the duration of emissions during wind events.

• An evaluation of the modeled results on a s easonal and monthly basis seems to indicate that the expected reduction in dust emission from wind erosion duri ng the winter months

in the northern tier of stat es is not being modeled adequa tely. 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 a nd 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 upd ated 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.May 2006

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### 6-3 Recommendations

While the results of the project are still bei ng reviewed and evaluated and are dependent on various assumptions incorporated into the met hodology, based on an assessment of the results of the fugitive windblown emission estimation me thodology 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 deta iled data regarding land use types and soil characteristics for the regional domain. In part icular, 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 me thods 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 agricu ltural 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 East ern 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 characte ristics 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 th at 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 possi ble approach to this i ssue would be to apply the snow cover data based on monthly, or seasonal, regional climate data in a postprocessing step to appropriately reduce th e emission estimates from the windblown dust

model.May 2006

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