CHAPTER 6 

Air Quality Modeling 

6.1 Introduction ....................................................................................................... 6-1 

6.2 Overview of the Dust ID Program ..................................................................... 6-1 

6.3 Dispersion Modeling Techniques ....................................................................... 6-2 

 6.3.1 Preparation of the Meteorological Data ............................................. 6-3 

 6.3.2 PM10 Emissions and Source Characterization ................................... 6-3 

 6.3.3 CALPUFF Options and Application ................................................... 6-5 

 6.3.4 Background PM10 Concentrations ..................................................... 6-6 

6.4 Attainment Demonstration ................................................................................. 6-6 

 6.4.1 Control Strategies Analysis ............................................................... 6-7 

 6.4.2 Attainment Demonstration Results .................................................... 6-7 

6.5 References ........................................................................................................ 6-8 
FIGURES 

Following page

Figure 6.1 Owens Lake PM10 and meteorological monitoring network..................... 6-8 

Figure 6.2 Owens Lake Sensit network for July 2005 through June 2006. .............. 6-8 

Figure 6.3 Model domain and one-km mesh size terrain (m) ................................... 6-8 

Figure 6.4 Area source configuration for July 2005 through June 2006. .................. 6-8 

Figure 6.5 Fifth highest 24-Hour PM10 (μg/m3

) at shoreline receptors, 

 no Keeler dunes, after controls (every 4th plotted). ................................ 6-8 

Figure 6.6 Number of 24-hour PM10 predictions greater than 150 μg/m3 

 at shoreline receptors, no Keeler dunes, after controls 

 (every 4th plotted) ................................................................................... 6-8 
6-1 

6 

Air Quality Modeling 

6.1 INTRODUCTION 

An air quality model was used to help identify air pollution sources that contributed to PM10

violations, and to evaluate control strategies to bring the area into attainment. The CALPUFF 

modeling system was selected for use in the Owens Valley State Implementation Plan (SIP). 

CALPUFF is an approved United States Environmental Protection Agency (USEPA) guideline 

model that is used for evaluating SIP control strategies and for other regulatory purposes (Scire, 

et al., 2000). It was previously used to support the attainment demonstration in the 2003 Owens 

Valley PM10 SIP (2003 SIP). The modeling analysis contained in this SIP revision updates the 

2003 SIP modeling studies with more recent observations collected from July 2002 to June 2006. 

This section describes the sources of data that were used to support the CALPUFF model and 

explains how the model was run. Further details are provided in the Modeling Report included in 

this 2008 SIP as Appendix B. 

The model is an important tool that is used to help quantify the PM10 impacts caused by dust 

source areas at Owens Lake. Data to support the air quality model are collected as part of the 

Owens Lake Dust Source Identification (Dust ID) Program. The Dust ID Program is a long-term 

monitoring program that is intended to identify dust source areas for control under the provisions 

of the Supplemental Control Requirements (SCR) in the 2003 SIP and the 2006 Owens Lake 

Settlement Agreement entered into between the District and the City on December 4, 2006 

(Settlement Agreement) (GBUAPCD, 2006b). 

In the modeling analysis emissions from individual dust source areas are simulated to assess 

whether they caused or contributed to an exceedance or violation of the National Ambient Air 

Quality Standards (NAAQS) for PM10. The Owens Valley planning area is currently designated 

as a federal nonattainment area for the 24-hour PM10 standard, which is set at 150 μg/m3

. 

Attainment of the 24-hour PM10 NAAQS is achieved when predicted concentrations are not 

above 150 μg/m3

 more than once per year on average. In the current study, the model attainment 

demonstration is performed for a four-year period with special attention to receptors locations 

that are at or above the 3,600 foot contour of Owens Lake. 

6.2 OVERVIEW OF THE DUST ID PROGRAM 

The District started a field monitoring program at Owens Lake in January 2000 to identify PM10

emission source areas, and to estimate their PM10 emissions and impacts on air quality at the 

shoreline. The data used in the 2008 SIP was collected during July 2002 through June 2006 

using the methods described in the Owens Lake Dust ID Field Manual (GBUAPCD, 2007). The 

field program was designed based on previous observations and field studies that suggest PM10

emissions are related to the flux of saltating sand-sized particles. 
Air Quality Modeling 

6-2 

Figure 6.1 is a map of Owens Lake showing the locations of the meteorological and PM10

monitoring stations. Figure 6.2 shows the locations of the sand flux monitoring network for the 

period from July 2005 through June 2006. Features of the Dust ID Program are as follows: 

• Co-located Sensits and Cox Sand Catchers (CSCs) were used to estimate hourly sand flux 

rates at each lake bed monitor site shown in Figure 6.2. Sensits measure the kinetic 

energy and the particle counts of sand-sized particles as they saltate (bounce) across the 

surface. CSCs are passive instruments used to collect sand-sized particles blown across 

the surface during a dust event. For a given period, the total mass of saltating sand was 

based on the CSC catch. The Sensits were then used to time-resolve the horizontal sand 

flux (Ono, et al., 2003a, Gillette, et al., 2004). 

• Hourly PM10 concentration data were collected at seven sites around Owens Lake using 

Tapered Element Oscillating Microbalance (TEOM) PM10 monitors. TEOMs are a 

USEPA-designated equivalent method for measurement of PM10 concentration. 

• Hourly surface meteorological data were collected at 13 District stations within the 

domain shown in Figure 6.1. These data were augmented by an additional two District 

sites south of the domain and up to three sites operated by the City during periods of the 

four year study. 

• A 915 MHz Radar Wind Profiler and Radio Acoustic Sounding System (RASS) were 

used to collect upper level wind and temperature measurements. The Wind Profiler was 

located at the Mill Site until it was removed during June 2004. 

• To help verify the location of dust source areas, time-lapse video cameras were installed 

at three sites to continuously record dust events during daylight hours and three human 

observers mapped dust source areas and plumes during the storms on regular workdays. 

In addition, the erosion boundaries of some source areas were mapped with the aid of a 

field crew using a Global Positioning System (GPS) after a storm. 

A large Geographic Information System (GIS) database was constructed using observations 

collected during the Dust ID Program. Using the GIS database, the District prepared maps 

displaying hourly sand movement, winds, visually observed plume and source area boundaries, 

and PM10 concentrations for dust events at Owens Lake during the study period. Owens Lake 

Dust ID Field Manual provides further detail (GBUAPCD, 2007). 

6.3 DISPERSION MODELING TECHNIQUES 

The CALPUFF modeling system was selected for assessing source contributions to observed 

PM10 concentrations and for the development of control strategies for the 2008 SIP. CALPUFF 

is the USEPA recommended modeling approach for long-range transport studies (40 CFR Part 

51, Appendix W). USEPA also recommends application of the modeling system on a case-bycase basis to near-field dispersion problems where the three-dimensional qualities of the wind 

field are of interest. Observations during the Dust ID Program indicate dust events on Owens 

Lake are sometimes influenced by complex wind patterns, with plumes from the North Sand 

Sheet traveling in different directions than plumes from the South Sand Sheet. Both CARB and 

the USEPA approved the application of CALPUFF during their review of the modeling protocol 

for the 2003 SIP. 
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6-3 

6.3.1 Preparation of the Meteorological Data 

Three-dimensional wind fields for CALPUFF were constructed from surface and upper air 

observations using the CALMET meteorological preprocessor program. CALMET combines 

surface observations, upper air observations, terrain elevations, and land use data into the format 

required by CALPUFF. In addition to specifying the three-dimensional wind field, CALMET 

also estimates the boundary layer parameters used to characterize diffusion and deposition by the 

CALPUFF dispersion model. 

The model domain shown in Figure 6.3 is a 34 km-by-48 km (21 by 30 mile) area centered on 

Owens Lake. The extent of the model domain was selected to include the “data rich” study area, 

terrain features that act to channel winds, and receptor areas of interest. The meteorological grid 

used a one-kilometer horizontal mesh size with ten vertical levels ranging geometrically from the 

surface to four kilometers aloft. 

The majority of the necessary surface meteorological data came from the District’s network of 

ten-meter towers shown in Figure 6.1 and two District stations south of the domain. In addition 

to the District’s network, surface data from the City’s field programs at Owens Lake were used 

when available. Cloud cover and ceiling height observations were also obtained from the Bishop 

Airport and China Lake. Cloud cover is a variable used to estimate the surface energy fluxes and, 

along with ceiling height, is used to calculate the Pasquill stability class (a classification of 

atmospheric stability). 

The upper air data for construction of the wind fields and estimation of mixing heights with 

CALMET included local hourly observations from the Mill Site Wind Profiler and regional 

twice-daily upper air soundings from Desert Rock Airport (Mercury, Nevada) and China Lake 

Naval Air Station. The Wind Profiler with RASS samples wind and temperature from 300 ft, up 

to 15,000 ft with a vertical resolution as low as 200 ft. The Wind Profiler data were used until 

the instrument was removed in June 2004. 

The China Lake and Desert Rock twice daily soundings were used to extend the profiles aloft 

near the profiler and, following removal of the Wind Profiler, for upper level temperature lapse 

rates. Upper level winds within the domain depend on either actual Wind Profiler measurements 

or extrapolation of the local surface wind measurements using the wind profile characteristics 

derived from the Wind Profiler studies 

6.3.2 PM10 Emissions and Source Characterization 

This section provides an overview of the methods discussed in Section 4.3, which were used to 

calculate hourly wind-blown PM10 emissions for dispersion model simulations at Owens Lake. 

PM10 emission fluxes from source areas at Owens Lake were calculated using hourly sand flux 

activity data and the following simple relationship: 

Equation 6.1

PM10 = Kf × q 
Air Quality Modeling 

6-4 

Where: 

PM10 = the vertical PM10 emission flux (g/cm2

/hr) 

Kf = an empirical constant (referred to as the K-factor) 

q = the horizontal sand flux measured at 15 cm above the surface (g/cm2

/hr) 

Field data at Owens Lake suggest the horizontal sand flux at a single measurement height is 

proportional to the total horizontal sand flux and is a good indicator of wind erosion processes 

generating PM10 emissions. The total horizontal sand flux is a strong function of both the surface 

shear stress and the properties of the soil at the time of the event. Rather than trying to predict 

the horizontal sand flux using wind speed and properties of the soil, sand movement on the lake 

was parameterized using the network of paired Sensit and CSC measurements. 

Experimental and theoretical evidence suggest Kf is a property associated with the binding 

energies of the soil and is relatively independent of the surface stress induced by wind speed. On 

Owens Lake this empirical constant appears to vary by season, due to the presence or absence of 

protective salt crusts, and by source areas grouped together by surface soil textures. In the Dust 

ID Program Kf was inferred using the modeling practices described by Ono, et al. (2003a). 

Simulations were performed using a first guess for Kf and the measured hourly sand flux data. 

Following a screening analysis, predictions were then compared to observed PM10

concentrations and a revised estimate for Kf was obtained. The screening criteria were selected to 

ensure a strong relationship existed between the source area and the downwind PM10 monitoring 

site. The source-to-receptor relationship was established using wind direction data, sand flux 

data for the source area, the maps generated from visual observations, and source contribution 

matrices based on the modeling. 

The screened estimates for Kf were then grouped together by period and source area. Four Kfactor areas were selected based on common surface soil properties. These source areas are 

identified as: the Keeler dunes, North area, Central area and the South area (see the maps in 

Figures 4.3 and 4.4). The periods were subjectively based on inspection of the variability 

exhibited in time series plots and considerations of the precipitation-temperature history thought 

to affect surface crusting, surface erodibility, and the formation of efflorescent salts on the 

surface. For each period and source area with nine or more hourly Kf estimates remaining after 

the screening process, a revised Kf was derived based on the 75th percentile of the ensemble. 

During periods and for source areas where nine data pairs were not available, the seasonal 2003 

SIP Kf defaults for the areas were used 

Table 4.1 lists the Kf estimates used in the 2008 SIP from the data collected during the four year 

period and the methods outlined above. Figures 4.10 through Figure 4.13 show the temporal 

variability of the Kf estimates assigned to each of the four general source areas. The hourly Kf

plots show the seasonality of the data and provide an indication of the uncertainty of the 

estimates used in the 2008 SIP. 

The CALPUFF simulations at Owens Lake are sensitive to source area configuration. Emissions 

were varied hourly according to Equation 6.1. The paired Sensit and CSC measurements were 

assumed to be representative of the horizontal sand flux for irregularly shaped source areas near 

the sand flux site. The following general rules were used to characterize and map source areas on 

the lake bed: 
Air Quality Modeling 

6-5 

• Actual source boundaries were used when available to delineate emission sources in the 

simulations. Actual source boundaries were determined using a weight-of-evidence 

approach considering visual observations, GPS mapping, and surface erosive 

characteristics. Erosive characteristics that were considered when defining a source 

boundary include properties of the soil, surface crusting, wetlands, and the proximity of 

the brine pool. 

• Source boundaries were also defined based on the DCM locations. For example, sand 

flux measurements outside the DCM were assumed to apply up to the boundary of the 

DCM. Sand flux measurements inside the DCM were assumed to apply to the area inside 

the DCM. 

• Source areas were represented by a series of rectangular cells that generally conform to 

the actual shape of the source area and share the same hourly sand flux rates as the sand 

flux site representing that source area. Smaller rectangles were used as the active areas 

became smaller during the study period and in some instances near the shoreline to better 

represent source areas where predicted concentrations are expected to be particularly 

sensitive to the source area configuration. 

Figure 6.4 shows the annual source configurations used in the 2008 SIP attainment 

demonstration for the period from July 2005 through June 2006. The location, size, Sensit, and 

general source area assignments for each source cell during the four annual periods are shown in 

Appendix B. The number of individual sources simulated varied from 1500 to over 2000 

depending on the year of the simulation. The total simulated area ranged from 77 to 130 square 

kilometers. 

With the exception of the Keeler dunes, PM10 emissions from non-Owens Lake PM10 windblown 

sources are not included in the model as individual sources. Due to the difficult nature of 

accurately estimating emissions from these much smaller, sporadic sources, non-Owens Lake 

PM10 emissions are included as contributors to the background concentration (see Section 6.3.4). 

This also includes contributions from upwind sources that may be outside the modeling domain. 

6.3.3 CALPUFF Options and Application 

The application of CALPUFF involves the selection of options controlling dispersion. Although 

the simulations are primarily driven by the meteorological data, emission fluxes, and source 

characterization, the dispersion options also affect predicted PM10 concentrations. In this study, 

the following options were selected for the simulations: 

• Dispersion according to the conventional Pasquill-Gifford dispersion curves. 

• Near-field puffs modeled as Gaussian puffs, not elongated “slugs. 

• Consideration of dry deposition and depletion of mass from the plume. 

Dry deposition and subsequent depletion of mass from the dust plumes depend on the particle 

size distribution. Several field studies have collected particle size distributions within dust 

plumes at Owens Lake. Based on results from Niemeyer, the CALPUFF simulations assumed a 

lognormal distribution with a geometric mean diameter of 3.5 μm and a geometric standard 

deviation of 2.2 (Niemeyer, et al., 1999). These variables are based on the average of 13 dust 
Air Quality Modeling 

6-6 

plume size distributions reported by Niemeyer between June 1995 and March 1996 at different 

locations within the Airshed. 

6.3.4 Background PM10 Concentrations 

The dispersion model simulations include only wind-blown emissions from the source areas with 

sand flux activity shown in Figure 6.4 and in Appendix B. During high wind events other local 

and regional sources of fugitive dust also contribute to the PM10 concentrations observed at the 

monitoring locations. A constant background concentration of 20 μg/m3 was added to all 

predictions to account for background sources. The constant background was calculated from the 

average of the lowest observed PM10 concentrations for each dust event when 24-hour PM10

concentrations at any of the sites were above 150 μg/m3. To avoid including impacts from lake 

bed dust source areas in the background estimate, the procedures used a simple wind direction 

filter to exclude hours when the lake bed may have directly influenced observed PM10

concentrations. Such hours were removed and daily average background concentrations were 

recalculated based on the remaining data (Ono, 2002). 

6.4 ATTAINMENT DEMONSTRATION 

The CALPUFF modeling techniques described in previous sections and in Appendix B were 

applied to assess control strategies proposed for the 2008 Owens Valley PM10 SIP. These control 

strategies are described in Chapters 7 and 8. This section of the report describes the methods 

used to demonstrate attainment of the 24-hour PM10 NAAQS and presents the results of the 

analysis. 

PM10 emissions were simulated using the hourly sand flux data collected during July 2002 

through June 2006 based on the area source configuration shown in Figure 6.4 and Appendix B. 

The characterization of PM10 emissions follows the general techniques discussed above 

described more fully in Section 4.3. 

Emissions from the Keeler dunes were excluded from the simulations to assess attainment. The 

District believes emissions from the Keeler dunes and several other off-lake sources are 

primarily caused by deposition from the lake bed sources. As discussed in more detail in Section 

7.5, the District will work with the City and other federal, state and local agencies to develop a 

plan to control dust emissions from the Keeler dunes. Any PM10 control measures necessary for 

the Keeler dunes will be implemented by or before December 31, 2013 in order to demonstrate 

attainment of the federal standard by 2017. 

The influence of non-lake bed sources is included in the simulations through the use of a 

background concentration. As discussed in Section 6.3.4, a background concentration of 

20 μg/m3 was added to all model predictions. 

Attainment of the NAAQS was assessed using concentration predictions at the historic shoreline 

in addition to receptors at the monitoring stations. Attainment of the 24-hour NAAQS is 

achieved when the fifth highest 24-hour PM10 concentration in four years at each receptor is less 

than 150 μg/m3. Predictions were obtained at more than 460 receptor locations placed at the 

historic shoreline (approximately at the 3600 foot elevation) of Owens Lake. 
Air Quality Modeling 

6-7 

6.4.1 Control Strategy Analysis 

The control strategy assessed in this study was developed as part of the 2006 Settlement 

Agreement between the District and the City. The location of 2003 SIP DCAs and the additional 

areas for control from the Settlement Agreement are shown in Figure 7.1. The 2003 SIP 

attainment demonstration evaluated controls for the existing DCAs. The Supplemental Dust 

Control Areas were identified through the Supplemental Control Requirement provision of the 

2003 SIP. The 2008 SIP attainment demonstration evaluates these additional areas: Channel 

Areas, Supplemental DCAs, and Study Areas. 

For the 2008 SIP and the controls in the 2006 Settlement Agreement, the City developed a 

customized spreadsheet containing the source-receptor contributions for every predicted 

concentration greater than 50 μg/m3. Control efficiencies were assigned based on control type, 

but allowed to vary within certain DCAs. The spreadsheet starts with the controls specified in the 

2003 SIP and then adds controls to new areas identified in the Settlement Agreement. These 

additional areas begin with no control and then are repetitively increased until all shoreline 

receptors are predicted to have PM10 concentrations less than 150 μg/m3. The District then 

checks the resulting set of controls by re-applying the CALPUFF modeling system. 

Control efficiencies for the 2008 SIP attainment demonstration are discussed in Section 7.3. 

Areas with variable levels of control in the Settlement Agreement are shown in Figure 7.2. These 

same efficiencies were used in the 2008 attainment demonstration, except for the Study Areas 

(S1, S2, S3, and S4 in Figure 7.1). The Study Areas were assumed to have no controls as none 

are required by the Settlement Agreement. 

PM10 emissions from the Keeler dunes (see discussion above) and the 2003 SIP DCAs were not 

considered in the 2008 attainment demonstration. Dust control measures were not fully 

implemented in the 29.8 square mile 2003 SIP DCAs during the modeling period from July 2002 

through June 2006. Thus it was not known whether emissions from these areas would be 

representative of future controlled conditions. For the purpose of the 2008 SIP to establish 

control levels for the supplemental DCAs in the Settlement Agreement, it was assumed that no 

emissions were coming from the 2003 DCAs. Controls for these 2003 SIP DCAs were 

considered in the 2003 attainment demonstration. 

6.4.2 Attainment Demonstration Results 

The predicted fifth highest 24-hour PM10 concentrations at receptors located along the shoreline 

are shown in Figure 6.5 based on a CALPUFF simulation of the control strategy discussed 

above. The numbers of times the PM10 predictions are above 150 μg/m3 at shoreline receptors 

are displayed in Figure 6.6. Although four predictions are above the 24-hour NAAQS, the 

design or fifth highest concentration at the same receptor was 147 μg/m3 for the four-year 

simulation. The modeling analysis demonstrates attainment of the 24-hour PM10 NAAQS using 

the Settlement Agreement control strategy. 

The highest concentrations are along the shoreline at locations influenced by the Study Areas. 

These areas are being investigated, but there are currently no plans to control these areas. The 

Study Areas have relatively high emissions for a few days in the four-year simulations. 
Air Quality Modeling 

6-8 

However, the frequency of such events from these areas is not high enough to cause violations of 

the 24-hour PM10 NAAQS. 

6.5 REFERENCES 

GBUAPCD, 2006b. Great Basin Unified Air Pollution Control District, Settlement Agreement 

between the District and the City to resolve the City’s challenge to the District’s 

Supplemental Control Requirement determination issued on December 21, 2005 and 

modified on April 4, 2006, GBUAPCD, Bishop, California, December 4, 2006. 

GBUAPCD, 2007a. Great Basin Unified Air Pollution Control District, Owens Lake Dust ID 

Field Manual. GBUAPCD, Bishop, California, January 24, 2007. 

Gillette, et al., 2004. Gillette, Dale, Duane Ono, Ken Richmond, A Combined Modeling and 

Measurement Technique for Estimating Wind-Blown Dust Emissions at Owens (dry) 

Lake, CA, Journal of Geophysical Research, Volume 109, January 17, 2004. 

Niemeyer, et al., 1999. Niemeyer, T.C., D.A. Gillette, J.J. Delisui, Y.J. Kim, W.F. Niemeyer, T. 

Ley, T.E. Gill, and D. Ono, Optical Depth, Size Distribution and Flux of Dust from 

Owens Lake, California, Earth Surfaces Processes and Landforms, 24: 463-479, 1999. 

Ono, 2002. Ono, Duane, Memo on Owens Lake Background PM10 Calculation Method, Great 

Basin Unified Air Pollution Control District, Bishop, California, September 13, 2002. 

Ono, et al., 2003a. Ono, Duane, Ellen Hardebeck, Scott Weaver, Billy Cox, Nikolai Barbieri, 

William Stanley, Ken Richmond, and Dale Gillette, Locating and Quantifying WindBlown Dust PM10 Emissions at Owens Lake, California, A&WMA’s 96th Annual 

Conference & Exhibition, June 2003, San Diego, California, Paper #69487, Air & Waste 

Management Association, Pittsburgh, Pennsylvania, June 2003. 

Scire, et al., 2000. Scire, J.S.; Strimaitis, D.G.; Yamartino, R.J. A User’s Guide for the 

CALPUFF Dispersion Model (Version 5). Earth Tech, Inc., 196 Baker Avenue, Concord, 

MA 01742, January 2000.
400 405 410 415 420 425 430

East - West UTM (km)

4010

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4020

4025

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North - South UTM (km)

Teom Site

Met Site - 0203

Met Site - 0506

Lone Pine

Keeler

Olancha

Dirty Socks

Shell Cut

Flat Rock

Mill Site

Stanley

Wave

A-Tower

Delta

B-Tower

Cottonwood

Cartago

 

 

Figure 6.1 - Owens Lake PM10 and meteorological monitoring network
406 408 410 412 414 416 418 420 422 424

East - West UTM (km)

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7190 7191 7192 7193 7195 7196 7199

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7604 7605 7606 7607 7608 7609 7610

7627 7628 7629 7631 7632 7633

7650 7651 7655

7673 7674 7676 7677 7678

7696 7697

 

 

Figure 6.2 - Owens Lake sensit network for July 2005 through June 2006
 

 

Figure 6.3 - Model domain and one-km mesh size terrain (m) 

400 405 410 415 420 425 430

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Figure 6.4 - Area source configuration for July 2005 through June 2006
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Max = 147 μg/m3

 

 

Figure 6.5 - Fifth highest 24-hour PM10 (μg/m3) at shoreline receptors, no Keeler 

dunes, after controls (every 4th plotted) 
406 408 410 412 414 416 418 420 422 424 426

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Figure 6.6 - Number of 24-hour PM10 predictions greater than 150 μg/m3

 at shoreline 

receptors, no Keeler dunes, after controls (every 4th plotted)