A combined modeling and measurement technique for estimating

windblown dust emissions at Owens (dry) Lake, California

Dale Gillette

Fluid Modeling Facility, Applied Modeling Research Branch, Atmospheric Modeling Research Division, Air Resources

Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina, USA

Duane Ono

Great Basin Unified Air Pollution Control District, Bishop, California, USA

Kenneth Richmond

MFG, Incorporated, Lynnwood, Washington, USA

Received 30 January 2003; revised 1 October 2003; accepted 10 November 2003; published 17 January 2004.

[1] The problem of dust emissions from playa sources is an important one both in terms

of human health and in terms of global dust issues, distribution of loess, and mineral

cycling. A refined method of modeling atmospheric dust concentrations due to wind

erosion was developed using real-time saltation flux measurements and ambient dust

monitoring data at Owens Lake, California. This modeling method may have practical

applications for modeling the atmospheric effects of wind erosion in other areas.

Windblown dust from the Owens Lake bed often causes violations of federal air quality

standards for particulate matter (PM10) that are the highest levels measured in the United

States. The goal of this study was to locate dust source areas on the exposed lake bed,

estimate their PM10 emissions, and use air pollution modeling techniques to determine

which areas caused or contributed to air quality violations. Previous research indicates that

the vertical flux of PM10 (Fa) is generally proportional to the total horizontal saltation

flux (q) for a given soil texture and surface condition. For this study, hourly PM10

emissions were estimated using Fa = K 0 m15, where m15 is the measured sand flux at

15 cm above the surface, and K 0

was derived empirically by comparing air quality model

predictions to monitored PM10 concentrations. Hourly sand flux was measured at 135 sites

(1 km spacing) on the lake bed, and PM10 was monitored at six off-lake sites for a

30 month period. K 0 was found to change spatially and temporally over the sampling

period. These changes appeared to be linked to different soil textures and to seasonal

surface changes. K 0

values compared favorably with other Fa/q values measured at Owens

Lake using portable wind tunnel and micrometeorological methods. Hourly trends for the

model-predicted PM10 concentrations agreed well with monitored PM10 concentrations.

Dust production was estimated at 7.2 104 t of PM10 for a 12 month period. A single

storm accounted for 9% of the annual dust emissions at 6.5 103 t. The modeling results

were used to identify 77 km2 of dust-producing areas on the lake bed that will be

controlled to attain the federal air quality standard for PM10. INDEX TERMS: 0322

Atmospheric Composition and Structure: Constituent sources and sinks; 0305 Atmospheric Composition and

Structure: Aerosols and particles (0345, 4801); 1809 Hydrology: Desertification; 1815 Hydrology: Erosion

and sedimentation; KEYWORDS: dust, desertification, wind erosion

Citation: Gillette, D., D. Ono, and K. Richmond (2004), A combined modeling and measurement technique for estimating

windblown dust emissions at Owens (dry) Lake, California, J. Geophys. Res., 109, F01003, doi:10.1029/2003JF000025.

1. Introduction

[2] The problem of dust emissions from playa sources is

an important one both in terms of human health and in terms

of global dust issues, distribution of loess, and mineral

cycling. The methodology developed for this study may

have practical applications globally to help model the

atmospheric effects of wind erosion in other areas. Because

of the optical properties of dust and estimations of the large

emissions of dust from such sources as the Sahara desert,

considerable work has been done on global-scale modeling

of dust sources based on first principles of dust emission

and large-scale meteorology (for one example of many, see

Ginoux et al. [2001]). J. Prospero (personal communication,

2002) suggested that dust sources of global importance may

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, F01003, doi:10.1029/2003JF000025, 2004

Copyright 2004 by the American Geophysical Union.

0148-0227/04/2003JF000025$09.00

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be focused on large-scale topographic depressions located

in desert regions. Since Owens Lake is a topographic

depression of area more than 100 km2

, where the rainfall

is less than 15 cm per year, measurements and modeling

there could be of value for global-scale models. Future work

could include projects such as applying the Owens Lake

modeling method to other wind erosion areas, improving

global-scale models by validating their predictions against

measurements taken at Owens Lake, or improving on firstprinciple-based emission estimates using the large data set

collected at Owens Lake.

[3] The dried bed of Owens Lake in Inyo County,

California, is the largest single source of particulate matter

pollution in the United States (U.S. Environmental Protection Agency (EPA), Air Data, 2003, available at http://

www.epa.gov/air/data). The lake bed covers an area

approximately 285 km2 and is a natural saline lake at the

terminus of the Owens River. When the Owens River was

diverted by the City of Los Angeles into an aqueduct in

1913, it caused the lake to become virtually dry by 1928. A

small permanent brine pool is what remains at the lowest

part of the basin. It is surrounded by exposed, dry alkali

soils. Windblown dust from the exposed lake bed can cause

24 hour average PM10 (particulate matter less than a 10 mm

nominal aerodynamic diameter) concentrations to exceed

12,000 mg m3 at the historic shoreline; the federal PM10

standard is set at 150 mg m3. Dust storms often affect the

health and welfare of people living within 80 km of the lake.

Air pollution levels in communities surrounding Owens

Lake exceeded 400 mg m3 on over 330 days from 1995

to 2002, a level that would trigger a stage 1 health advisory

to protect sensitive individuals such as children, the elderly,

and people with heart or lung disease [Great Basin Unified

Air Pollution Control District (GBUAPCD), 2003].

[4] In 1993 the U.S. EPA designated the southern Owens

Valley as a ‘‘serious’’ nonattainment area for particulate

matter. As a result, an air quality plan was developed to

implement a control strategy that would bring the area into

attainment with federal standards by 31 December 2006. By

the end of 2003, about 50 km2 of the most emissive areas of

the lake bed will be controlled by shallow flooding and salt

grass cover. The focus of this paper is on the method that

was used to locate windblown dust source areas on the

Owens Lake bed, to estimate the vertical flux of PM10, and

to determine which source areas may cause or contribute to

violations of the air quality standards. Through this study,

additional areas were identified for control measure implementation that, when controlled, will bring the area into

attainment with the PM10 standard by the statutory deadline.

2. Method for Estimating the Vertical Flux of

PM10 Emissions

[5] The working hypothesis for this study was that the

vertical flux of PM10 (Fa), measured in g m2 s

1

, is

proportional to the total horizontal flux of sand-sized

particles (q); that is,

Fa 1⁄4 K q; ð1Þ

where K is a dimensional constant having units of m1 and q

(g m1 s

1

) is the horizontal flux of sand-sized particles in a

1-m-thick layer above the ground, as defined in equation (2):

q 1⁄4

Z1m

0

C zð ÞVH ð Þz dz: ð2Þ

Here, C(z) is the concentration of sand-sized grains at height

z, and VH is the horizontal velocity of the sand grains.

Experimental evidence for a relationship between Fa and q is

discussed in section 2.1.

[6] As shown conceptually in Figure 1, wind erosion

involves particles that creep along the ground and sandsized particles or agglomerates that bounce or saltate across

the surface. These creeping and saltating particles loosen

other particles and sandblast the surface, causing finer

particles, including PM10, to be ejected and to mix vertically

in the turbulent air stream. The amount of PM10 emitted is

generally proportional to the horizontal saltation flux. Using

this working hypothesis, Fa could be estimated from sand

Figure 1. Conceptual depiction of the wind erosion process with a Cox Sand Catcher (CSC) and Sensit

positioned in the saltation zone. The mass of saltating particles collected by the CSC was time resolved

using the Sensit’s electronic response to estimate hourly sand flux at 135 sites. PM10 concentrations

measured at shoreline PM10 monitors were used to infer the ratio of the vertical PM10 flux to the

horizontal sand flux using dispersion modeling.

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flux measurements taken with instruments placed in the

saltation zone. This zone typically ranges from the ground

to about 1 m above the surface. As discussed in section 3,

the ratio Fa/q can be inferred by comparing monitored PM10

concentrations with the predicted concentrations from an air

quality model. It was assumed that the relationship between

Fa and q would remain fairly stable in all but a few

exceptional circumstances that may be unique to Owens

Lake and that these exceptional circumstances would not

account for more than a small fraction of the total PM10.

The hypothesis that Fa could be estimated from q made

possible a program of efficient sampling based on sand flux

measurements that could reveal the location of sources of

PM10 and also characterize their dust production rates and

the time and duration of their activity.

[7] Sand flux is often modeled instead of measured. For

example, Gillette et al. [1996] and Owen [1964] showed

that sand flux is generally proportional to u* (u*

2  u*t

2

),

where u*t is the minimum aerodynamic threshold friction

velocity. Previous measurements at Owens Lake showed

that despite the uniform appearance of the lake bed, u*t was

highly variable in time and space and would have to be

closely monitored if it was used to model q. Because of this

variability, we opted not to model q but rather to use a more

direct measurement approach. For about the same effort to

monitor u*t we surmised that one could directly measure q

(or a surrogate in this case) and obtain a more accurate sand

flux rate.

[8] Although much of the lake bed produces dust, some

areas of the 285 km2 lake bed are consistently covered by a

durable crust or are wet and are normally nonemissive. The

potentially emissive areas for this study cover about

135 km2

. Figure 2 shows a map of the study area. The

portion of the lake bed not covered by the sampling grid

generally indicates areas that are normally nonemissive

areas. Some source areas inside the grid have been observed

to be active all year and may be highly emissive, while

others were seasonal and sometimes sporadic. The focus of

this study was to better characterize the time, place, and

strength of erosion activity for areas within the sampling

grid. This information would be used with the Calpuff

model [Scire et al., 2000a] to determine in which areas

dust controls should be placed. As shown in Figure 2, 135

sand flux sites, spaced 1 km apart, were installed to monitor

hourly sand flux rates within the potential erosion area.

[9] The sampling grid was separated into four source

areas, as shown in Figure 2, for the purpose of determining

large-scale ratios for Fa/q for each area. These areas were

selected because of differing geomorphology or source

activity that seemed to be somewhat independent of each

other. The Keeler Dunes, for example, are sand dunes that

differ in geomorphology from the surrounding area and

were a frequent source of dust. The ‘‘north area’’ and the

‘‘south area’’ were seen to be strong source areas that often

started at different times during dust storm days. Activity in

the ‘‘central area’’ was more sporadic than in the other three

areas.

2.1. Experimental Evidence for a Relationship

Between Fa and q

[10] Bagnold [1941] and Iversen et al. [1976] showed

that there is a minimum aerodynamic threshold friction

velocity (u*t ) for individual particles to be entrained into

the air stream by aerodynamic forces from a smooth

surface. This u*t varies with particle size, roughness of

the surface, crusting of the soil surface, soil texture, soil

moisture, and salt content [Gillette et al., 1980; Gillette et

al., 1982; Gillette, 1988; Breuninger et al., 1989]. However, for observations of natural wind erosion for a range

of friction velocities smaller than u*t, one can find emissions of 10 mm particles [Cahill et al., 1996; Gillette,

1977]. Because aerodynamic forces cannot have been

responsible for them and because sandblasting of the soil

was observed at the time of their emissions, it is highly

probable that sandblasting was the dominant mechanism

for their input.

[11] The chemical composition of PM10 is the same as

aggregated coatings of PM10-sized particles on sandsized particles [Gillette and Walker, 1977]. Evidence

showed that some individual PM10 particles are similar

to clay platelets that adhere to the surfaces of larger

quartz particles. As the clay particles were not found to

exist in the parent soil except as coatings on quartz

particles, the airborne particles of the fine mode were

almost certainly removed from quartz grains by collisions with the larger particles. The collisions (sandblasting) acted to release portions of aggregated particles and

may also have broken the crystalline structure of mineral

particles.

[12] Experiments by Shao et al. [1993] and Houser and

Nickling [2001] showed that particle-particle interaction

(sandblasting) on an erodible surface is an important and

probably dominant mechanism that produces suspended

particle flux. Both experiments used wind tunnels, the

floors of which contained clay-rich material. Experiments

by Shao et al. [1993] used finely divided clay, while

Houser and Nickling’s [2001] experiment used crusted

clay material. In both experiments, high winds first

yielded exceedingly small amounts of fine material, then

large amounts of PM10 when sand-sized grains were fed

into the stream. Aerodynamic forces alone were able to

entrain only small amounts of PM10. At the same wind

speeds, however, sandblasting caused steady entrainment

of PM10.

[13] Gillette’s [1977] work, which simultaneously measured horizontal fluxes of sand-sized particles (q) and

vertical PM10 fluxes (Fa) on agricultural soils in Texas,

formed a body of experimental work with which to compare

results at Owens Lake. Gillette’s results showed that the

ratio Fa/q is consistent when grouped by soil-surface-texture

type. Testing the largest group of observations for a single

texture type (fine sand) of Gillette’s data further led us to

accept the null hypothesis that there is no correlation

between Fa/q and u* or between Fa/q and q at the 5% level

of significance, even though the data showed that q is a

strong function of u*, in agreement with the theory of Owen

[1964]. Results by Nickling and Gillies [1989] and by

Alfaro and Gomes [2001] show that Fa/q is largely independent of friction velocity (and also q) for friction velocities well above the threshold friction velocity. However, for

friction velocities closer to the threshold friction velocity,

Fa/q seems to increase with friction velocity. Since dust

emissions close to the threshold friction velocity are much

smaller than for those well above, the overall behavior is

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dominated by Fa/q being largely independent of friction

velocity.

2.2. Theoretical Justification for a Relationship

Between Fa and q

[14] The sandblasting abrasion rate is proportional to the

kinetic energy flux from the sand-sized particles, which is

proportional to u*

3

. Greeley and Iversen [1985] summarized a body of research directed toward the aeolian

abrasion of rocks and minerals and concluded that the mass

of rock or mineral abraded per impact of sand-sized

particles is directly proportional to the kinetic energy of

the impacting particle. From dimensional analysis, Gillette

and Stockton [1986] showed that the kinetic energy flux of

the wind is proportional to the cube of the friction velocity.

Since we can assume that the kinetic energy of the wind is

absorbed and carried to the surface by saltating particles at

the surface, it follows that the vertical kinetic energy flux

carried by saltating particles is proportional to u*

3

.

[15] According to the classical formulations of Bagnold

[1941] and Owen [1964] as well as to many formulae listed

by Greeley and Iversen [1985], the horizontal mass flux of

saltating sand-sized particles (q) is roughly proportional to

u*

3 for u* well above threshold. Since both abrasion and the

Figure 2. Map of Owens Lake showing the locations of CSCs and Sensit instruments (CSCs and

Sensits are at the same locations), PM10 monitors, and meteorological (met) towers. Subareas of the dry

lake bed are drawn in the north area, south area, central area, and Keeler Dunes.

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horizontal mass flux of saltating sand-sized particles are

roughly proportional to the same variable, they are therefore

roughly proportional to each other. Physically, sand-sized

particles localize kinetic energy onto small target areas,

while the energy from fluid transfer is spread out over a

much larger area.

[16] The Shao et al. [1993] expression for the particle

flux of suspended particles produced by the impact of

saltating particles is

Fa 1⁄4 gmd

g

y

qf VH

u

*

 !; ð3Þ

where Y is binding energy, g is a constant, md is mass per

particle, g is acceleration of gravity, VH is the horizontal

velocity of the saltating particle, and u* is friction velocity.

Both g and g are constants, and the function f (VH/u*) has

been shown by Owen [1964] to be almost constant.

Therefore the ratio Fa/q may be expressed as

Fa

q 1⁄4  mdy ; ð4Þ

where  = gg f (VH/u*). Using equation (4) to evaluate Fa/q

requires evaluation of the binding energies of suspensionsized particles to the soil and the sizes of typical saltation

particles. Such evaluation, however, is a difficult task.

[17] Subtleties within the size distribution of PM10 are

caused by sandblasting. Alfaro et al. [1997] proposed a

theory following along the lines of the Shao et al. [1993]

theory: that the size distributions of the saltating particles

are vitally important because the kinetic energy of individual saltating grains, along with the kinetic energy required

to release suspendible particles, determines the quantity and

size distribution of the suspended aerosol produced by

sandblasting. Details of the size distribution of particles

smaller than 10 mm are explained in Alfaro et al.’s [1997]

theory by the sandblasting mechanism.

[18] The theory of Lu and Shao [1999] expresses Fa as

proportional to either q or to qu*. The case in which Fa is

proportional to qu* assumes a lifting mechanism other than

sandblasting. Such a mechanism should be detectable for

Owens Lake by plotting Fa/q versus q, friction velocity, or

wind speed. If Fa/q is constant with wind speed, the Owens

Lake data will be supportive of a sandblasting mechanism

for dust emission. If Fa/q increases roughly proportional to

wind speed, the Owens Lake data will be supportive of Lu

and Shao’s [1999] alternative theory for dust emission.

2.3. Possibility of Non-Saltation-Driven

PM10 Emissions

[19] Direct suspension of particles may occur when wind

speeds are high enough or when sand-sized and larger

particles are not the dominant loose particles on a surface.

The emission of PM10 by the erosion of salt ‘‘fluff’’ has

been observed at Owens Lake for brief periods following

efflorescence of salt. It is possible that during these brief

periods, PM10 may be emitted by direct suspension. When

only fine-grain sediments are available as loose particles on

the surface, direct entrainment can occur without the necessity of sandblasting. Ablation of this fluff does not seem to

be correlated with significant sand-sized particle movement

at the beginning of dust storms [Saint-Amand et al., 1986].

During the emission of fluff the weak structure of this

material may well be crushed by a short initial surge of

wind, after which direct aerodynamic entrainment can take

place. Also, since the salt fluff is limited in supply, its PM10

flux is limited both in total mass and in time. From

observations over a period of years we estimate that the

contribution of total PM10 flux by direct aerodynamic

entrainment is small. For example, one of the authors

observed dust storms at Owens Lake commencing on

11 March 1993 and ending with rainfall on 25 March 1993.

White salt-rich aerosol from fluff was produced for only a

half hour to an hour, whereas the total time of wind erosion

accompanied by vigorous sand-sized particle movement was

about 35 hours.

[20] Finally, the term ‘‘sand-sized particles’’ is used rather

than sand in this study because several areas of Owens Lake

have little sand but nonetheless have an abundance of sandsized particles. An example of such an area is the clay rich

area to the northeast of Dirty Socks. Here, clay-rich sediments can aggregate and crack into sand-sized aggregates.

These sand-sized aggregates act very similarly to sand

particles composed entirely of a solid piece of only one

mineral.

2.4. K Factor Approach and Variability of the

Vertical Flux of PM10 Dust

[21] K is defined as the ratio of Fa to q:

K 1⁄4 Fa=q: ð5Þ

For a specific time, K and q are variable quantities for a

given location that may be expressed as overall means for

the lake bed plus fluctuations for given positions:

q 1⁄4 q þ q0 ð6Þ

and

K 1⁄4 K þ k0: ð7Þ

Both sums of all q 0 and all k 0 are zero. For a single storm

the vertical flux of PM10 dust at a particular square

kilometer may be expressed as

Fa 1⁄4 Kq þ k0q þ k0q0: ð8Þ

Analysis of Gillette’s [1977] data showed that q and K are

not correlated; that is,

k0q0 1⁄4 0: ð9Þ

The middle term on the right-hand side of equation (8) (k0q)

has a mean of zero since the mean of k 0

is zero. Values of q

typically range from zero in some subareas to 1 g cm1 s1

for high wind speeds in a given area of the lake (several km2

in area); K values typically range two or more orders of

magnitude [Gillette et al., 1997a]. Consequently, q has a

larger range of variability than K. Our results show that

average K factors may change by one order of magnitude

and that q may change by three orders of magnitude

(excluding q = 0).

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[22] One can grid the lake surface such that individual

horizontal positions west-east and south-north may be

labeled ‘‘i’’ and ‘‘j,’’ respectively. For successive grid

(i and j) positions separated by 1 km an individual measurement of q or Fa at position i, j may be made. Individual

measurements of qij were used to represent q for a square

kilometer at position i, j. Then, the rate of total mass of dust

produced in a given storm is weighted by the area of each

measurement (1 km2

):

Mtot 1⁄4 X

ij

Kijqij 1 km2   1⁄4 K

X

i;j

qij 1 km2   1⁄4 Kq

X

i;j

1 km2  ;

ð10Þ

where

K 1⁄4 Mtot P

i;j

qij 1km2  

and where

q 1⁄4

P

i;j

qij 1km2  

P

i;j

1km2   :

[23] Mtot is estimated by measurements of PM10 concentrations at the shoreline of the lake bed and modeling of the

dust transport from the lake bed. Details of the modeling are

available in section 3.6.

[24] The ‘‘K factor’’ method of estimating the vertical

flux approximates the vertical mass flux of PM10 at each i, j

position by using the mean value of K for the area being

considered times the individual qij value at each position for

the estimate of Faij

. Thus for each position, equation (8) is

rewritten as

Faij 1⁄4 Kqij þ eij; ð11Þ

where eij is the difference between the true vertical flux of

dust at position i, j and Kqij (alternatively, the sum of the

last two terms of the right-hand side of equation (8)). The

mean of eij for the entire area is zero. The mean vertical

PM10 mass flux for the area of the lake considered is

Fa 1⁄4 Kq: ð12Þ

We did not evaluate eij of equation (11) since there was no

data on k 0

. This K factor method neglects the error term eij

to give an approximate value for the individual Faij,

Faij  Kqij; ð13Þ

so that each individual Faij has an error eij. Although

individual fluxes at specific locations have associated error

with the K factor method, the total flux for the lake is well

estimated. The mean K (K factor) is expressed (from

equation (10)) as

K 1⁄4 X

i;j

Kij P

qij

i;j

qij

2

6

4

3

7

5: ð14Þ

The mean K is often dominated by strong source subregions

of the lake that have large sand flux qs. Therefore the mean

K (K factor) may not be a good estimator of individual Ks

for weak source areas (small qs) if these Ks are significantly

different. Consequently, the estimation of K might be

representative of primarily the strong source areas of the

lake. For example, if the strong sources were eliminated,

this method would more accurately represent the weaker

source areas. During small emission episodes, K factor

measurements are expected to have more variability. This

variability may be real, or it may be caused by increased

error in the measurement of q since only parts of the square

kilometer cells may have active erosion during small events.

[25] Gillette et al.’s [1997a] alternative method of measuring vertical PM10 fluxes at Owens Lake might improve

estimation of Fa at individual points. However, this method

would require at least 10 particle concentration measurements for each of our 135 measuring locations on the lake

and an accuracy of measurement that may not be technically

achievable at this time. We opted to use the K factor method

based on the above analysis since it adequately estimated Fa

for strong source subareas on the lake surface for the large

and highly variable (in time) surface area with which we

were concerned.

3. Measurement and Modeling Method for the

K Factor Approach

[26] To implement the method given in section 2, a

combination of measurements and modeling was used.

The method relates dust source strength to sandblasting

using equation (1) so that Fa could be determined by

measuring a grid of q values for the entire lake bed and

finding values of K to represent each of the four subareas of

the lake bed surface shown in Figure 2. Spatial and temporal

Ks were derived empirically by comparing monitored concentrations to concentrations predicted by an air quality

model. These spatial and temporal Ks were then used with

measured hourly sand flux to estimate Fa and to model

ambient PM10 concentrations during each dust event.

[27] Hourly sand flux values were determined from a

sampling system that used two sand flux instruments. One

instrument was a passive sand collector that captured

saltating particles, the other an electronic sensor that

recorded the hourly particle counts or kinetic energy of

the saltating particles. The passive collector was a Cox

Sand Catcher (CSC), which collected saltating sand-sized

particles. The CSC mass was measured after 1 – 4 weeks of

continuous sampling. The collected mass was then time

resolved using electronic readings from a co-located Sensit

to estimate hourly sand flux rates during the collection

period. Co-located CSCs and Sensits provided hourly sand

flux data for 30 months at sites spaced 1 km apart on a

135 km2 area of the lake bed, as shown on the map in

Figure 2. The CSC inlet and the Sensit detector are both

positioned in the saltation zone at 15 cm above the surface.

This dual measurement technique is shown conceptually in

Figure 1.

3.1. Measurement of Long-Term Average q Using the

Cox Sand Catcher

3.1.1. Efficiency of the Cox Sand Catcher

[28] The K factor approach depends on the measurement

of q or a reliable surrogate for q. We chose to measure the

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horizontal flux of sand-sized particles at 15 cm as a

surrogate for q (see Figure 1). To use this surrogate

measurement, we must show that (1) the flux of sand-sized

particles at 15 cm can be measured reliably and that (2) the

sand-sized particle flux at 15 cm height is linearly related to

q. To accomplish the first requirement, the CSC, which was

used for this study, was calibrated with a widely used

collector, the Big Spring Number Eight (BSNE), manufactured by Custom Products of Big Spring, Texas.

[29] The CSC is a device designed to collect airborne

sand-sized particles at 15 cm moving horizontally from all

directions. This device has no moving parts, whereas the

BSNE is directed into the wind by an attached wind vane.

The CSC, which consists of PVC piping, is fabricated by

the Great Basin Unified Air Pollution Control District. A

schematic drawing of the CSC is given in Figure 3.

[30] BSNE and CSC collectors were placed on the Owens

Lake bed surface and were allowed to collect particles

during several dust storms during periods of high and low

wind speed. Figure 4 shows the mass collected by the

BSNE (standard) that points into the wind by an attached

vane and the CSC for the same location, collection height,

and collection period. The plot shows that the masses

captured by the two collectors are linearly related, with an

R2 value of 0.973. It was also determined by comparison

with the BSNE that the CSC has an apparent inlet size for

flux calculations of 1.435 cm2

. This relationship was used to

convert the CSC mass collection into a flux rate by dividing

the mass by the apparent inlet size for the time duration of

the collection period.

3.1.2. Relation of Mass Flux Measured at 15 cm to q

[31] The second relationship, that of the ratio of a 1 cm2

sand-sized particle mass flux from 14.5 to 15.5 cm height

(m15) to the sand-sized particle mass flux from 0 to 1 m

height (q), was also determined empirically. Observed horizontal mass fluxes m (units of mass per length squared per

time) at various heights were fitted to Shao and Raupach’s

[1992] empirical equation for saltation mass flux profiles:

m zð Þ1⁄4 c exp az þ bz2  : ð15Þ

Observed values of m(z) for 10 sets of m(z) profile data

obtained continuously on the lake surface were fit to

equation (15), and then, the integrated mass flux q, similarly

to equation (2), was estimated from

q 1⁄4

Z 1m

0

mdz; ð16Þ

where q has the units mass/(length time). The mean value

of (m15/q), where m15 is the mass flux (mass per area per

Figure 3. (a) Photograph and (b) schematic drawing of a

CSC.

Figure 4. Mass captured by a side-by-side CSC and Big

Spring Number Eight airborne particle collector.

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time interval) for heights 14.5– 15.5 cm, was 0.024 cm1

(2.4 m1), with a standard deviation of 0.002 cm1 (0.2 m1).

Values for the shape factors a and b of equation (17) along

with their standard deviations in parentheses for the data

were 0.078 (0.020) and 0.0004 (0.0003), respectively. The

value of the more variable factor c was 2.15 (2.55). Gillette et

al. [1997b] calculated the ratio of sand-sized particle mass

flux from 9.5 to 10.5 cm height to q for many airborne particle

profiles at two areas of Owens Lake: near the Dirty Socks

Pond and the south area (site of the former Geomet

observatory) and at the north area. The value of this ratio

was approximately 0.03 cm1

. This value compares

favorably to Gillette et al.’s [1997b] value, considering

that heights at 10 cm would be expected to have larger

mass fluxes than at 15 cm. Consequently, this yields the ratio

q=m15 1⁄4 41:71⁄2cm
 1⁄4 0:4171⁄2m
: ð17Þ

In practice, we used the dimensionless quantity

K0 1⁄4 Fa=m15 ð18Þ

along with the K defined in equation (5). To convert K 0to the

K of equation (5), equation (18) was used to give

K 1⁄4 K0 2:41⁄2m1


or

K 1⁄4 K0 0:0241⁄2cm1
: ð19Þ

3.2. Measurement of Hourly Horizontal Mass Fluxes

at 15 cm Height

[32] One hour m15 values were measured at the centers of

a 1-km-spaced square grid work covering the potentially

active 135 km2 of the surface of Owens Lake shown in

Figure 2. Hourly mean m15 values were calculated by

combining information from the sand-sized particle flux

values from CSCs (collected over a 10– 30 day period) and

a collocated Sensit particle flux device. The Sensits and

CSCs experienced the same sand-sized particle fluxes at

15 cm height. The Sensit instrument recorded two continuous responses that have been shown to be nearly linearly

proportional to mass fluxes [Gillette et al., 1997b; Stout and

Zobeck, 1996] for carefully controlled conditions, although

different mineralogies and size distributions of the particles

could lead to different responses for the same mass flux. To

estimate hourly sand flux rates, the total mass measurement

from the CSC for a sampling period was time resolved

using the hourly Sensit readings, which were either based

on particle count or kinetic energy readings. For each

measured point on the lake surface for the collection period,

hourly sand flux was estimated from

m15 1⁄4 S; ð20Þ

where S is hourly Sensit response (less its background

value) and  is the ratio of the CSC total mass collection to

the total Sensit response (particle count (PC) or kinetic

energy (KE)) for a given collection period. An example of

the response of a typical Sensit to the collected CSC mass is

shown in Figure 5. Similar plots were generated for each

Sensit to check for large deviations in , which may

indicate that a Sensit was operating improperly.

[33] The two Sensit continuous responses are called by

the manufacturer ‘‘KE’’ and ‘‘PC’’ outputs. The Sensit

response for the KE output includes a background which

must be subtracted from the KE signal before the integrated

Sensit response is converted to m15, as in equation (20). The

other output of the Sensit, PC, was also available. This

output did not have a background value; however, PC

outputs were found to be more limited in range than KE.

[34] A 3 month test of the sand flux network at the start of

our program revealed problems with the Sensits that were

later resolved to ensure the data quality. Although early tests

Figure 5. Accumulated airborne mass catch (R R m15dAdt) at 15 cm height (kg) and Sensit kinetic

energy instrumental response integrated over the same sampling period and the same height for side-byside locations on Owens Lake.

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showed that the Sensits’ particle count or kinetic energy

outputs were generally proportional to sand flux as measured

by the CSCs, this was not true for all Sensits, and care had to

be taken in collecting and interpreting the data. The initial

testing also revealed a small number of Sensits with unpredictable outputs, which were revealed by large deviations in

 when sand catch values were high. These Sensits were

quickly identified and removed from the network. Owing to

changes in the Sensit construction and design during the

period that Sensits were obtained for this study, other

anomolies were discovered. The first was the manufacturer’s

incorporation of different background KE outputs for different batches of Sensits. These individual KE background

values were recorded when there was no erosion activity,

which was indicated by zero particle counts. The background KE was simply subtracted from the Sensit KE data

to correct the data. Some Sensits were found to be too

sensitive for high-erosion areas, and the signal would be

saturated. These were discovered by analyzing the Sensit to

sand catch ratios, , for each sampling period. These Sensits

were moved to less active sites. A less apparent problem was

discovered during testing of the Sensits with a mechanical

tapping device that showed slightly different responses to

tapping on different sides of the piezoelectric sensor ring.

This may indicate that the piezoelectric ring is not completely

uniform. A look at the Sensit data for some sites did show

small differences in the Sensit and sand catch relationships

for northerly versus southerly dust events. However, because

the Sensits are maintained in the same directional orientation

and the K factors are only determined when the wind

direction is in a line from the Sensit to the PM10 monitor,

this is unlikely to cause errors in the K factor calculations. In

addition, high winds at Owens Lake were observed to be

primarily northerly or southerly since they are strongly

influenced by the Owens Valley’s steep mountain ranges.

3.3. Shoreline PM10 Monitoring Data

[35] PM10 concentration measurements were taken at six

fixed stations at the shoreline locations shown in Figure 2.

No stations were placed on the western shoreline that runs

north and south because of the rarity of winds strong

enough to cause dust emissions with easterly components.

Ruprecht and Patashnik Tapered-Element Oscillating

Microbalance (TEOM) instruments were placed at the six

locations along with meteorological stations. TEOM instruments are used to give hourly PM10 concentrations and are

operated under standard U.S. EPA protocols. Three monitoring stations are sited in small towns: Keeler, Lone Pine,

and Olancha. The other three sites are at locations where the

public has access and are frequently impacted by dust

plumes: Shell Cut, Flat Rock, and Dirty Socks.

3.4. Surface and Upper-Air Meteorological Data

[36] Figure 2 shows the locations for the 10 m towers.

This network of surface stations collects hourly wind data at

every location. Surface pressure, temperature, relative

humidity, and precipitation are also collected at several of

the stations. These data, and, when available, wind data

from other field programs, are used as the basis for the

construction of a surface wind field. In order to characterize

the boundary layer on the lake, there is a 915 MHz radar

wind profiler with a radio acoustic sounding system (RASS)

being operated at the Dirty Socks station shown in Figure 2.

The wind profiler with RASS collects hourly wind and

temperature data aloft to investigate vertical wind shear

and provide data on the boundary layer structure. The

surface and upper-air meteorological data are used with the

procedures described in section 3.6 to characterize threedimensional transport on the lake and provide a link between

the source areas and shoreline PM10 monitoring program.

3.5. Off-Lake Observations of Source Areas

[37] The locations of PM10 source areas on the lake bed

are verified by using a total of 13 video cameras at six offlake view points. Three of the observation points are

manned. Hourly mapping of source areas is done during

storms at elevated locations in the Sierra Mountains (west of

the lake), the Coso Mountains (south of the lake), and the

Inyo Mountains (east of the lake). Source area mapping is

also augmented after storms by circumscribing erosion areas

with GPSs.

3.6. Calpuff Modeling Method Used for Estimation

of K Factors

[38] K factors were derived empirically as K 0

by comparing air quality model predictions using the Calpuff modeling system to observed concentrations at six PM10 monitor

sites [Scire et al., 2000a]. The three-dimensional wind field

for Calpuff was constructed from surface and upper-air

observations using the Calmet meteorological preprocessor

program. Calmet [Scire et al., 2000b] combines surface

observations, upper-air observations, terrain elevations, and

land use data into the format required by the dispersion

modeling component, Calpuff. Winds are adjusted objectively, using combinations of both surface and upper-air

observations according to options specified by the user. 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.

[39] Calpuff is commonly applied to near-field dispersion

where the three-dimensional qualities of the wind field are

important. Observations at Owens Lake have frequently

shown three-dimensional qualities. The model domain

includes a 34 48 km area centered on Owens Lake.

The model used a 1 km horizontal mesh, with 10 vertical

levels extending from the surface to 4 km aloft. The extent

of the model domain was selected to match the 1 km mesh

size of the Owens Lake sand-sized particle flux network. A

lognormal particle mass versus size distribution was used

from Owens lake dust size distribution data [Niemeyer et al.,

1999]. The distribution was used to give nine particle size

classes, to which deposition velocities were assigned using

Slinn and Slinn [1980].

[40] K factors were estimated for four areas of the lake bed

using the following three steps: (1) generate hourly K 0

values,

(2) screen out K 0 values that had weak source-receptor

relationships, and (3) group K 0 into different temporal

periods for each area of the lake bed. K factors were transformed from the dimensionless K 0

values by equation (19).

3.6.1. Step 1: Generate Hourly K 0 Values

[41] Hourly K 0 values were generated using the Calpuff

modeling system [Scire et al., 2000a]. Initially, the emissions at point i, j were estimated to be Faij = K 0

initm15ij

. K 0

init

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was set arbitrarily to 5 105 to generate model concentrations that were close to the monitored concentrations. In a

postmodeling step, the model results with K 0

init were compared to monitored concentrations at each of the six PM10

monitor sites (TEOM sites). Hourly K 0 values that would

have yielded the same modeled concentrations as those that

were monitored were then calculated using equation (21):

K0 1⁄4 K0

init1⁄2 
 ð Þ Cobs  Cback =Cmod ; ð21Þ

where

K0

init = 5 105

;

Cobs observed hourly PM10 concentration (mg m3

);

Cback background PM10 concentration (assumed to be

20 mg m3

);

Cmod model-produced hourly PM10 concentration

(mg m3

).

3.6.2. Step 2: Screen K 0 Values

[42] K 0

was calculated for every hour that had active sand

flux in cells which were upwind of a PM10 monitor. This

excluded hours when the dust source areas were from

unmonitored source areas off the lake bed, such as nearby

sand dunes. These hourly K 0 values were then screened to

remove hours that did not have strong source-receptor

relationships between the active source area and the downwind PM10 monitor. For example, the screening criteria

excluded hours when the edge of a dust plume may have

impacted a PM10 monitor site. Because the edge of a dust

plume has a very high concentration gradient, a few degrees

error in the plume direction could greatly affect K 0

> as

calculated in equation (21). Four individual source areas

were chosen, as shown in Figure 2. The data were screened

using wind direction information such that at least 65% of

the dust concentration recorded by the hourly TEOM

measurement at the given location came from the target

source area. The screening criteria included the following:

(1) modeled and TEOM-measured PM10 must both be

greater than 150 mg m3

; (2) the wind speed at a 10 m

height must be greater than 5 m s1

; and (3) sensit-derived

1 hour m15 must be greater than 2 g cm2 h1 in at least one

cell that was located within 10 km and ±15 upwind from a

PM10 monitor site.

3.6.3. Step 3: Group Hourly K 0 Values Spatially

and Temporally

[43] About 1000 hours of screened data were used to

generate temporal and spatial K 0 values: (1) for each storm

(event) the average K 0 is calculated for the four source

areas; (2) for each of the four source areas the K 0 averages

are divided into seasonal sets; (3) K 0 values are calculated

for the 50th, 75th, and 90th percentiles for each season for

each of the four source areas; (4) model performance is

assessed by comparing the model predictions to the

monitored TEOM concentrations at each site. This was

done using each of the three K 0 percentiles in step 3 to

determine which set provided the best model performance.

Equation (19) was later used to convert K 0

to Fa/q (K factor).

4. Results

4.1. Case Study of a Single Dust Storm, 2–3 May 2001

[44] As an illustration of the Owens Lake dust emission

model, a storm that began at 0400 LT on 2 May 2001 and

ended at 1200 LT on 3 May 2001 is provided as an example

of the method. Figure 6a shows the accumulation of m15

mass flux for the duration of a specified time (Rm15dt = Q15

(g cm2 per storm)) for the storm duration for each km2 of

the area shown in Figure 2.

[45] Figure 6b shows the superimposition of hourly visual

maps of the dust sources (solid lines) and dust plumes

(dashed lines) for the 36 hours of the dust storm on the map

of Owens Lake. The observations were drawn from elevated

(mountain) positions west, east, and south of the lake.

These observations do not cover the entire 36 hour storm

period (for nighttime hours) so that areas may have measured sand flux (not dependent on visual observations)

when no visual observations were possible. The two dominant source areas, the north area and the south area (see

Figure 2), show plumes of dust being carried southeast and

south, respectively. This divergence of plume directions has

been observed previously and has been attributed to the

presence of the Coso Mountains, south of the Shell Cut

station. Plumes originating in the central source areas were

observed for only short distances headed south. Plumes

from the western part of the south area were not observed.

Plumes from the western part of the north area were

observed heading southeasterly across the stable crusted

area of Owens Lake. A comparison of Figures 6a and 6b

shows that the areas of most intense sand movement

coincide with the source areas for the dust plumes, even

though areas of less intense sand movement (such as the

western parts of the south area) did not coincide with

observed plumes. These sand movements possibly occurred

during hours when observations were not taken.

[46] Figure 7 shows the 1 hour mean values for wind

speed, wind direction, TEOM concentrations, and plume

observations at 1300 LT on 2 May 2001. TEOM PM10

concentrations increase from a value of 32 mg m3 upwind

of the lake at the Lone Pine station to 2452 mg m3 directly

downwind of the strong south area source. Wind speeds

range from 10 to 16 m s1

, and plume directions are

consistent with plume directions in the model. The Keeler

TEOM registered a much lower concentration than the Dirty

Socks TEOM, reflecting the fact that it was not in the direct

downwind plume of the north area. The Q15 values (horizontal sand-sized particle mass flux measured at a height of

14.5– 15.5 cm) for the hour 1300 LT on 2 May 2001 is also

shown in Figure 7. All of the source areas shown in Figure 2

are seen to be active during this hour. Sand-sized particle

fluxes as high as 118 g cm2 h1 are seen in the north area

and as high as 43 g cm2 h1 in the south area. The central

area had individual sand-sized particle flux measurements

as high as 7 g cm2 h1 near the Shell Cut location.

[47] Figure 8 shows cumulative vertical fluxes of PM10

(

R

Fadt) for the full duration of the 2– 3 May 2001 dust

storm for each cell of the map. K factors used for Figure 8

were produced by completing steps 1 and 2 in sections

3.6.1– 3.6.2. Of the four source areas shown in Figure 1,

the north area and the south area are seen to be strong

source areas on 2 –3 May. The central area was seen to be

only partially active: west of the Flat Rock station and north

and west of the Shell Cut station. The Keeler Dunes area

was a strong, although small, dust source area.

[48] The K 0 component of the PM10 emission, proportional to the K factor (see equation (19)), was calculated by

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the methods given in sections 3.6.1– 3.6.3. K factors were

calculated for each hour and for each source area by

completing steps 1 and 2 of sections 3.6.1 – 3.6.2. A plot

of hourly K factors is shown in Figure 9a for the south area,

the north area, and the central area. The differences for

source areas of the three TEOM locations are reflected in

the lower values of the north area compared to those from

the south and central areas during the early part of the

storm. Central area K factors are often larger than south area

K factors. A feature common to all three source areas is that

they possess larger K factor values at the start of the storm

compared to the later values. Saint-Amand et al. [1986]

Figure 6a. Accumulated sand-sized particle flux (Rm15dt) at 15 cm height for a dust storm at Owens

Lake, California, on 2 – 3 May 2001. Shading in each 1 km2 grid cell shows the range of accumulated

sand-sized particle flux at 15 cm height (g cm2

) for that cell for the entire storm.

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suggested that softer crustal material at Owens Lake reflects

differing mineralogical crystallization that is separated at a

15C boundary; harder crusts are formed at temperatures

greater than 15C (late spring and summer), while softer

crusts are formed at cooler than 15C (winter and early

spring). This might explain the initial rapid lowering of the

K factors as removal of more easily abraded material (softer

crust), exposing a harder crust below. After the decrease of

the K factors of the first day of the storm, the K factors of

the second day were rather consistent in time.

[49] Figure 9b compares observed versus predicted hourly

PM10 concentrations (mg m3

) at the Dirty Socks TEOM for

the 2– 3 May 2001 episode at Owens Lake using the average

K factors for the north, central, and south areas for the dust

Figure 6b. Dust plume boundaries for a dust storm at Owens Lake, California, on 2– 3 May 2001.

Observed hourly boundaries of dust plumes are superimposed on the map of Owens Lake.

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storm and the average K factor for the previous storm for the

Keeler Dunes. The storm average K factors for each source

area were used to calculate individual vertical fluxes of dust

using the m15 at every location for each hour of the storm.

These Fa values were used in the transport model to calculate

concentrations at the location of the Dirty Socks TEOM.

Although changes in the hourly concentration trends were

predicted, the comparisons show that the hourly concentration at Dirty Socks was underpredicted for concentrations

>10,000 mg m3 and <100 mg m3 for this event. Underestimation of strong emissions and overestimating weak emissions was expected by using the storm mean K factors.

4.2. Summaries of Q15 for 1 July 2000 –30 June 2001

[50] Figure 10 shows Q15 for each square kilometer of the

Owens Lake grid for 1 year: 1 July 2000 – 30 June 2001.

Figure 7. Map of Owens Lake, California, during a dust storm on 2 – 3 May 2001 at 1300 LT. The range

of 1 hour time-integrated sand-sized particle flux at 15 cm height (g cm2

) is shown in each 1 km2 grid

cell by shading. The boundaries of dust plumes are superimposed.

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Figure 8. The range of time-integrated PM10 particle flux (R

Fadt) (t km2) for a dust storm for each

1 km2 grid cell on 2 – 3 May 2001 at Owens Lake, California, versus location on Owens Lake, shown by

shading. The boundaries of four distinct dust emission source areas are shown.

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Since zero Q15 values represent areas that have not had sand

motion in a significant quantity, the pattern of nonzero Q15

is roughly equivalent to the pattern of dust-emitting surface

for this time period. The pattern of Q15 provides a rough

picture of the strongest dust source areas of the Owens

Lake bed.

4.3. Variation of the K Factor for Two Source Areas

During One Storm

4.3.1. Hourly K Factors Developed for the South Area

[51] Figure 11 shows hourly K factors (Fa/q estimated

using equation (19)) developed from using steps 1 and 2 of

sections 3.6.1 – 3.6.2. The K factors are plotted versus four

independent variables: day, PM10 mass concentration measured by the TEOM, wind speed, and maximum Sensit flux

during the 1 hour sampling period. The time period is

January 2000 – June 2002. Figure 11a, showing hourly

K factors versus day, suggests that the highest K factors

occur in the spring of the year, although there is also large

scatter for all the times. The observation of large K factors

in the spring is consistent with the discussion of the storm of

2 – 3 May 2001 in section 4.1. Figure 11c shows a considerable scatter of Fa/q versus wind speed. The K factors

show a slight reduction of scatter for higher wind speed

along with an absence of small K factors for the strongest

winds. Likewise, the maximum Sensit flux during the hour

of sampling shows reduction of scatter for the highest

maximum Sensit response. Finally, there seems to be a

small trend of the K factor with PM10 (TEOM) concentration. We feel that the high-PM10 concentrations occur only

during times of effective dust production (high Fa/q values)

so that Figure 11b shows that the dust concentration

correlates with efficiency of dust production.

4.3.2. Hourly K Factors Developed From the

North Area

[52] Figure 12 shows the hourly K factors developed for

the north area by using steps 1 and 2 of sections 3.6.1 –

3.6.2. Figures 12a –12d show the same general features as

Figures 13a – 13d did for the south area, although with fewer

data points. They also suggest a possible slight correlation

of K factor with wind speed and maximum Sensit flux.

Figure 12c possibly suggests a higher correlation than does

Figure 11c. The north and south areas both have sandy soil

textures, which may help explain the similarity in K factor

ranges and values in Figures 11 and 12.

4.4. Summary of the Temporal and Spatial Fa//q Values

for Owens Lake

[53] Table 1 summarizes the temporal and spatial Fa/q

values that were generated from the screened K 0 data. The

75th percentile storm average values are summarized

because they were found to provide the best model performance on high PM10 days (see the complete method for

calculating K factors in sections 3.6.1– 3.6.3). For other

purposes, such as for individual storms, day-specific storm

average values may be more accurate. All four areas showed

temporal trends in K factors. These differences might be

attributed to the different soil textures that were found in a

soil survey of the surface soil textures. Average K factor

values were more stable over different areas of Owens Lake

than were sand flux rates, which typically varied by three

orders of magnitude for sites in the same area. The claydominated central area had the highest average K factors but

was normally less than a factor of ten different from the

lowest K factor during any period. The Keeler Dunes, north

area, and south area, which all had sandy soil textures,

normally had less than a factor of three difference between

the areas.

4.5. Total PM10 Mass Fluxes for the Period 1 July

2000–30 June 2001

[54] To calculate cumulative vertical mass flux R

Fadt for

an extended period of time, we used K factors that were

equal to the 75th percentile of the hourly K factors for each

of the source areas; that is, for each source area and each

season we multiplied the 75th percentile K factor for

each season times hourly m15 fluxes to calculate PM10

vertical fluxes (Fa). Comparisons of observed concentrations with concentrations developed from using the seasonal

50th, 75th, and 90th percentile K factors showed that the

75th percentile K factors gave the best agreement between

calculated and observed concentrations.

[55] The estimated cumulative vertical mass flux for all

locations shown in Figure 2 at Owens Lake is shown in

Figure 9a. Hourly K factors (estimated Fa/q (m1)) for the

2 – 3 May 2001 dust storm developed for three source areas

on Owens Lake (south area, north area, and central area).

Figure 9b. Observed versus model-calculated hourly

PM10 concentrations (mg m3

) at the Dirty Socks TEOM

for the 2 – 3 May 2001 episode at Owens Lake.

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Figure 13 for the 1 year period from 1 July 2000 to 30 June

2001. This 1 year time period was chosen since 1 July

2000 was the first time the entire Owens Lake surface was

fully instrumented, while after 1 July 2001, dust controls

were placed on the lake bed, which significantly reduced

dust production. Figure 13 shows a pattern of PM10

integrated vertical fluxes for the 1 year period that shows

some similarities in patterns of the time integrated Q15s for

the 2 day storm of 2 – 3 May 2001 shown in Figure 6a. The

storm of 2 – 3 May 2001 was the largest dust-producing

event for the 1 year period. The similar patterns for PM10

integrated vertical flux of the largest single storm and that

Figure 10. The range of time-integrated sand-sized particle flux at 15 cm height (Rm15dt) (g cm2) for

all dust storms at Owens Lake, California, from 1 July 2000 to 30 June 2001 versus location on Owens

Lake, shown by shading. The boundaries of four distinct dust emission source areas are shown.

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Figure 11. Hourly K factors (Fa/q (m1

)) developed for the south area of Owens Lake for January

2000 –June 2002 versus (a) day, (b) hourly mean PM10 mass concentration obtained by TEOM, (c) wind

speed (m s1

), and (d) maximum Sensit flux (maximum m15). Data are restricted to the conditions given

in section 2.2.6.

Figure 12. Same as Figure 11, but for the north area.

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for the yearly accumulated total suggest that the south area

is usually the largest dust-producing part of the lake

surface. For the year of measurements, our examination

of individual storm events showed that the north area

occasionally is the largest dust contributor of the four

areas, that the Keeler Dunes are a small but constant

contributor of dust, and that the central area produces less

dust than the north area or south area. Figure 14 shows the

frequency distribution of daily total PM10 integrated vertical flux for the entire lake for the same year. The distribution shows that 50% of the dust storms emit <10 t in a

24 hour period. Dust storms were observed to be highly

sporadic through the year, although the largest total emissions usually take place in the months of March –May, and

the smallest total emissions take place in the months of

June –August. For the 1 year period the total PM10 emission from the surface of Owens Lake was 7.2 104 t. The

storm of 2 – 3 May 2001 accounted for 6.5 103 t, or 9%,

of the yearly emissions.

5. Discussion

5.1. Consistency of K Factors With Other Work

[56] Here, we compare Fa/q ratios as determined using

the K factor method with Fa/q ratios obtained from smallscale portable wind tunnel and micrometeorological methods. When comparing the source area average or lake-wide

average K factor with individual K values obtained for small

areas on the lake, it is important to remember that K factors

may be regarded as weighted means of individual Ks (see

equation (14)); that is, the K factor is a weighted sum of the

individual Kijs: Where there is relatively small qij, there is

not much influence by the local Kij on the K factor, and a

properly measured small-scale Kij might (but does not

necessarily) differ from a properly measured large-scale

average K factor. Therefore a requirement for comparison

of our mean K factors with the mean of wind tunnel Ks was

that the wind tunnel test area not lie in an area of belowaverage sand flux.

[57] K factors vary inversely as the binding energy of the

surface sediment according to equation (4). At Owens Lake,

binding energy (corresponding to surface cohesion crusting

or lack thereof ) depends on the soil type, salt content,

moisture content at the surface, and temperature conditions

during surface dehydration [Saint-Amand et al., 1986].

These conditions are often unpredictable and vary on both

large and small scale and in time, making the binding

energy a very elusive parameter.

[58] Alfaro and Gomes’s [2001] theoretical work on the

effect of binding energies and kinetic energies of saltating

sand particles shows that variability resulting from differing

sizes of sand grains, differing velocities of sand grains, and

binding energies can result in a small-scale variation of K

more than a factor of 10. Such variability is seen in the K

results for similar soils of Gillette et al. [1997a].

[59] The K factor method from this study averages over

a large dust-emitting area contributing to one dust plume

and over a 1 h time period. Small-scale K factors can also

be determined using wind tunnel methods on the lake bed

or micrometeorological methods. For example, we compare in section 5.1.2 the averages of many wind tunnel Ks

measured in one of the four source areas of Owens Lake

with the time-averaged K factors for the period of measurement. We also compare a small-scale K value obtained

in 1993 with K factors for the south area. Although this

comparison was less desirable than one having data

collected at the same time, it takes advantage of historical

data.

5.1.1. Measurements of Fa//q Ratios in the South Area,

March 1993

[60] Micrometeorological measurements of the vertical

fluxes of PM10 and sand-sized particle fluxes on the south

area of Owens Lake taken 11 March 1993 by Gillette et al.

[1997a] gave a ratio of PM10 flux to sand-sized particle

mass flux (Fa/q) of 2.8 104 m1

. The K factors for the

south area (representative of the area in which the Gillette et

al. [1997a] measurements were obtained) are roughly consistent in the springtimes of 2001 and 2002, shown in

Figure 11. This range of K factors is consistent with the

Fa/q ratios found by Gillette [1977] for sand-textured soils

and suggests that the binding energy and size of saltating

particles for the tested surface material at the south area of

Owens Lake is of the same order as that for sandier soils.

Vertical fluxes of PM10 aerosol for 11 March 1993, estimated by Sun photometry for a large section of Owens

Lake, were reported by Niemeyer et al. [1999]. These values

were used to calculate values of Fa/q by using q values

reported by Gillette et al. [1997a] for the same storm in the

south area of the lake bed. After removing one anomalous

value (2 102 m1

), explained by Niemeyer et al.

[1999] as being due to the pooling of dust by the Sierras, the

range of their values (1 104 m1

1 103 m1)

enclosed the Gillette et al. [1997a]Fa/q value.

5.1.2. Wind Tunnel K Factors Versus K Factors of

This Study

[61] Nickling and Brown [2002] used a portable wind

tunnel to measure small-scale Fa/q. In their measurements

the surface of Owens Lake was used as the floor of the wind

tunnel, which was carefully lifted onto the surface, avoiding

disturbance. Two kinds of Fa/q measurements were made:

Table 1. Temporal and Spatial 75th Percentile Storm Average Fa/q Values at Owens Lakea

Period

Start End Keeler Dunes North Area Central Area South Area

1 Jan. 2000 3 Feb. 2001 1.2 104 0.5 104 1.6 104 0.5 104

4 Feb. 2001 18 April 2001 1.2 104 0.5 104 6.2 104 1.6 104

19 April 2001 30 Nov. 2001 1.2 104 0.5 104 1.5 104 0.5 104

1 Dec. 2001 8 March 2002 4.7 104 4.9 104 8.6 104 1.0 104

9 March 2002 18 April 2002 1.4 104 1.3 104 1.9 104 2.1 104

19 April 2002 30 June 2002 1.4 104 1.3 104 1.6 104 0.4 104

a

Values are in m1.

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with or without clean sand being fed into the upwind part of

the tunnel. Since the total floor area of the wind tunnel was

12 m2

, the tunnel measurements represent very local Fa/q

values. In contrast, our K factors represent mean Fa/q values

for areas of about 3 107 m2

.

[62] Comparison of our K factors with the wind tunnel

Fa/q are given in Table 2 [Nickling and Brown, 2002]. Our

values and the wind tunnel values for the same lake areas

are within a factor of three. Considering the large differences in averaging area and the standard deviations of the

Figure 13. The range of accumulated PM10 particle flux (RFadt) (t km2) for all dust storms at Owens

Lake, California, from 1 July 2000 to 30 June 2001 versus location on Owens Lake, shown by shading.

The boundaries of four distinct dust emission source areas are shown.

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means, we consider the comparisons to be within the range

of natural variability of Fa/q found on the lake bed.

5.2. Implication of K Factor Data on the Dust

Emission Mechanism

[63] Our K factor data from Owens Lake may constrain

the nature of PM10 emissions. Figure 11 shows that for the

south area, our K factors seem to be approximately constant

versus maximum sand-sized particle flux for wind speeds

less than 17 m s1

. For these wind speeds we favor the

impact kinetic energy mechanism. However, the appearance

that K factors increase for wind speeds >17 m s1 may

indicate that the Lu and Shao [1999] excavation mechanism

might increase PM10 emissions at very high wind speeds.

5.3. Dispersion Model Uncertainty

[64] The Calpuff modeling system and meteorological

data collected at Owens Lake have been used to characterize

transport and examine the relationships between the shoreline PM10 observations and on-lake Sensit data in order to

infer PM10 emission rates. The variability found in the

derived emission fluxes and K factors are influenced by

both actual physical effects and the uncertainties associated

with the modeling system. Uncertainties in the modeling

system could be caused by the following.

[65] 1. The first cause could be the inaccurate specification of diffusion and transport caused by errors in model

formulation or the lack of sufficient horizontal and vertical

meteorological data. Diffusion estimates from the model

have been checked qualitatively by comparing plume depths

and widths mapped by off-lake observers with model

predictions. Observed plume widths appear to be well

described by the model, but in some instances, calculated

plume trajectories differ from those mapped by the off-lake

observers. For some of the high-wind events, plume depths

seem to be underpredicted by the model; the video cameras

suggest more turbulent vertical diffusion. For such events

the derived vertical PM10 emission fluxes may be underestimated in this analysis.

[66] 2. A second cause could be the insufficient resolution

of the Sensit array. The 1 km spacing of the Sensit array is

insufficient to capture some of the smaller source areas

during the less severe dust events. For the larger dust events

on the lake the source areas are much larger than 1 km2

, and

Table 2. Comparison of Our Mean K Factors With Small-Scale Mean K Factors Determined by a Wind Tunnel

Without Feed

Time Period Average K Factors

Start End Area

Our K Factor

(Large-Scale Fa/q), m1

Wind Tunnel

(Small-Scale Fa/q), m1

1 Jan. 2000 3 Feb. 2001 north 4.3 105 5.5 105

1 Jan. 2000 3 Feb. 2001 Keeler Dunes 8.4 105 3.1 105

4 Feb. 2001 18 April 2001 central 57.8 105 23.3 105

4 Feb. 2001 18 April 2001 south 14.2 105 15.8 105

19 April 2001 30 Nov. 2001 central 13.7 105 38.4 105

19 April 2001 30 Nov. 2001 south 4.8 105 7.4 105

Figure 14. Frequency of daily total mass of PM10 emissions from the entire Owens Lake surface

(

R

FadA) (t d1

) versus the daily emission range for the period 1 July 2000 – 30 June 2001. Data are

restricted to the conditions given in section 3.6.

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the horizontal spacing of the Sensits appear to be adequate

to characterize sand motion.

[67] 3. A third cause could be the insufficient coverage of

the Sensit array. A number of off-lake source areas have

been observed to affect the shoreline PM10 monitoring

network. With the exception of the Keeler Dunes northwest

of Keeler, sand fluxes in these sources areas are not

recorded by either CSCs or Sensits and are not included

in the model simulations. To the extent possible, wind

direction data have been used to filter out the contribution

of off-lake sources during the analysis. During periods of

highly variable winds or when circulation is more complex,

use of the hourly average wind direction at the PM10

monitoring site may not always be effective in removing

the influence of sources not simulated in the analysis.

[68] 4. It is possible that for low u* the wind stress varies

strongly with atmospheric stability regimes [see, e.g., Chu

et al., 1996]. However, for dust emission episodes, u* was

large enough to preclude large errors. Averaging times for

u* were 1 hour.

[69] For the most part, the uncertainties mentioned above

are unbiased and result in scatter in the derived K factors and

inferred emission fluxes. Large-scale spatial and seasonal

differences in the derived variables, when based on ensemble

statistical measures, are expected to be more robust.

5.4. Variability for Fa/q Observed at Low Wind Speeds

[70] Figures 11 and 12 show that the variability of Fa/q is

higher for low wind speeds and smaller for larger wind

speeds. In our case the large variability in Fa/q for low wind

speeds may be caused by the patchiness of threshold

velocities and minimum energies for fine particle production by sandblasting. Patchiness of threshold friction velocity causes large variability of q near the threshold velocity,

and variability of minimum energy for emission of PM10

particles causes large variability for sand kinetic energies

near the threshold. The variability of threshold velocity is

caused by differing sizes of sand particles, variable roughness, and crusting of the soil. The variability of minimum

energy for PM10 emission by sandblasting (i.e., particleparticle bonding) may be affected by variable crusting of the

soil. As wind speed increases, threshold velocity is

exceeded for a wide variety of particles, and roughness is

smoothed by sandblasting. Evidence of smoothing of the

Owens Lake surface during the storm of 11 March 1993

[Gillette et al., 1997a] was observed when rough, crusted

soil was sandblasted and developed into smooth, loose sand.

Consequently, for higher wind speeds the Fa/q ratio would

be expected to have less variablility with wind speed.

5.5. Application of the K Factor Method to Control

Dust Sources at Owens Lake

[71] A practical application of the work described above,

and the primary intent of this study, was to identify the dust

source areas that can cause or contribute to violations of the

NAAQS for PM10. By incorporating the dust emissions into

the Calpuff model as described in section 3.6, we calculated

the contribution from each square kilometer source area

on the lake bed to shoreline PM10 concentrations at over

400 locations (virtual receptors.) These source contributions

were estimated for every hour during the 30 month study

period. Because K 0 only changed seasonally (Table 1),

emission trends were primarily driven by hourly sand flux

rates, which varied by three orders of magnitude during dust

events. As a result of the model predictions, it was determined that 77 km2 of the lake bed will require dust control

measures to reduce emissions to the point that compliance

with the NAAQS can be expected. The dust control area

generally corresponds to the shaded cells in Figure 13, with

the exclusion of a few cells that had low dust emissions and

did not significantly impact shoreline areas.

[72] The air quality plan, based on this model prediction,

anticipates the application of shallow flooding, managed

vegetation, or gravel on the identified dust source areas.

These control measures are expected to reduce dust emissions by 99%. Implementation of control measures on the

identified source areas are anticipated to be completed by

31 December 2006, in accordance with the federal Clean

Air Act [GBUAPCD, 2003].

5.6. Application of the K Factor Method to Other

Large-Scale Dust Sources

[73] Following J. Prospero’s (personal communication,

2002) observation that large-scale topographic depressions

located in desert regions may be dust sources of global

importance, Owens Lake may provide a good case study

since it is a topographic depression of more than 100 km2 in

area where rainfall is <15 cm yr1

, and it is a prolific source

of dust. The measurement and modeling technique used at

Owens Lake to estimate detailed patterns of dust emissions

may help to improve large-scale modeling in other topographic depressions and important dust-producing areas.

Our technique was based on the present inability to model

the saltation flux on the surface of Owens Lake because of

changes in crusting, the addition of loose sand to the surface

by wind transport from upwind, and changes in the mineralogy hydration state caused by temperature changes, resulting in soft crusts during the winter months and hard crusts

during the summer months. Consequently, we chose to

measure saltation flux rather than predict it. On the other

hand, our results (Figures 11 and 12) show that K factors

change seasonally ( probably largely because of the above

mineralogical change) and with location on the surface

( probably because of sediment texture differences). However, K factors are more well behaved and more predictable

than are saltation fluxes. It would seem that for topographic

depressions, our technique of measurement of q and measurement and modeling of K factors would be applicable. In

applying this technique, care must be taken to design

sufficient measurements to detect changes in K factors by

season and location on the source area, as we found for

Owens Lake. These changes are caused by the presence in

the dust source sediments of hydrating minerals that change

state in the annual temperature range expected and the

presence of differing size distributions and compositions

of sediments in the source area.

[74] At a larger scale, one may find the measurement part

of our strategy to be quite expensive; that is, measurement

of q on a 1 km scale for a depression significantly larger

than Owens Lake would require more instrumentation. We

found that the scale of 1 km reproduced observed dust

plumes to the degree necessary for the purposes of predicting sites causing significant air pollution. However, it is

possible that one could also model large-scale depressions

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using a coarser sampling network and less time resolved

saltation flux sampling points for depression sediments that

are more homogeneous than those at Owens Lake.

[75] In the case where saltation fluxes can be modeled

accurately the extensive measurements of saltation flux

required in our method could be replaced by modeled

saltation fluxes. In the authors’ opinion, modeling saltation

flux is very difficult to do well. It depends strongly on the

ability to predict the threshold velocity, which may be highly

variable due to nonuniform surface conditions, such as

variations in the soils and seasonal effects on the surface

crust. However, saltation flux modeling for places with

uniformly eroding surfaces could be done reasonably well

for a single event since a constant threshold velocity could be

used. In the case where saltation flux modeling is used

instead of measurements, K factors could be determined by

modeling combined with measurements of dust concentrations around the depression perimeter. In cases where such

measurements are not possible, rough estimates may be

possible by using representative K factors. For example,

for a large-scale depression, values of K might resemble

those from Owens Lake or K measured in other areas if

general characteristics of the sediment are similar. For cases

where measurements of q and K are not available, wisely

chosen K values and well-modeled q values could be used to

estimate Fa.

[76] If global climate models project changes in the

frequency distribution and mean of winds, the frequency

of wind storms will likely change. A detailed analysis of the

change of dust storm strength and frequency would require

the wind speed frequency distribution and the distribution of

threshold velocities for wind erosion of the surface. The

amount of increase/decrease was modeled by Gillette [1999]

using a simplified probability distribution for wind speed

and threshold velocity. When threshold velocities remain

constant and velocities of surface winds increase, the

probabilities for wind erosion events increase. If threshold

velocities decrease and velocities of surface winds increase,

one would expect a much larger increase.

6. Conclusions

[78] Windblown dust from the Owens Lake bed often

causes violations of federal air quality standards for particulate matter (PM10) that are the highest levels measured in the

United States. A refined method of modeling atmospheric

dust concentrations due to wind erosion was developed for

Owens Lake that combines real-time saltation flux measurements with ambient dust-monitoring data. A combination

measurement and modeling method was shown to be a useful

estimator for the vertical flux of PM10 from a large winderoding area. The measured quantities were (1) hourly m15

(sand-sized particle mass flux at 15 cm height), from which q

mass fluxes were calculated using equation (17), and

(2) hourly PM10 concentrations on the shore of Owens Lake

at six different locations. The modeled quantity was the

K factor, a variable that expresses the ratio of the vertical

flux of PM10 (Fa) to the horizontal flux of sand-sized

particles (q). The product of the K factor and q is the vertical

flux of PM10 mass. The quantity q is, in turn, related to m15,

the horizontal flux of sediment flux at a height of 15 cm. This

combination method is quite useful since m15 was shown to

be a smooth function in space but highly sporadic in time. By

measuring m15 on a grid network with 1 km separation over

135 km2

, we were able to estimate hourly q rates for the entire

lake bed. The measured variability of m15 over time and

space would have been difficult to predict due to strong

dependence on soil crusting and changes in soil aggregation

that are not well modeled at this time. Temporal trends in K

factors appeared to correspond to seasonal changes in the

surface soil conditions. The K factors developed for Owens

Lake agreed favorably with K factors for which both horizontal mass flux and vertical PM10 flux were measured in the

southern part of Owens Lake in 1993. In addition, wind

tunnel measurements of K factors obtained at locations found

to be the most prolific producers of dust on the lake were also

found to be in reasonable agreement with the K factors

developed for the four subregions of the lake, for roughly

the same year and season.

[79] By multiplying measured sand flux rates with the

model-derived K factors, it was possible to estimate the

vertical flux of PM10 for Owens Lake. For the 1 year period

1 July 2000– 30 June 2001 the total PM10 emission from the

surface of Owens Lake was 7.2 104 t. The largest single

storm (on 2 – 3 May 2001) accounted for 9% of those

emissions. Dust emissions were found to vary with season.

The largest emissions took place in the months of March –

May, while the smallest emissions take place in the months

of June–August. As a result of this study, dust source areas

on 77 km2 of the lake bed were identified and were

determined through dispersion modeling to have caused or

contributed to federal air quality violations. Dust control

measures to bring the area into attainment with air quality

standards are expected to be implemented on these source

areas by 31 December 2006.

[80] Acknowledgments. The authors gratefully acknowledge the

guidance and encouragement of Ellen Hardebeck, Air Pollution Control

Officer of the Great Basin Unified Air Pollution Control District. Scott

Weaver, Michael Slates, and Jim Parker were extremely helpful in working

with the data. Bill Cox led the field work and designed the Cox Sand

Catcher. Nik Barbieri, Chad Stein, Guy Davis, and Dan Johnson accomplished the demands of keeping the experiment going on the bed of Owens

Lake. Mention of trade names or commercial products does not constitute

endorsement or recommendation for use. Funding for this study was

provided by the City of Los Angeles Department of Water & Power to

the Great Basin Unified Air Pollution Control District under a regulatory

mandate to control windblown dust at Owens Lake (California Health and

Safety Code x42316).

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D. Gillette, Fluid Modeling Facility, Applied Modeling Research Branch,

Atmospheric Modeling Research Division, Air Resources Laboratory,

National Oceanic and Atmospheric Administration, MD-81, Research

Triangle Park, NC 27711, USA. (gillette.dale@epa.gov)

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