Comparison of PI-SWERL with dust emission measurements from a

straight-line field wind tunnel

Mark Sweeney,1 Vic Etyemezian,2 Torin Macpherson,3 William Nickling,3 John Gillies,4

George Nikolich,2 and Eric McDonald5

Received 14 May 2007; revised 27 September 2007; accepted 14 November 2007; published 23 February 2008.

[1] The Portable In situ Wind ERosion Lab (PI-SWERL) was developed to measure dustemissions from soil surfaces. This small, portable unit can test the emissivity of soilsin areas that are difficult to access with a field wind tunnel, and can complete a largernumber of tests in less time. The PI-SWERL consists of a cylindrical enclosure containingan annular flat blade that rotates at different speeds, which generates shear stress uponthe surface. The shear stress generated by PI-SWERL results in the entrainment ofparticles including dust. PI-SWERL was developed to provide an index of dust emissionpotential comparable to the field wind tunnel. The PI-SWERL dust emission results werecompared against those obtained from a �12 m long, 1 m wide, 0.75 m high straightline suction-type portable field wind tunnel by conducting collocated tests at 32 distinctfield settings and soil conditions in the Mojave Desert of southern California. Clay- tosand-rich soils that displayed a range of crusting, gravel cover, and disturbance weretested. The correspondence between dust emissions (mg m�2 s�1) for the two instrumentsis nearly 1:1 on most surfaces. Deviation between the two instruments was noted fordensely packed gravel surfaces. For rough surfaces a correction can be applied to thePI-SWERL that results in comparable dust emission data to the wind tunnel. PI-SWERLcan be used to complement research efforts in aeolian geomorphology aimed to quantifyspatial and temporal patterns of dust emissions as well as air quality research related todust emissions.

Citation: Sweeney, M., V. Etyemezian, T. Macpherson, W. Nickling, J. Gillies, G. Nikolich, and E. McDonald (2008),

Comparison of PI-SWERL with dust emission measurements from a straight-line field wind tunnel, J. Geophys. Res., 113, F01012,

doi:10.1029/2007JF000830.

1. Introduction

[2] The utilization of field wind tunnels has been criticalin the study of aeolian processes and for measuring thepotential of soils to emit dust [Gillette et al., 1982; Nicklingand Gillies, 1989; Houser and Nickling, 2001]. Airbornedust has important implications for air quality, humanhealth, visibility, and climate change [Pewe, 1981; Pye,1987; Harrison et al., 2001]. From an air quality andregulatory perspective an important component of wind-blown dust is PM10 (particulate matter less than 10 mmaerodynamic diameter), which is regulated by a nationalambient air quality standard [US EPA, 1995].

[3] Large, portable field wind tunnels are currently con-sidered the standard for measuring dust emissions in fieldsettings. Large wind tunnels attempt to simulate the atmo-spheric boundary layer and are used to model aeolianprocesses. However, measurements with large field windtunnels are sometimes difficult due to limitations imposedby physical access to sites, the requirement of relatively flattopography, and the availability and cost of labor required tooperate the wind tunnel. The PI-SWERL (Portable In situWind Erosion Lab) [Etyemezian et al., 2007, United StatesPatent No: US 7,155,966 B1] (Figures 1a–1c) is a noveldevice for measuring aeolian dust emissions that is smalland requires limited setup time compared to large field windtunnels. This allows for comparatively rapid and econom-ical measurements of windblown dust emissions at sites thatmay not be accessible to wind tunnels. A field comparisonof the large field wind tunnel and PI-SWERL is necessaryso that a meaningful relationship can be established betweenPI-SWERL and wind tunnel dust emission data.[4] In this paper, we describe a series of collocated tests

comparing the PI-SWERL to the University of Guelphportable straight-line field wind tunnel (Figure 1d). Thetests were designed to simulate multiple wind conditions at32 distinct field settings and soil conditions. The results of

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113, F01012, doi:10.1029/2007JF000830, 2008ClickHere

for

FullArticle

1Earth Sciences, University of South Dakota, Vermillion, South Dakota,USA.

2Division of Atmospheric Sciences, Desert Research Institute, LasVegas, Nevada, USA.

3Department of Geography, University of Guelph, Guelph, Ontario,Canada.

4Division of Atmospheric Sciences, Desert Research Institute, Reno,Nevada, USA.

5Division of Earth and Ecosystem Sciences, Desert Research Institute,Reno, Nevada, USA.

Copyright 2008 by the American Geophysical Union.0148-0227/08/2007JF000830$09.00

F01012 1 of 12

the collocated tests and advantages and limitations of the PI-SWERL are also discussed.

2. Background

[5] Aeolian transport of particles occurs when a thresholdwind speed is reached that imparts a critical amount of shearstress on the surface [Bagnold, 1941]. This critical shearstress, proportional to the square of the threshold frictionvelocity (u*,th), causes particles to be directly entrained bythe wind [Garland, 1983]. Silt and clay particles (dust),however, are most commonly entrained by the impact ofsaltating sand grains [Bagnold, 1941; Shao et al., 1993;Rice et al., 1997; Rice et al., 1996]. Important factors thataffect the threshold friction velocity for a particular surfaceinclude: soil moisture, surface roughness, size distributionof soil aggregates, and the existence of a surface crust.Many of these effects have been studied using field windtunnels.[6] Soil moisture can increase the threshold friction

velocity of a soil by increasing cohesiveness of sedimentaryparticles [Chepil, 1956; McKenna Neuman and Nickling,

1989; Saleh and Fryrear, 1995; McKenna Neuman andMaljaars, 1998]. Moisture content of 4% by weight isusually sufficient to prohibit particle entrainment [Bisaland Hsieh, 1966]. Soil moisture also aids in crust develop-ment and aggregate formation, as well as creating condi-tions more conducive to vegetation growth that acts toprotect the surface against wind erosion.[7] The soil aerodynamic roughness height (zo) and the

physical roughness of the surface, influence the thresholdabove which saltation and sandblasting, and therefore dustgeneration, can occur. On smooth surfaces, zo values greaterthan 0.001 m dramatically decrease the ability of the windto generate dust [Gillette, 1999]. Low densities of roughnesselements, such as pebbles on a sand surface, actually lowerthe threshold velocity by promoting local flow accelerationand scouring [Logie, 1982]. Higher densities of roughnesselements raise the threshold velocity due to increasedsurface protection and absorption of momentum from thewind [Logie, 1982; Nickling and McKenna Neuman, 1995;Gillies et al., 2000; Crawley and Nickling, 2003].[8] The dry size-distribution of soil aggregates present in

the loose, wind-erodable fraction of the soil also affects dustemissions [Chatenet et al., 1996; Alfaro et al., 2004]. Alfaroet al. [2004] demonstrated, using data from wind tunneltests carried out by Nickling and Gillies [1989], that soilswith a fine soil aggregate population had a high potential fordust emissions. The soils from those tests were character-ized by a range of silt and clay contents [Nickling andGillies, 1989] and showed that, contrary to what is oftenassumed, texture is not a directly relevant parameter forpredicting the potential for dust emission. Nonetheless,texture may play an indirect role because crusts that limitsaltation, and hence dust production, are more liable to formon fine textured soils than on coarse textured ones.[9] The presence of soil crusts substantially affects dust

emissions. The strength of bonding between soil particles oraggregates can influence the amount of dust generated.Even a weak crust has been shown to reduce the rate ofdust emission significantly [Gillette et al., 1982].[10] Aeolian transport has been studied with field wind

tunnels for almost a century with early efforts focusing onsoil drift in agricultural fields [Chepil and Milne, 1939].More recently, Gillette and colleagues conducted windtunnel tests on desert surfaces and agricultural fields toexamine the effects of soil texture, surface roughness, andcrust strength on the threshold friction velocity [Gillette etal., 1980, 1982; Gillette, 1988; Belnap and Gillette, 1998].Nickling and Gillies [1989, 1991] estimated dust emissionsfrom desert surfaces that had variable texture, vegetativecover, and degrees of surface disturbance. They concludedthat periodically disturbed surfaces had the greatest poten-tial for dust emissions. James et al. [2001] corroboratedthose results with measurements using a small field windtunnel in and near Las Vegas, Nevada. A large portablewind tunnel was used to provide estimates of windblownagricultural dust contributions to ambient particulate matter(PM) in Washington State [Stetler et al., 1994; Stetler andSaxton, 1995; Saxton et al., 2000]. Houser and Nickling[2001], using a wind tunnel developed at the University ofGuelph in Ontario, Canada, were able to demonstrate thatdust emissions from clay crusted surfaces decrease dramat-ically when the supply of saltating sand grains is exhausted.

Figure 1. (a) Schematic of the PI-SWERL. The rate ofrotation of the annular ring is controlled by a DC motor,which is in turn controlled by a computer. The dustsuspended by the shearing force beneath the annular ring ismeasured by a PM-10 monitor and reported to the computer.(b) PI-SWERL in the field. (c) Underside view of PI-SWERL showing annular ring. (d) University of Guelphfield wind tunnel.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

2 of 12

F01012

[11] Field wind tunnels that have been built and used forcharacterizing threshold friction velocities and dust emis-sions vary widely in design but generally fall into two sizecategories: (1) large tunnels with duct cross section areas onthe order of 1 � 1 m and working lengths from 4 m to 11 m[e.g., Houser and Nickling, 2001], and (2) small tunnelswith typical cross sections ranging from 0.15 m � 0.15 m to0.3 m � 0.3 m and lengths between 3 m to 3.5 m [e.g.,Gillette, 1978; Butterfield, 1991]. The larger wind tunnelsare generally preferred for aeolian studies since they over-come constraints on the boundary layer and saltationprocess that are imposed by the physical dimensions ofsmaller tunnels [Owen and Gillette, 1985; White andMounla, 1991]. However, owing to their complexity, rela-tively few large wind tunnels have been designed for fieldpurposes [Nickling and Gillies, 1991; Pietersma et al.,1996; Raupach and Leys, 1990; Houser and Nickling,2001; Maurer et al., 2006].[12] The design of the PI-SWERL (Figures 1a–1c) is

based on the observation that wind erosion and dustemission are strongly related to the friction velocity, u*,which provides a measure of the amount of shear stressapplied to a surface by the flow of air over that surface [e.g.,Chepil, 1958; Shinn et al., 1976]. Unlike a traditional windtunnel, the PI-SWERL is not designed to emulate theatmospheric boundary layer. Rather, it provides a measureof the stability of a soil by directly generating wind shearabove the surface created by Couette-like flow [Schlichtingand Gersten, 2000]. The PI-SWERL (Figure 1b) has beendesigned to be mounted on an all-terrain vehicle (ATV) toimprove portability over traditional field wind tunnels (bothsmall and large varieties). Though not used in the studiesreported here, a miniature version of the PI-SWERL hasalso been developed and can be carried and operated by oneperson.

3. Methods

[13] We compared the PI-SWERL to the University ofGuelph straight-line field wind tunnel by running collocatedtests at 23 different sites at the Ft. Irwin National TrainingCenter, within the Mojave National Preserve, and at theSalton Sea in California between April 2005 and September2005 on soils with a range of textures and surface character-istics. At nine sites, the soil surface was intentionallyaltered by artificial disturbance so that the data presentedhere represent comparisons over 32 distinct soil surfaceconditions.

3.1. University of Guelph Straight-Line Wind Tunnel

[14] The University of Guelph’s Wind Erosion ResearchLaboratory suction-type portable field wind tunnel is con-structed of 7 collapsible aluminum and fiberglass sectionsthat form a 0.75 m high with a 1.0 m � 11.9 m open-flooredworking section (Figure 1d). Six adjustable wheels aremounted on the outside of the tunnel, allowing it to beraised, moved and transported. In operation, canvas flaps oneach side of the wind tunnel are covered with soil or sand-filled tubing to help form a seal with the surface duringtesting.[15] Wind speed in the tunnel is measured with a pitot

tube that is mounted on a precise vertical traverse powered

by a computer controlled stepper motor. During a typicaltest, the pitot tube starts at a height of 0.005–0.01 m andautomatically traverses back and forth to a height of 0.10–0.15 m stopping at predetermined setpoints along the way.The vertical profiles of wind speed, collected electronicallythrough a pressure transducer, are required to obtain aboundary layer profile and the friction velocity u*. Frictionvelocity, which is proportional to the square root of shearstress, is calculated from the best fit of the measured windprofiles to the Prandtl equation [Prandtl, 1935]:

uz

u*¼ 1

kln

z

zoð1Þ

where uz is the wind speed (m s�1) at height z (m), k is vonKarman’s constant (0.4), and zo is the aerodynamicroughness height (m).[16] Concentrations of PM10 inside the wind tunnel are

measured at 1 s intervals using DustTrak (DT; TSI, Model8520) aerosol monitors, which use a laser diode and opticalbackscatter principle to infer PM10 concentrations and arecalibrated at the factory to Arizona Road Dust standard(ISO 12103-1). Four DTs are positioned on top of the wind-tunnel and connected by conductive tubing to stainless steelsampling nozzles that are aligned into the flow and posi-tioned 0.05, 0.10, 0.17 and 0.25 m above the test surface.The sediment transport rate of mostly sand sized particles,or saltation flux, is measured using the Guelph-TrentSediment Trap [Nickling and McKenna Neuman, 1997].The wedge-shaped trap has a 0.02 m � 0.3 m rectangularopening that is oriented into the flow with the long sidestretching upwards from and perpendicular to the ground.The back of the trap is made of stainless steel mesh with62.5 mm openings that allow air to flow through the trapwhile retaining sand. Sediment collected in the trap isweighed at the end of each test. On the basis of thesensitivity of the balance used (±0.1 g) sediment transportrates are accurate to within ±25 mg m�1 s�1.[17] Two types of wind tunnel tests were conducted at

most sites. Those designated Type 1 involved a rapidlyincreased fan speed that was then held at a constant RPM.Type 2 designated tests were ‘‘stepped’’ where the fan speedwas increased sequentially in a series of four or six steps,each with a duration of about 120 s (See Figures 2a, 2b).Type 2 tests were intended to mimic the stepped operationof the PI-SWERL (See below). For a Type 2 test, frictionvelocity in the wind tunnel was calculated by measuringwind speed at a single height and using the aerodynamicroughness height z0 obtained from Type 1 tests at differentlocations within the same site to solve equation (1).

3.2. PI-SWERL

[18] The PI-SWERL [Etyemezian et al., 2007] consists ofa computer controlled 24-volt DC motor attached to the topof an open-bottomed cylindrical chamber 0.25 m high and0.57 m in diameter. The motor, powered by a 24 volt powersupply, is coupled to a 0.51 m diameter flat annular ringwith a width of 0.06 m, which hangs parallel to and 0.06 mto 0.05 m above the soil surface within the chamber. Whenthe annular ring revolves about its center axis, a velocitygradient forms between the flat-bottom of the ring and theground creating a shear stress on the surface. The magnitude

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

3 of 12

F01012

of the shear stress increases with the rate of rotation, whichis reported to the computer in terms of revolutions perminute (RPM). A blower with a filtered inlet vents clean airinto the PI-SWERL chamber at a rate that is controlled bythe computer. The blower airflow rate (set near 4 L s�1),monitored by a mass flowmeter (TSI, Model 4235-01-01),is used in estimating dust emissions. As with the Guelphwind tunnel, the concentration of PM10 in the PI-SWERLchamber is monitored with a DT, which is attached to thetop of the chamber using conductive tubing. The tube ispositioned at the top of the chamber in order to measure thePM10 concentration of suspended particles.[19] During testing, the PI-SWERL is placed on the

ground and a canvas skirt attached to its base is weighteddown with lead shot to form a seal with the test surface. ThePI-SWERL measurement cycle is controlled by a laptopcomputer based on a set of user-defined steps, whichprescribe a target value of RPM for a set duration. The firststep is always a clean air flush where the chamber is ventedwhile the annular blade is not rotating (�90 s). Similarly,during the last step, the chamber is flushed when power to

the motor is cut off (�60 s). The specific RPM targets forintermediate steps (usually six) are determined by theoperator based on experience. Target steps are chosen sothat the PM10 concentration at the highest step does notexceed 100 mg m�3 (nominal upper limit for DT) for longperiods. The duration of the intermediate steps is generallyaround 120 s each (Figure 2c).[20] Etyemezian et al. [2007] have characterized the wall

shear stress induced by the PI-SWERL over a smoothhorizontal surface (plywood). They note that shear stressincreases with RPM, is elevated near the inside circumfer-ence of the PI-SWERL immediately underneath the annularring over an area of 0.07 m2, and decays sharply withdistance from the cylinder wall. For the plywood testsurface, Etyemezian et al. [2007] fit the peak frictionvelocity u* (m s�1) as a function of the RPM with theempirical equation:

u*;peak ¼ kR � ð�4:7 � 10�12 � RPM3 � 5:5 � 10�8

� RPM2 þ 5:3 � 10�4 � RPM þ 0:0165Þ ð2Þ

where kR is a correction factor for surface roughness that isequal to unity for nominally smooth surfaces (See below).[21] Radial profiles of the normalized friction velocity for

two values of RPM (1200 and 2400) are shown in Figure 3.

Figure 2. Raw time versus RPM and PM-10 concentrationfor select sites. (a) Normal wind tunnel test where RPM ofthe fan is increased to a predetermined RPM and held forthe duration of the test. Note the peaked nature of the dustflux. (b) Wind tunnel ramp test, where the speed of the fanwas sequentially increased during testing. (c) Typical PI-SWERL test showing stepwise increases in RPM duringtesting. RPM level is also plotted.

Figure 3. Friction velocity and hypothetical dust emissionradial profiles at the soil tests surface for PI-SWERL bladespinning at 1200 RPM (squares) and 2400 RPM (circles).Open symbols represent shear stressed measured with Irwinsensors [Irwin, 1980] normalized by the peak shear stresscalculated in equation (2) (u*/u*,peak). Solid symbols arehypothetical normalized estimates of dust emissionsassuming that emissions are proportional to the cube offriction velocity (u*

3/u*,peak3 ). Dashed lines are hypothetical

emissions assuming a threshold friction velocity of 0.4 m/sapplies. Vertical lines represent portion underneath theannular blade used to calculate u*,peak. Adapted fromEtyemezian et al. [2007].

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

4 of 12

F01012

Etyemezian et al. [2007] used the average of the data pointsbetween the two vertical lines to obtain an estimate of thepeak friction velocity, which was then used to obtain the fitin equation (2). The vertical lines also represent the effectivearea, Aeff, of soil surface that may exhibit sediment transportunder the applied shear stress. The exact locations of thevertical lines were chosen somewhat arbitrarily, but wasinformed by substantial previous work that related thevertical dust flux to the cube or fourth power of frictionvelocity [Gillette, 1977; Gillette and Passi, 1988; Nicklingand Gillies, 1989; Shao et al., 1993]. Along with thenormalized friction velocity (u*/u*,peak), Figure 3 showsthe radial profile of the cube of those values, which fallsoff rapidly to either side of the pair of vertical lines. Thus inchoosing the location of the vertical lines–which deter-mines both the effective area, Aeff, and the effective frictionvelocity ascribed to a specific RPM level–the portion of thecurve in Figure 3 that was highest and most flat wasselected. Also shown in Figure 3 is the effect of imposinga 0.4 m s�1 threshold friction velocity, below which nosediment transport would occur [e.g., Gillette, 1988].[22] Figure 3 lends some insight into the motivation for

the PI-SWERL design. The most important design criterionwas that the PI-SWERL should be substantially moreportable and easier to operate than large or even small fieldwind tunnels. The difficulty of conditioning straight-lineflow over the reduced distance scales required by a moreportable device dictated the circular design, where time-averaged gradients in the axial direction would be zero atthe test surface. The necessity for an annular blade design,as opposed to full circular blade, can be seen in Figure 3. Afull circular blade would result in continuous gradients ofshear stress from the edge of the blade to the center of thePI-SWERL chamber. The annular design allows for a moresudden drop in shear stress between the inside edge of theblade and the center of the PI-SWERL chamber. This isdesirable, because in the case of a continuous, shallowradial change in shear stress, the dust emissions from thesoil test surface at the outside edge of the blade would beoccurring under a different friction velocity than those at thecenter. This would make it difficult to discern what themagnitudes of dust emissions are at specific values offriction velocity. The annular blade design significantlyreduces the effective area over which dust emissions occurand consequently tightens the range of friction velocitiesthat are causing the dust emissions. Overall, this results in amore direct relationship between induced shear stress andmeasured dust emissions. Note that if the inner diameter ofthe annulus is made too close to the outer diameter, then theamount of shear that can be transferred from the annularsurface to the soil test surface becomes too small. Notingthat these considerations all contributed to the design of thePI-SWERL chamber as well as the blade, in the end theexact dimensions are somewhat arbitrary and representdesign compromises.[23] One dimension that does not have a large effect on

how the PI-SWERL operates is its height. As long as the topof the cylinder is sufficiently far away from the rotatingannulus (approximately 1=2 the radius), the top portion of thePI-SWERL simply acts as a mixing chamber, ensuring thatthe dust is well mixed prior to leaving the chamber throughthe exhaust port. One implication of this observation is that

the location of the inlet for the dust monitor at the top of thePI-SWERL chamber is not particularly important.

3.3. Site Selection and Description

[24] Twenty-three sites were chosen that reflect a spec-trum of sand-, silt-, clay-rich, and texturally mixed soilsfound on playas and lacustrine sediments, beach ridges,alluvial fans, washes, and aeolian sands (See Table 1 andFigure 4). Surface properties were also diverse and includedsalt crusts, silt-clay crusts, gravel cover, unconsolidatedsediment, and artificially disturbed surfaces. To accommo-date the physical constraints of the wind tunnel, sites had tobe nearly free of vegetation and relatively flat. At nine sites,measurements were conducted before and after artificialdisturbance resulting in two different sets of measurementsat those sites. Artificial disturbance was generated bydragging large perforated metal plates across the surfacethree times using an ATV. At each site (and disturbancelevel, if applicable), wind tunnel tests were conducted at sixor more distinct locations. Several PI-SWERL tests wereconducted alongside the wind tunnel at each locationresulting in up to 16 PI-SWERL tests per site.[25] Test surfaces included: (1) gravel-covered surfaces

(F1, F2, L1, L6, L7, N2, A100-1) located on alluvialfan, beach ridge, or wash settings with 75% or less gravel;(2) mud-cracked surfaces (L5, N1, N3, N4, S1, S3, A101-1,A31-1) found on playa or lacustrine sediments, exhibitinghigh clay content and generally stabilized by silt-claycrusts which in some cases were thin, loose, or broken;(3) continuously crusted surfaces without cracks (Z3, Z4,A100-2, A34-1, A34-2, SS16, SS2) also found on playa andlacustrine sediments with a notable salt component andeither smooth or rough, botryoidal surface crusts. Soils weretypically moist beneath the continuous crusts; or (4) artifi-cially disturbed, unconsolidated surfaces that were loose andpowdery sometimes containing granule to pebble-size aggre-gates of soil or rock. One gravel-covered site (L7) wascovered with granule and coarse sand wind ripples.[26] At each site, several samples of the top 10 mm of the

soil were collected. Soil samples were weighed before andafter oven drying at 105�C for 24 hrs to obtain the initialwater content. The gravel fraction (>2 mm) was removed bydry sieving and weighed. Particle size distributions weredetermined on the fine Earth fraction (<2 mm) using acombination of standard dry sieves and laser diffractiontechniques [Gee and Orr, 2002]. The dry sieve method wasused to determine the coarse fraction of sand in the samples.A laser particle-size analysis (LPSA) procedure using theMicromeretics Saturn DigiSizer 5200

1

was used to deter-mine the volume percentage and size distribution of medi-um sand through clay-sized particles.

4. Results

[27] Raw time series data from the PI-SWERL and windtunnel show two ‘‘stages’’ of emissions. At the beginning ofa wind tunnel test or a new target RPM value for PI-SWERL, dust concentrations exhibit a peak followed by agenerally rapid decline, which transitions into a gradualreduction of dust concentration over time (Figure 2). Insome cases, this latter ‘‘residual’’ portion of the curvequickly approaches zero, suggesting a limited supply of

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

5 of 12

F01012

wind-erodible sediment. In other cases, the residual remainselevated for the duration of the wind tunnel test or PI-SWERL RPM step, indicating that over those timescales,the supply and availability of wind-erodible material doesnot impose a limit on dust emissions.[28] The instantaneous PM10 dust emission rate (Ewt, mg

m�2 s�1) measured with the wind tunnel is quantified as[Shao et al., 1993; Houser and Nickling, 2001]:

Ewt ¼1

L

Zz

0

Cuzdz ð3Þ

where L is the length of the wind tunnel, C is the averagePM10 dust concentration (mg m�3), and uz is the wind speed(m s�1) at height z. In practice, equation (3) is used indiscrete form since dust concentrations are measured atspecific heights within the boundary layer. In order tocompare emissions measurements between the wind tunneland PI-SWERL, an average wind tunnel emission rateEwt,ave for each test was calculated by averaging the value ofEwt over the duration of the wind tunnel test. This issomewhat arbitrary since the value of Ewt,ave depends on theduration of the wind tunnel test. However, each wind tunneltest and PI-SWERL RPM level were run over the samedurations so that within the context of the present study,

Table 1. Site Location, Soil Properties, and Wind Tunnel-PI-SWERL Calibration Properties

SiteLocation

(dec. degrees) TextureaGravel%

SurfaceCharacterb

#WTc

Tests#P-Sd

Tests Disturbanceeu* (m/s)WT

u* (m/s)f

P-SzoWT

zocorrected

F1 35.368111–116.608138

SL 30 G 7 14 D 0.3–1.0 0.3–0.8 9.3E–05–9.3E–04 5.0E–05

F2 35.353055–116.566055

LS - SL 44 G 6 18 D 0.8–1.1 0.3–0.8 7.7E�05–5.6E–04 5.0E–05

L1 35.230722–116.48325

S - LS 48 G 9 16 UD 0.5–1.10.5–1.1

0.5–1.10.3–0.8

3.9E–04–2.1E–035.0E–04–1.0E–03

5.0E–05

L5 35.272944–116.367638

SIC - SICL 0 M 11 15 UD 0.3–1.70.3–1.0

0.4–0.80.2–0.6

9.3E–05–5.7E–032.4E–04–7.9E–04

5.0E–72.7E–06

L6 35.272944–116.43075

S - LS 27 G 9 18 UD 0.4–1.10.4–0.9

0.6–1.00.3–0.8

1.8E–04–4.8E–042.4E–04–8.4E–04

5.0E–05

L7 35.235833–116.445861

S 2 L 8 16 U 0.4–1.2 0.3–1.0 4.9E–05–2.2E–03 5.0E–05

N1 35.428138–116.787166

SIC - C 4 M 9 13 UD 0.3–0.80.3–0.8

0.3–0.70.2–0.7

3.5E–05–1.8E–041.3E–04–5.3E–04

5.0E–072.7E–06

N2 35.43475–116.764694

S - CL 37 G 9 13 UD 0.4–1.00.4–1.1

0.6–1.10.2–0.6

5.0E–04–9.5E–046.3E–04–2.4E–03

5.0E–052.7E–06

N3 35.418305–116.861861

SIL - SICL 6 M 10 13 UD 0.3–1.00.3–0.9

0.3–0.70.2–0.6

1.8E–04–9.5E–041.2E–04–1.4E–03

5.0E–072.7E–06

N4 35.417527–116.869055

SICL 0 M 10 15 UD 0.4–1.20.3–1.2

0.3–0.70.3–0.8

2.2E–04–2.2E–031.7E–04–8.3E–04

5.0E–075.0E–05

S1 35.241027–117.005416

C 0 M 10 11 UD 0.2–0.80.3–0.8

0.3–0.70.2–0.6

2.5E–05–2.6E–049.8E–05–2.9E–04

5.0E–072.7E–06

S3 35.238361–117.03575

C 0 M 10 15 UD 0.2–0.70.3–0.8

0.3–0.70.2–0.6

7.6E–05–1.8E–043.5E–05–4.7E–04

5.0E–072.7E–06

Z3 35.126166–116.111333

SL - LS 0 S 10 16 U 0.2–1.1 0.6–1.1 4.0E–05–1.6E–03 2.7E–06

Z4 35.119–116.110111

SL 0 S 8 11 U 0.2–0.9 0.6–1.1 4.6E–05–3.9E–04 2.7E–06

Z5 35.119–116.110111

SL 0 S 8 10 D 0.3–1.1 0.6–1.1 3.9E–04–8.1E–04 2.7E–06

A31 33.36175–115.66695

SIC - SICL 0 M 6 12 U 0.2–0.6 0.3–0.8 4.3E–07–1.2E–05 5.0E–07

A34–1 33.347933–115.966467

SL - LS 10 S 6 9 U 0.4–0.7 0.3–1.3 1.4E–05–4.8E–04 5.0E–05

A34–2 33.347617–115.96625

SL - LS 0 S 6 9 U 0.4–0.8 0.6–1.3 1.3E–05–3.5E–04 5.0E–05

A100-1 33.35265–11597265

S 16 G 5 11 U 0.3–0.6 0.3–1.0 2.6E–05–3.4E–04 5.0E–05

A100-2 33.351583–115.970683

SL - LS 0 S 6 11 U 0.2–0.8 0.6–1.3 3.6E–05–5.8E–04 5.0E–05

A101 33.350183–115.655533

SIC - SICL 0 M 6 13 U 0.2–0.6 0.3–0.8 8.0E–07–8.5E–05 5.0E–07

SS2 33.323117–115.9457

S 13 G 6 11 U 0.3–0.7 0.3–1.0 2.5E–05–3.0E–04 5.0E–05

SS16 33.341–115.667967

C 0 S 4 10 U 0.3–0.8 0.6–1.3 4.0E–05–6.2E–04 5.0E–05

aS = sand, SL = sandy loam, LS = loamy sand, SIC = silty clay, SICL = silty clay loam, SIL = silt loam, C = clay, CL = clay loam.bG = gravel, M = mud crack, L = loose sand, S = salt crust.cWT = wind tunnel.dP-S = PI-SWERL.eD = artificially disturbed surface; U = undisturbed surface.fu* corrected using zo effective, if correction required.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

6 of 12

F01012

Ewt,ave (and EPI,i for the PI-SWERL–see below) are definedconsistently.[29] Similarly, raw, one-second PI-SWERL data were

converted to average emissions of PM10 EPI,i as:

EPI ;i ¼

Pend;ibegin;1

C � F � 1s

tend;i � tbegin;i� �

� Aeff

¼Xi

1

Ei ð4Þ

where C is dust concentration (mg m�3), F is the blowerflow rate (4.1 L s�1), Aeff is the effective test areaunderneath the PI-SWERL annular ring (0.07 m2), and tis time (s) at the beginning (tbegin,i) and ending (tend,i) ofeach RPM step level, i. In order to achieve comparabilitybetween the wind tunnel and PI-SWERL data, equation (4)must be the summation of the average dust emissions fromeach RPM target level up to and including level i.[30] In examining both the PI-SWERL and wind tunnel

data (Table 1), a number of qualitative observations can bemade with regard to the relative magnitudes of windblownsand and dust transport at the sites tested. First, as Figure 4shows, sites chosen for the collocated tests display a widerange of textures from predominantly sand to predominantlyclay soils. However, there does not appear to be a largedifference in dust emissions based on texture alone(Figure 5). For example, according to PI-SWERL measure-ments, at a u* of approximately 0.56 m s�1 for undisturbed

sites with relatively coarse texture (A100-1, A100-2, A34-1,A34-2, L1, L6, L7, N2, SS2, Z3, and Z4; Table 1, Figure 5)PM10 dust emissions range from 0.005–0.5 mg m�2 s�1

with an average across the 11 sites of 0.1 mg m�2 s�1. Thecorresponding range of emissions for relatively fine-tex-tured undisturbed sites (L1, N1, N3, N4, S1, S3, A31,A101, and SS16) is 0.008–0.2 mg m�2 s�1 with an averageacross nine sites of 0.04 mg m�2 s�1. Second, for siteswhere measurements were completed on both disturbed andundisturbed surfaces (four on coarse-textured soils and sixon fine-textured soils) dust emissions are significantlyhigher for disturbed conditions. At a u* of approximately0.56 m s�1, dust emissions ranged from 0.005–0.2 mg m�2

s�1 for undisturbed sites with an average of 0.04 mg m�2

s�1 while they ranged from 0.009–7 mg m�2 s�1 fordisturbed sites with an average of 3 mg m�2 s�1. Thissuggests that disturbance at these sites increases emissionsby a factor of approximately 100. This trend also holds ifsites with fine-textured soils and those with coarse texturedsoils are examined separately. Third, of the ten surfaces withthe highest dust emissions (out of 32 different surfacestested), seven contained substantial sediment collected inthe wind tunnel sediment trap (S1, S3, L5, N4, N3, N2, F1)indicating that at those sites, saltation of mineral or aggre-gate grains likely had a major role in the measured dustemissions. Of the seven sites, five had soil textures with asand content below 20% (S1, S3, L5, N4, N3) while twohad sand contents greater than 80% (N2 and F1). Fourth, the

Figure 4. Ternary plot of soil textures (sand, silt, and clay) at each testing site. Texture was measuredusing laser particle size analysis. Symbols filled with the same color represent replicate samples from thesame site.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

7 of 12

F01012

highest dust emissions also corresponded to the driest soilswith moisture contents generally less than 1%.[31] Wind tunnel data were compared to PI-SWERL

measurements on a site-by-site basis. For each test, theaverage PI-SWERL PM10 emissions (mg m�2 s�1) werecalculated for every RPM level. Measurements from mul-tiple locations at the same site were then aggregated toobtain site geometric means of PI-SWERL emissions.Initially, equation (2) with kR set to unity was used tocalculate the friction velocity associated with each PI-SWERL RPM level i. In that friction velocity settings forthe wind tunnel did not exactly correspond to frictionvelocities estimated for the PI-SWERL RPM settings, PI-SWERL data were interpolated to estimate equivalent windtunnel emissions. Replicate measurements, as such, werenot available for the wind tunnel. This is because thefriction velocities corresponding to Type 1 tests, wherethe fan speed was rapidly increased to and held at a presetRPM, were calculated after the wind tunnel run based on theprofile of the measured velocity gradient in the wind tunnel.This precluded the ability to set a desired value of frictionvelocity a priori. Therefore where available, wind tunneldata from Type 2, stepped tests were interpolated to estimatenominal replicate measurement(s) for the Type 1 test at agiven site. This type of replicate measurement was availableat 15 of the 32 sites. For measurement locations or frictionvelocities where a Type 2 test was not available, thelogarithmic standard deviation of the measurement wasassumed to equal the average value calculated from allother points where a nominal replicate measurement wasavailable.[32] In general, the relationship between PI-SWERL and

wind tunnel measured emissions was consistent, though

both devices measure variable dust emissions for locationsadjacent to each other on the same landform. This variabil-ity appears to be largely random and probably results fromthe inherent spatial variability of dust emissions, which iswell documented in the literature [Gillette, 1977; Nicklingand Gillies, 1989, 1991; Stetler et al., 1994; Houserand Nickling, 2001]. Despite the apparent variability inthese data, a systematic difference between PI-SWERLemissions and wind tunnel-measured emissions was notedat two sites, L1 and L6, for both the undisturbed surface andthe artificially disturbed surface. At those two sites, PI-SWERL emissions were substantially higher at all values offriction velocity. In examining the photographs of thesurfaces at those two sites and comparing with all the othersites, it was clear that L1 and L6 were the only two siteswhere there was a heavy (almost packed) cover of non-erodible, roughness elements–gravel in this case. Theseobservations suggest that the likely underlying cause for theapparent mismatch between PI-SWERL and wind tunnelemissions at L1 and L6 is that the relationship betweenRPM and friction velocity used for the PI-SWERL (equa-tion (2)) underestimates the actual friction velocity. This isbecause at a given RPM, the amount of shear stressgenerated over a rough gravel surface is different than overa smooth surface (z0 � 2.7 mm for smooth plywood,Borrmann and Jaenicke, 1987). Thus a correction to thecurve represented by equation (2) is required to moreaccurately represent shear stress dependence on RPM forL1 and L6. The turbulent equations for Couette flow, wheretwo infinite plates move parallel to one another without apressure gradient [Schlichting and Gersten, 2000], can beapplied to approximately represent the shearing flow in thePI-SWERL, where a flat annular ring rotates parallel to the

Figure 5. Comparison of PM10 emissions from individual PI-SWERL and wind tunnel tests at u* =0.56 m s�1. Letter designations following site names indicate U = undisturbed site and D = disturbedsite. The averages of the PI-SWERL data for each site were used to arrange data from left to right inincreasing order of emissions.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

8 of 12

F01012

ground. According to the Couette flow equations, at aspecific RPM, the friction velocity generated between asmooth surface (PI-SWERL blade) and a rough surface(gravel cover, corresponding to ks = 3.1 mm or equivalent tohemispheres with a radius of 2.6 mm packed tightly;Schlichting, 1936) is 40% higher than the friction velocitybetween two smooth surfaces (PI-SWERL blade and ply-wood surface) at the same RPM and distance separation.That is, for soil surfaces with densely packed, non-erodibleroughness elements, the value of kR should be 1.4.[33] While the type of empirical roughness correction to

the friction velocity versus RPM curve described above isacceptable when the sites are distinct from one another anda ‘‘rule of thumb’’ can be implemented with the aid of sitephotographs, it would be preferable to measure the frictionvelocity directly at each site. It should be noted that theGuelph wind tunnel data do not require such a correctionbecause a vertical profile of the wind speed gradient abovethe ground is obtained at each test location, providing amore direct measure of shear stress and friction velocity.[34] The relationship between wind tunnel measured

PM10 emissions and PI-SWERL PM10 emissions at all siteswhere they were collocated and at all the levels of frictionvelocity at which the wind tunnel was operated, yields thecorrespondence shown in Figure 6. It is apparent that thesetypes of measurements are inherently noisy due to thenatural heterogeneities of the soil surface as noted above.This is true for both the wind tunnel (vertical error barsrepresent an estimate of geometric standard deviations fromreplicate measurements) and the PI-SWERL (horizontalerror bars represent geometric standard deviations). Boththe wind tunnel and the PI-SWERL measure dust emissionsfrom a given surface. However, the PI-SWERL does notobey many of the scaling laws that the wind tunnel does.There is a large number of ways to relate the two types of

measurements, but among the simplest is a power lawrelation such as:

log10 EWT ¼ b � log10 EPI þ a ð5Þ

or equivalently

EWT ¼ 10a � EPIð Þb ð6Þ

where E is measured emission flux in mg m�2 s�1, a and bare fitted constants, and the subscripts WT and PI refer towind tunnel and PI-SWERL, respectively. Estimates of aand b were obtained using the data shown in Figure 6 and astatistical technique known as structural, or sometimesfunctional analysis [Mark and Church, 1977]. This type ofanalysis is needed when solving for an unbiased estimate ofa linear regression model when both the x and y variablescontain measurement or other random errors. The com-monly used least squares regression method assumes thatthere are no errors associated with the x values; Mark andChurch [1977] discuss the frequent misuse and the resultingbias that is introduced by least squares regression inestimation of linear models when both variables have anassociated error. There are several variations of how thistechnique is applied depending on what is known about thevariables and their distributions [e.g., Madansky, 1959;Kendall and Stuart, 1961; Moran, 1971; Wong, 1989;Draper and Smith, 1998].[35] Unlike least squares regression, functional analysis

requires some knowledge of the magnitudes and distribu-tions of the errors associated with the x and y variables. Weexamine the model as laid out by Mark and Church [1977]using similar notation and proceed to explain how some ofthe needed parameters were estimated. It is useful therefore

Figure 6. Comparison of PI-SWERL and Guelph wind tunnel emissions measurements. Squaresrepresent data points where the PI-SWERL friction velocity has been corrected for heavy gravel cover onthe surface. Error bars for both PI-SWERL and wind tunnel data are shown in the horizontal and verticaldirections, respectively and represent one geometric standard deviation. Best fit parameters shown werecalculated using equation (11) to estimate l.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

9 of 12

F01012

to let X represent log10EPI and Y represent log10EWT andrecast equation (5) in simpler form as Y = bX + a.[36] The maximum likelihood estimate of b using a

functional relationship is given by

bf ¼ Syy � lSxx þffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiSyy � lSxx� �2þ4lS2xyh ir �

=2Sxy ð7Þ

where Syy, Sxx, and Sxy can be calculated directly from the Nobservations of xi and yi, and their respective sample meansusing:

Sxx ¼XNi¼1

xi � ~xð Þ2; Syy ¼XNi¼1

yi � ~yð Þ2;

Sxy ¼XNi¼1

xi � ~xð Þ yi � ~yð Þ: ð8a; b; cÞ

[37] There are a number of ways to estimate l dependingon how well the uncertainties of the measurements areknown. If the uncertainties are not well quantified, thenl’s maximum likelihood estimate is given by Syy/Sxx. Thisgives the ‘‘reduced major axis’’ solution with l = 0.96, bm =0.98, am = �0.03, R = Sxy/

ffiffiffiffiffiffiffiffiffiffiffiffiSxxSyy

p= 0.76. Note that R is the

product-moment correlation coefficient and that a is simplycalculated from rearranging equation (5) and solving usingthe sample means for xi and yi.[38] A better estimate can be achieved if l was calculated

from the ratio of the error variances of the two variables.That is,

l ¼ s2y=s

2x ð9Þ

s2x ¼

Xmj¼1

dxj þ exj� �2

=m; s2y ¼

Xmj¼1

dyj þ eyj� �2

=m ð10a; bÞ

where m is the number of replicate observations of thevariable and d and e are, respectively, the real randomeffects (such as due to spatial heterogeneity) and the error ofmeasurement. The present data set consists of PI-SWERLemissions estimates with replicate measurements for eachdata point appearing in Figure 6 and wind tunnel emissionsestimates with nominal replicate measurements for some ofthe data points in this Figure. For a value of l that is moreclosely tied to the available data (than provided by thereduced major axis solution), the ratio of sy

2 to sx2 was

estimated by utilizing the average sample variance from alldata points where a replicate measurement was availableusing the approximation

l ¼s2y

s2x

�

Plj¼1

Pmj

k¼1

yj;q � Yj� �2

=mj

�=l

Ppj¼1

Pqjk¼1

xj;q � Xj

� �2=qj

�=p

: ð11Þ

[39] Starting with the denominator, p is the number of PI-SWERL data points (100), qj is the number of replicatemeasurements associated with Xj, and Xj is the sample mean

of the qj replicate measurements. For the numerator, weconsider only the l wind tunnel data points that do havereplicate measurements (56). Otherwise, the summation isquite similar to the denominator with mj equal to the numberof replicate measurements represented by Yj. While there aremore analytically sound methods for estimating l, equation(11) captures the basic information that is needed for anestimate of b, namely a means to quantify how well thevalues of yi are known as compared to how well the valuesof xi are known.[40] Using equation (11), we obtain l = 0.68, bf = 1.0,

af = 0.03, R = Sxy/ffiffiffiffiffiffiffiffiffiffiffiffiSxxSyy

p= 0.76. Interestingly, this

technique which results in a different value for l yieldsestimates for b and a that are similar to the reduced majoraxis model. Specifically, both approaches give a value for athat is close to zero and a value of b that is close to unity. Thissuggests that equation (6) reduces to EWT ffi EPI.[41] The above analysis leads to an interesting point

regarding the value of 0.07 m2 used for the effective area,Aeff, in equation (4). As mentioned in an earlier section, thelocations of the vertical lines in Figure 3 that gave this valueof Aeff were determined by reasoning that the majority of themeasured dust emissions in the PI-SWERL chamber orig-inate from areas of elevated shear stress immediatelyunderneath the rotating annular blade. This reasoning seemsto be supported by the small value of a (i.e., the relationshipbetween wind tunnel and PI-SWERL measurements is closeto 1:1 using this value of Aeff).[42] Note that Figure 6 also shows the results when the

same type of analysis is done on only surfaces with a heavygravel cover, surfaces without a gravel cover, and surfaceswhere the sand saltation flux measured by wind tunnelexceeded 100 mg m�1 s�1. The implications are discussedin greater detail in the subsequent section. Here, we onlynote that in general, estimates of b are close to unity in allthese cases.[43] Despite the overall good correlation between wind

tunnel and PI-SWERL measurements of dust emissions,Figure 5 suggests that there may be some site-based bias forsites with low emissions. Specifically, sites N2-U, A101-U,L5-U, and N4-U, N1-U, N3-U, and S3-U all exhibit higherwind tunnel-measured emissions compared to PI-SWERLemissions. Except for N2-U, these sites are all characterizedby mud-cracked surfaces. The reason for this apparentdiscrepancy between the PI-SWERL and wind tunnel onmud-cracked surfaces is not known and may be a measure-ment artifact or an inherent measurement bias. An exampleof a measurement artifact would occur because the edges ofthe wind tunnel are placed directly on the test surface, whilethe PI-SWERL is supported by three legs external to the testchamber. Perhaps the placement of the wind tunnel disturbssome of the mud cracks and makes the test surface artifi-cially more emissive. An inherent measurement bias couldbe a consequence of the differences in design between thetwo instruments. It is possible that the mud cracks substan-tially change the distribution of shear stress in the PI-SWERL and that the relationship between RPM and shearstress expressed in equation (4) does not hold for thesetypes of surfaces. This underscores the importance ofincluding a direct measure of shear stress in the PI-SWERL.Alternatively, perhaps the longer fetch of the wind tunnelallows for aggregates to disintegrate as they roll over the

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

10 of 12

F01012

surface. However, in this case, one would expect that thesediment trap inside the wind tunnel would contain somematerial at the end of a run, while very little if any wasactually found in the trap.

5. Discussion and Conclusions

[44] One of the limitations of the PI-SWERL is that itdoes not simulate the processes or wind velocity profile ofthe atmospheric boundary layer that is typically associatedwith sediment transport by wind. Raupach and Leys [1990]have enumerated some specific attributes that are desirablein a field wind tunnel designed for sediment transportstudies. The tunnel should allow: (1) development of alogarithmic wind profile similar to that in nature, (2) repro-duction of realistic shear stresses, (3) realistic dispersion ofsuspended sediment, (4) uniform flow that avoids localscour, and (5) turbulence or gusts that encourage initiationand sustainability of erosion. Furthermore, the tunnel shouldbe long enough for sediment transport, specifically saltation,to reach equilibrium. Saltation layer development has beenfound to reach equilibrium within 10 to 15 m in a large windtunnel [Shao and Raupach, 1992] and is therefore theoret-ically possible with the University of Guelph wind tunnel.Shorter tunnels may overestimate sediment transport relatedto saltation [Shao and Raupach, 1992]. Some tunnel designsallow for the introduction of saltating sand grains to facilitatesaltation equilibrium [Raupach and Leys, 1990; Houser andNickling, 2001;Maurer et al., 2006]. The height of the tunnelis important for avoiding artificial limitation of the saltationprocess. The Froude Number is the relevant dimensionlessparameter for saltation [Owen and Gillette, 1985; White andMounla, 1991] and Froude Numbers <10–20 [Owen andGillette, 1985; White and Mounla, 1991] are often cited asappropriate for designing field wind tunnels for sedimenttransport studies.[45] The PI-SWERL does not meet many of these guide-

lines for large field wind tunnel design and may have someof the same shortcomings as small straight-line wind tunnelsthat have been used in the past [e.g., Gillette et al., 1982;Butterfield, 1991]. Because of the comparatively thinboundary layer formed in small tunnels, roughness elements(such as vegetation or large rocks) may alter the shear stressdistribution on the soil surface resulting in unrealisticsediment flux. The limited height of the rotating bladeand the close proximity of the outer casing of PI-SWERLmay cause sand grains to ricochet off of these surfaces,resulting in sediment transport conditions that are quitedifferent from natural saltation and resultant dust emission.[46] These observations suggest that the PI-SWERL may

not be ideally suited for detailed study of the mechanics ofwind erosion or the direct simulation of the effect of theatmospheric boundary layer on soil surfaces. Despite theselimitations, the collocated measurements reported here in-dicate that the PI-SWERL can provide a very reasonablemeasure of the potential for dust emission over a range ofsoil types encompassing a range of textures, surface rough-ness, and degree of surface crust formation. In Figure 6,each of the best fit lines for the different types of surfacesconsidered lies close to the 1:1 line suggesting that PI-SWERL- and wind tunnel- measured emissions are approx-imately equivalent when considered over a large number of

replicate measurements. Furthermore, the black squares inFigure 6 that represent data points from sites where theGuelph wind tunnel recorded sand transport (>100 mg m�1

s�1) are within a factor of five or so of the 1:1 line.Considering the variability inherent to these types of meas-urements, this represents good agreement. We note, how-ever, that this agreement is based on experimental data.Thus extension of the correlation between PI-SWERL andlarge wind tunnel measurements to soil surface types notrepresented in this study (e.g., sand dunes) may be tenuousin the absence of additional information–either throughfundamental understanding of the differences and similari-ties between the two methods or through further collocatedtesting. As an example of the need to exercise caution whencomparing PI-SWERL measurements to values presented inthe literature, in the specific case of mud-cracked surfacesexamined in this study, there appears to be a bias betweendust emissions measured with the wind tunnel and thosemeasured with the PI-SWERL.[47] The great advantage of the PI-SWERL over large

field wind tunnels and even smaller, more portable tunnelsis the ability to obtain numerous measurements economi-cally and rapidly at sites that pose logistical difficulties. ThePI-SWERL is highly portable, requires minimal setup time,and is easy to operate. Recently, the entire unit has beenmounted on an ATV to further facilitate field measurementsin difficult terrain. This study suggests that while the PI-SWERL does not meet many of the criteria strictly requiredfor field wind tunnels used in aeolian sediment transportstudies, a reasonable correspondence exists for a variety ofsoil surface types, including those where saltation may playa large role in dust emissions. In addition to continuedcharacterization of this novel measurement method, plannedfuture work includes incorporation of direct measurement ofshear stress during testing, inclusion of sensors or sand trapsfor quantifying the degree of sand transport at each test, andevaluating the effect of the size of the PI-SWERL test areaon the variability of the results.

[48] Acknowledgments. This study was funded by the Army Re-search Office, contract DAAD19-03-1-0159, and by the State of CaliforniaDepartment of Water Resources, contract 4600003670. The researchpresented is sponsored by the Department of the Army, Army ResearchOffice; the content of the information does not necessarily reflect theposition or the policy of the federal government, and no official endorse-ment should be inferred. We thank Matt Alexander, Jon Belanger, MonikaSaaliste, Brian Boyd, Jeremy Burke, Kristen Self for assistance in the field,and Ruth Sparks (ITAM, National Training Center) for logistical support.Permission to work in the Mojave National Preserve was granted by theU.S. National Park Service.

ReferencesAlfaro, S. C., J. L. Rajot, and W. G. Nickling (2004), Estimation of PM20emissions by wind erosion: Main sources of uncertainties, Geomorphol-ogy, 59(1–4), 63–74.

Bagnold, R. A. (1941), The Physics of Blown Sand and Desert Dunes,265 pp., Chapman and Hall, London.

Belnap, J., and D. A. Gillette (1998), Vulnerability of desert biological soilcrusts to wind erosion: The influences of crust development, soil texture,and disturbance, J. Arid Environ., 39, 133–142.

Bisal, F., and J. Hsieh (1966), Influence of moisture on erodibility of soil bywind, Soil Sci., 3, 143–146.

Borrmann, S., and R. Jaenicke (1987), Wind tunnel experiments on theresuspension of sub-micrometer particles from a sand surface, Atmos.Environ., 21, 1891–1898.

Butterfield, G. (1991), Grain transport rates in steady and unsteady turbu-lent airflows, Acta Mechanica Suppl., 1, 97–122.

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

11 of 12

F01012

Chatenet, B., B. Marticorena, L. Gomes, and G. Bergametti (1996), Asses-sing microped size distributions of desert soils erodible by wind, Sedi-mentology, 43, 901–911.

Chepil, W. S. (1956), Influence of moisture on erodibility of soil by wind,Soil Sci. Soc. Proceedings, 20, 288–292.

Chepil, W. S. (1958), The use of evenly spaced hemispheres to evaluateaerodynamic forces on a soil surface, Trans. Am. Geophys. Union, 39,397–404.

Chepil, W. S., and R. A. Milne (1939), Comparative study of soil drifting inthe field and in a wind tunnel, Sci. Agric., 249–257.

Crawley, D., and W. G. Nickling (2003), Drag partition for regularly-arrayed rough surfaces, Boundary Layer Meteorol., 107, 445–468.

Draper, N. R., and H. Smith (1998), Applied Regression Analysis, 706 pp.,John Wiley & Sons, New York, NY.

Etyemezian, V., G. Nikolich, S. Ahonen, M. Pitchford, M. Sweeney,J. Gillies, and H. Kuhns (2007), The Portable In-Situ Wind ErosionLaboratory (PI-SWERL): A new method to measure windblown dustproperties and potential for emissions, Atmos. Environ., 41, 3789–3796.

Garland, J. A. (1983), Some recent studies of the resuspension of depositedmaterial from soil and grass, in Precipitation Scavenging, Dry Depositionand Resuspension, vol. 2, edited by H. R. Pruppacher, R. G. Semonin, andW. G. N. Slinn, pp. 1087–1097, Elsevier, Amsterdam The Netherlands.

Gee, G. W., and D. Orr (2002), Particle-size analysis, in Methods of SoilAnalysis: Part 4–Physical Methods, edited by J. H. Dane and G. C.Topp, pp. 255–293, No. 5 Soil Sci. Soc. Am. Book Series, Soil ScienceSociety of America, Madison, WI.

Gillette, D. A. (1977), Fine particulate emissions due to wind erosion,Trans. ASAE, 20, 890–897.

Gillette, D. A. (1978), Tests with a portable wind tunnel for determiningwind erosion threshold velocities, Atmos. Environ., 12, 2309–2313.

Gillette, D. A. (1988), Threshold friction velocities for dust production foragricultural soils, J. Geophys. Res., 93, 12,645–12,662.

Gillette, D. A. (1999), A qualitative geophysical explanation for ‘‘hot spot’’dust emitting source regions, Contrib. Atmos. Phys., 72(1), 67–77.

Gillette, D. A., and R. Passi (1988), Modeling dust emission caused bywind erosion, J. Geophys. Res., 93(D11), 14,233–14,242.

Gillette, D. A., J. Adams, A. Endo, and D. Smith (1980), Threshold velo-cities for input of soil particles into the air by desert soils, J. Geophys.Res., 85, 5621–5630.

Gillette, D. A., J. Adams, D. Muhs, and R. Kihl (1982), Threshold frictionvelocities and rupture moduli for crusted desert soils for the input of soilparticles into the air, J. Geophys. Res., 87(C11), 9003–9015.

Gillies, J. A., N. Lancaster, W. G. Nickling, and D. Crawley (2000), Fielddetermination of drag forces and shear stress partitioning effects for adesert shrub (Sarcobatus vermiculatus, Greasewood), J. Geophys. Res.Atm., 105(D20), 24,871–24,880.

Harrison, S. P., K. E. Kohfeld, C. Roelandt, and T. Claquin (2001), The roleof dust in climate changes today, at the last glacial maximum and in thefuture, Earth Sci. Rev., 54, 43–80.

Houser, C. A., and W. G. Nickling (2001), The emission and vertical flux ofparticulate matter <10 micrometers from a disturbed clay-crusted surface,Sedimentology, 48, 255–267.

Irwin, H. P. A. H. (1980), A simple omnidirectional sensor for wind tunnelstudies of pedestrian level winds, J. Wind Eng. Ind. Aerodyn., 7, 219–239.

James, D. E., J. Pulgarin, J. Becker, S. Edwards, T. Gingras, G. Venglass,and C. MacDougall (2001), Development of vacant land emission PM-10: Factors in the Las Vegas Valley, Report Prepared by the Civil andEnvironmental Engineering Department, UNLV, for Clark CountyDepartment of Comprehensive Planning, Las Vegas NV, 16 p.

Kendall, M. G., and A. Stuart (1961), The Advanced Theory of Statistics,26 cm, Griffin London.

Logie, M. (1982), Influence of roughness elements and soil moisture on theresistance of sand to wind erosion, in Aridic Soils and GeomorphicProcesses, edited by D. H. Yaalon, Catena Supplement 1, pp. 161–173, Catena Verlag GMBH, Armelgasse.

Madansky, A. (1959), The fitting of straight lines when both variables aresubject to error, J. Am. Stat. Soc., 54, 173–205.

Mark, D. M., and M. Church (1977), On the misuse of regression in Earthsciences, Math. Geol., 9(1), 63–75.

Maurer, T., L. Herrmann, T. Gaiser, M. Mounkaila, and K. Stahr (2006), Amobile wind tunnel for wind erosion field measurements, J. Arid Environ.,66, 257–271.

McKenna Neuman, C., and S. M. Maljaars (1998), A wind tunnel study ofthe influence of pore water on aeolian sediment transport, J. Arid Envir-on., 39, 403–419.

McKenna Neuman, C., and W. G. Nickling (1989), A theoretical and windtunnel investigation of the effect of capillary water on the entrainment ofsediment by wind, Can. J. Soil Sci., 69(1), 79–96.

Moran, P. A. P. (1971), Estimating structural and functional relationships,J. Mult. Anal., 1, 232–255.

Nickling, W. G., and J. A. Gillies (1989), Emission of fine-grained parti-culates from desert soils, in Paleoclimatology and Paleometeorology:Modern and Past Patterns of Global Atmospheric Transport, edited byM. Leinen and M. Sarnthein, pp. 133–165, Series C: Mathematical andPhysical Sciences, vol. 282, Kluwer Academic Publishers, Dordrecht,The Netherlands.

Nickling, W. G., and J. A. Gillies (1991), Sahelian aerosols (Mali), FinalTechnical Report No. 3, Department of Geography, University of Guelph,Ontario, Canada, 44 p.

Nickling, W. G., and C. McKenna Neuman (1995), Development of defla-tion lag surfaces, Sedimentology, 42(3), 403–414.

Nickling, W. G., and C. McKenna Neuman (1997), Wind tunnel evaluationof a wedge-shaped aeolian transport trap, Geomorphology, 18, 333–345.

Owen, P. R., and D. A. Gillette (1985), Wind tunnel constraint on saltation,in The International Workshop on the Physics of Blown Sand, Vol. 8,edited by O. E. Barndorff-Nielsen, pp. 253–269, University of AarhusPress, Aarhus, Denmark.

Pewe, T. L. (1981), Desert dust: origin, characteristics, and effect on man,Geol. Soc. America Spec. Pub., 186, 303 p.

Pietersma, D., L. D. Stetler, and K. E. Saxton (1996), Design and aero-dynamics of portable wind tunnel for soil erosion and fugitive dust re-search, Trans. Assoc. Agric. Eng., 39(6), 2075–2083.

Prandtl, L. (1935), Aerodynamic Theory Vol. III, 223 pp., Springer-Verlag,Berlin.

Pye, K. (1987), Aeolian dust and dust deposits, 334 p. Academic Press,London.

Raupach,M. R., and J. F. Leys (1990), Aerodynamics of a portable wind tunnelfor measuring soil erodibility by wind, Aust. J. Soil Res., 28, 177–191.

Rice, M. A., B. B. Willetts, and I. K. McEwan (1996), Observations ofcollisions of saltating grains with a granular bed from high-speed cinefilm, Sedimentology, 43(1), 21–32.

Rice, M. A., C. E. Mullins, and I. K. McEwan (1997), An analysis of soilcrust strength in relation to potential erosion by saltating particles, EarthSurf. Processes and Landforms, 22(9), 859–884.

Saleh, A., and D. W. Fryrear (1995), Threshold wind velocities of wet soilsas affected by wind blown sand, Soil Sci., 160, 304–309.

Saxton, K., D. Chandler, L. Stetler, B. Lamb, C. Claiborn, and B.-H. Lee(2000), Wind erosion and fugitive dust fluxes on agricultural lands in thePacific Northwest, Trans. Assoc. Agric. Eng., 43(2), 623–630.

Schlichting, H. (1936), Experimentelle Untersuchungen zum Rauhigkeit-sproblem, Ing.-Acrh., Bd., 7, 1–34.

Schlichting, H., and K. Gersten (2000), Boundary Layer Theory, 8th Edition,801 pp., Springer-Verlag, Berlin, Germany.

Shao, Y., and M. R. Raupach (1992), The overshoot and equilibration ofsaltation, J. Geophys. Res., 97(D18), 20,559–20,564.

Shao, Y., M. R. Raupach, and P. A. Findlater (1993), The effect of saltationbombardment on the entrainment of dust by wind, J. Geophys. Res., 98D,12,719–12,726.

Shinn, J. H., J. C. Kennedy, J. S. Koval, B. R. Clegg, and W. M. Porch(1976), Observations of dust flux in the surface boundary layer for steadyand nonsteady cases, in Proc. Atmosphere-Surface Exchange of Particu-late and Gaseous Pollutants, Richmond WA, Energy Research and De-velopment Administration, Symposium Series CONF-740921, 625–637.

Stetler, L. D., and K. E. Saxton (1995), Fugitive dust (PM10) emissions anderoded soil relations from agricultural fields on the Columbia plateau, Airand Waste Management Association Specialty Conference Proc.: Parti-culate Matter: Health and Regulatory Issues, Pittsburgh PA, 537–547.

Stetler, L. D., K. E. Saxton, and D. W. Fryrear (1994), Wind erosion andPM10 measurements from agricultural fields in Texas and Washington,Air and Waste Management Association 87th Annual Meeting & Exhibi-tion, June 19 –24 , Cincinnati OH, 94-FA145.02.

US EPA (1995), Compilation of air pollutant emission factors—Vol. 1.Stationary point and area sources, Report No. AP-42, United States En-vironmental Protection Agency, Research Triangle Park, NC.

White, B. R., and H. Mounla (1991), Froude number effect on wind tunnelsaltation, Acta Mechanica Supplementum, 1, 145–157.

Wong, M. Y. (1989), Likelihood estimation of a simple linear regressionmodel when both variables have error, Biometrika, 76(1), 141–148.

�����������������������V. Etyemezian and G. Nikolich, Division of Atmospheric Sciences,

Desert Research Institute, Las Vegas, NV, USA.J. Gillies, Division of Atmospheric Sciences, Desert Research Institute,

Reno, NV, USA.T. Macpherson and W. Nickling, Department of Geography, University of

Guelph, Guelph, Ontario, Canada.E. McDonald, Division of Earth and Ecosystem Sciences, Desert

Research Institute, Reno, NV, USA.M. Sweeney, Earth Sciences, University of South Dakota, 414 E. Clark

St., Vermillion, SD, USA. (mark.sweeney@usd.edu)

F01012 SWEENEY ET AL.: COMPARISON OF PI-SWERL TO WIND TUNNEL

12 of 12

F01012