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Example of RPM test cycle. Thick black lines correspond to periods when measured values are used during data processing. (a) example RPM curve test cycle and (b) expanded scale corresponding to dashed box in a.
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The effect of small-scale surface roughness on measurements with PI-SWERL (The Portable In-Situ Wind Erosion Laboratory) was investigated using a viscometer-type device. The relationship between shear stress at the soil surface and PI-SWERL rate of rotation (RPM) was determined empirically with test surfaces of varying roughness including fine to c...
Contexts in source publication
Context 1
... a measurement with a new surface type the entire test plate assembly was weighed and the distribution of roughness over the test surface was noted. This information is required for calculating the moment of inertia, a critical parameter in subsequent data reduction (See Eq. (2) in Section 3.3 below). A typical example of an RPM test is shown in Fig. 2. Following the initial warm-up period, the power to the PI-SWERL is abruptly cut and the PI-SWERL is physically removed from its bench so that the influence of the decelerating annular blade is not felt by the test plate. The rotating plate is allowed to slow down and come to a stop on its own. This procedure is immediately repeated ...
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... separate test included several decay observations (see Fig. 2b) that were used to calibrate the apparatus. The mass for each test surface configuration was used to calculate the moment of inertia of the entire rotating assembly with: ...
Context 3
... and angular acceleration are the rotational analogs to mass and acceleration, respectively. Strictly, the angular velocity, angular momentum, and torque are vector quantities. However, for the apparatus described here, the only motion is rotation in one plane hence we have dropped the conventional vector notation. The decay curves shown in Fig. 2 visualize the response of the RPM as a function of time from the PI-SWERL operating at maximum speed followed by the abrupt removal of the PI-SWERL from the test bench. Once the PI-SWERL is removed, the torque acting on the test plate (T cal,i (x)) as a function of test plate angular velocity can be calculated as the sum of the torques ...
Context 4
... an intercept was used, the highest P-value for c was 0.019, but in 22 of the 25 cases, P-values were less than 0.005. Between the decay curves that were used to calibrate each test configuration i, the PI-SWERL net rate of rotation relative to the test plate/surface j (expressed as RPM) was held at a nominal value for a period of at least 5 min (Fig. 2) so that the test plate rate of rotation was at steady-state and all the torques acting on the system were in balance. This can be expressed by the ...
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Citations
... Sediment transport is influenced by, and influences, topographic surface roughness (Barchyn and Hugenholtz, 2012). Specifically, Sankey et al. (2022a) demonstrated a significant relationship between measured topographic surface roughness calculated from a lidar point cloud within a 20 cm kernel moving window to estimates of a roughness parameter used for calculating in-situ transport velocities in wind erosion experiments (Etyemezian et al., 2014). These same measurements have also been found to be correlated to measurable changes in sediment volumes related to aeolian and alluvial processes as well as changes in biocrust cover . ...
Vegetation and biological soil crust (biocrust) cover can have a stabilizing effect on dunes by fixing sediment in-place and increasing surface roughness, thus limiting dune mobility, sediment transport, and erosion. These biological effects influence rates of aeolian activity and thus surficial changes, though variability in wind and sediment supply may obscure these topographic effects. In this study, we compare monthly measures of sediment transport and decadal estimates of dune mobility to repeat topographic changes measured as a net volume change in sediment storage (difference in volume between all positive and negative topographic changes) and total volume change (absolute summed volume of all positive and negative changes) for areas of bare, vegetated, and biocrusted sand within a dunefield with limited sediment supply and unimodal winds. We found that monthly net volume changes normalized by area were similar between bare sand and sand with at least 20% vegetation cover. However, total volume change was significantly greater for bare sand and correlated with monthly sediment flux estimates (R2=0.46), though the relationship was significantly improved by including monthly changes in surface roughness (R2=0.8). Longer-term decadal trends in topographic change showed larger total volume changes with the greatest decreases in vegetation canopy cover. Additionally, decadal total volume changes strongly correlated with estimates of dune mobility (R2=0.99). We also found that increased total volume changes did not necessarily signal increased net volume changes for all land cover types. Specifically, increases in total volume change for bare sand resulted in near equal or lower net volume changes, as both positive (deposition) and negative (erosion) change increased with sediment transport. Conversely, less mobile land covers, such as biocrust covered sand, increased in erosion without significant increases in total volume change, demonstrating that more stable surfaces might exhibit a larger topographic change imbalance than mobile sediment surfaces under the same conditions. This study highlights the importance of considering multiple measures of topographic change for interpreting sediment mobility, transport, and availability. Additionally, we hypothesize a novel framework for remote sensing-based empirical studies aimed at interpreting aeolian landscape evolution resulting from climate change effects on weather as well as biological controls such as vegetation and biocrusts.
... Prior to deployment in the field, each combination of Irwin sensor head and its associated pressure transducer and power board were calibrated in a laboratory setting by exposing the Irwin sensor head to a sequence of shear stresses produced by a PI-SWERL instrument (Etyemezian et al., 2014). A smooth varnished plywood surface (0.6 m × 0.6 m) with holes slightly over-sized to the outer diameter of the Irwin sensor was used to hold the sensor head in place, flush with the plywood surface. ...
... The PI-SWERL instrument was placed over the plywood such that the centerline of the rotating blade was 50 mm above the Irwin head. The rotation of the PI-SWERL blade creates a shearing stress (N m − 2 ) (Etyemezian et al., 2014) on the surface that is measured as a differential pressure by the pressure transducer (Sensiron AG, SDP1000-L025) connected to the Irwin sensor head. Each Irwin sensor was exposed to 11 different Revolutions Per Minute (RPM) ranging from 250 RPM to 4500 RPM, in increments of 250 RPM from 250 -1000 RPM, and in increments of 500 RPM from 1500 -4000 RPM. ...
... Each RPM step lasted 60 seconds and a pressure transducer signal was sampled at 10 Hz from which step average output was calculated to develop a calibration relation between mV output from the pressure transducer and u s* (m s − 1 ). The RPM of the blade was converted to u s* using the smooth surface relationship of Etyemezian et al. (2014): ...
Accurate representation of surface roughness in predictive models of aeolian sediment transport and dust emission is required for model accuracy and for models to inform wind erosion management. While past wind tunnel and field studies have examined roughness effects on drag partitioning, the spatial and temporal variability of surface shear velocity and the shear stress ratio remain poorly described. Here, we use a four-month dataset of total shear velocity (u *) and soil surface shear velocity (u s*) measurements to examine the spatio-temporal variability of the shear stress ratio (R) before, during, and after vegetation green-up at a honey mesquite (Prosopis glandulosa Torr.) shrub-invaded grassland in the Chihuahuan Desert, New Mexico, USA. Results show that vegetation green-up, the emergence of leaves, led to increased drag and surface aerodynamic sheltering and a reduction in u s* and R magnitude and variability. We found that u s* decreased from 20% to 5% of u * as the vegetation form drag and its sheltering effect increased. Similarly, the spatiotemporal variability of R was found to be linked directly to plant phenological phases. We conclude that drag partition schemes should incorporate seasonal vegetation change, via dynamic drag coefficients and/or R, to accurately predict the timing and magnitude of seasonal aeolian sediment fluxes. The drag partition response to mesquite phenological phases also provided insight to potential mesquite herbicide treatment effects which, if successful, could increase wind erosivity and the onsite and downwind impacts of wind erosion unless protection by herbaceous plants is maintained.
... Etyemezian et al. (2007) used Irwin sensors placed under the rotating blades to develop an empirical relationship between the RPM of the spinning blades and shear velocity (u * ) on the underlying ground surface. Etyemezian et al. (2014) showed that the accuracy of the estimation of u * from RPM (m/s) of the rotating blades could be improved to a very high accuracy (R 2 of measured vs. predicted u * = 0.99) using the following empirical equation which incorporates a variable (α) for surface roughness: ...
... Sweeney et al. (2008) determined nearly exact correspondence in dust emissions measured with the PI-SWERL compared to an inline wind tunnel and proposed that corrections for PI-SWERL estimates on rough surfaces be developed to improve that correspondence. Etyemezian et al. (2014) developed roughness corrections (α coefficient in Equation 5) based on soil surface texture, and we implemented a simple empirical method using lidar to measure those roughness coefficient values (Equation 6; Text S1 in Supporting Information S1). Van Leeuwen et al. (2021) compared u *t measured with the PI-SWERL and a straight-line wind tunnel on fine sand and loamy sand; they determined nearly exact correspondence between methods for the fine sand surface, which is similar in texture to the sandy surfaces we studied, but not the loamy sand which consisted of more small dust-sized particles. ...
Transgression and regression of water levels (stages) have impacted the evolution of aeolian landforms and sedimentary deposits throughout geologic history. We studied this phenomenon over a 5‐day period of reduced flow on the Colorado River in Grand Canyon National Park, AZ, USA, in March 2021. These transient low flows exposed river‐channel sand deposits to the air, causing progressive desiccation (drying) and thereby making these deposits susceptible to aeolian transport. We measured aeolian threshold friction velocities (u*t) for sand saltation and PM10 dust emissions, as well as other characteristics, on a subaerially exposed sandbar and downwind aeolian dunefield during each day of the low river flow. The sandbar transitioned from supply‐limited to transport‐limited aeolian sediment transport conditions during the regression in river water stage. A possible tipping point between the two transport conditions occurred approximately 48 hr after the drop in river flow. The empirically measured u*t decreased as the sandbar sediment dried with increased subaerial exposure time. Theoretical estimates and empirical measurements of u*t corresponded closely on the aeolian dunefield and on the sandbar when it was drier during the third and fourth day of the experiment. Eighty‐seven percent of the variability in u*t was explained by empirical models that provide practical estimates of aeolian transport potential of subaerial river sediment deposits using monitoring data that are commonly available in this and other river systems. The work provides theoretical insight into the response of aeolian processes to sediment supply changes driven by periods of anthropogenic activity, drought, and climate change.
... The hybrid test was chosen over a continuous RPM ramp because it identifies surfaces that are not supply limited. Similar hybrid tests have been used for previous studies (Kavouras et al., 2009;Etyemezian et al., 2014;Fick et al., 2019). Following test initiation, the PI-SWERL was controlled by and all test data, including data from the DustTrak, were logged in an imbedded computer. ...
Abstract
Lordsburg Playa, a dry lakebed in the Chihuahuan Desert of southwestern New Mexico, is crossed by Interstate 10 (I-10). Clouds of dust blowing from the playa onto the highway represent an acute traffic safety hazard, making I-10 at Lordsburg Playa the deadliest stretch of highway in the United States for dust hazard. Metals contained in Lordsburg
Playa dust may represent an additional health risk to motorists and others exposed to them. The research team performed field investigations at Lordsburg Playa to assess land-surface conditions contributing to dust emission, quantify dust emissivity of playa surfaces using a portable in-situ wind erosion laboratory, and collect playa materials for subsequent analysis and modeling. Threshold friction velocities for dust entrainment ranged from < 0.30 ms−1 for delta and shoreline areas to > 0.55 m s−1 for ephemerally flooded playa areas; mean PM10 vertical flux rates ranged from < 500 μg m−2s−1 for ephemerally flooded playa areas to ~25,000 μg m−2s−1 for disturbed delta surfaces. The unlimited supply of coarse sediments along the western playa shoreline may contribute particles triggering dust events; this zone should be a focus area for dust mitigation efforts. If playa surface crusts become disturbed, loose sediment underneath is revealed, increasing dust emission potential; thus, activities on the playa that degrade the crust should continue to be restricted. Playa sediments generally were rich in silt, composed of minerals common to regional playas, and contained up to ~20 ppm bioavailable Pb, ~1830 ppm bioavailable Cu, and ~4690 ppm bioavailable Zn (possibly reflecting trafficrelated emissions deposited near the highway). Researchers combined two numerical models, the Single-Event Wind Erosion Evaluation Program and the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model, to estimate the generation and dispersion of dust and PM10 from key hotspots during two typical dust event days using data from field and laboratory tests, soil and land databases, and on-playa weather stations. Results from modeling were consistent with observations from traffic webcam photos and visibility records from meteorological sites. The combined models predicted transient dust PM10 concentrations of up to ~200,000 μg m−3 could impact I-10, presenting acute but short exposures to dust and airborne metals at the highway, although these brief exposures are not likely to significantly impact the long-term aerosol inhalation burden to anyone caught in a playa dust event.
... Prior to deployment in the field, each combination of Irwin sensor head and its associated pressure transducer and power board were calibrated in a laboratory setting by exposing the Irwin sensor head to a sequence of shear stresses produced by a PI-SWERL instrument (Etyemezian et al., 2014). A smooth varnished plywood surface (0.6 m × 0.6 m) with holes slightly over-sized to the outer diameter of the Irwin sensor was used to hold the sensor head in place, flush with the plywood surface. ...
... The PI-SWERL instrument was placed over the plywood such that the centerline of the rotating blade was 50 mm above the Irwin head. The rotation of the PI-SWERL blade creates a shearing stress (N m − 2 ) (Etyemezian et al., 2014) on the surface that is measured as a differential pressure by the pressure transducer (Sensiron AG, SDP1000-L025) connected to the Irwin sensor head. Each Irwin sensor was exposed to 11 different Revolutions Per Minute (RPM) ranging from 250 RPM to 4500 RPM, in increments of 250 RPM from 250 -1000 RPM, and in increments of 500 RPM from 1500 -4000 RPM. ...
... Each RPM step lasted 60 seconds and a pressure transducer signal was sampled at 10 Hz from which step average output was calculated to develop a calibration relation between mV output from the pressure transducer and u s* (m s − 1 ). The RPM of the blade was converted to u s* using the smooth surface relationship of Etyemezian et al. (2014): ...
... Each PS sample was collected in a sterile Whirlpak® bag attached to the outlet of the PI-SWERL. For each PS sample, dust was collected from a 15-min run of the PI-SWERL at 3500 RPM, which represents a friction velocity of 0.85 m s −1 , using an alpha value of 0.90 and the relationship as proposed by Etyemezian et al. (2014) [26]. This corresponds to a wind speed of approximately 16 m s −1 (57 km/h). ...
... Each PS sample was collected in a sterile Whirlpak® bag attached to the outlet of the PI-SWERL. For each PS sample, dust was collected from a 15-min run of the PI-SWERL at 3500 RPM, which represents a friction velocity of 0.85 m s −1 , using an alpha value of 0.90 and the relationship as proposed by Etyemezian et al. (2014) [26]. This corresponds to a wind speed of approximately 16 m s −1 (57 km/h). ...
Dust is a major vehicle for the dispersal of microorganisms across the globe. While much attention has been focused on microbial dispersal in dust plumes from major natural dust sources, very little is known about the fractionation processes that select for the “dust microbiome.” The recent identification of highly emissive, agricultural land dust sources in South Africa has provided the opportunity to study the displacement of microbial communities through dust generation and transport. In this study, we aimed to document the microbial communities that are carried in the dust from one of South Africa’s most emissive locations, and to investigate the selective factors that control the partitioning of microbial communities from soil to dust. For this purpose, dust samples were generated at different emission sources using a Portable In-Situ Wind Erosion Lab (PI-SWERL), and the taxonomic composition of the resulting microbiomes was compared with the source soils. Dust emission processes resulted in the clear fractionation of the soil bacterial community, where dust samples were significantly enriched in spore-forming taxa. Conversely, little fractionation was observed in the soil fungal communities, such that the dust fungal fingerprint could be used to identify the source soil. Dust microbiomes were also found to vary according to the emission source, suggesting that land use significantly affected the structure and fractionation of microbial communities transported in dust plumes. In addition, several potential biological allergens of fungal origin were detected in the dust microbiomes, highlighting the potential detrimental effects of dust plumes emitted in South Africa. This study represents the first description of the fractionation of microbial taxa occurring at the source of dust plumes and provides a direct link between land use and its impact on the dust microbiome.
... The advantage of the PI-SWERL, in comparison to wind tunnels, are its small size, lower weight, and high frequency of measurement runs, which enables many repeated measurements in a relatively short time. The major disadvantage of the PI-SWERL is its shallow annular blade (at 5 cm height) and lack of a naturally developed logarithmic wind profile, making it less representative of natural wind erosion and determining the influence of surface roughness then requires a separate assessment (Bacon et al., 2011;Etyemezian et al., 2014). Despite these disadvantages, the PI-SWERL was successfully used under laboratory conditions to assess dust emissions from crusts and loose substrates (van Leeuwen et al., 2021;Vos et al., 2020) and on different surface types in various regions of the world. ...
... Later, the surface areas of the clods were determined by measuring the diameter and counting the number of clods in an image of the areas sampled by the PI-SWERL. Surfaces with more than 5% clods were excluded because the quality of PI-SWERL measurements on surfaces with high roughness is not well understood (Bacon et al., 2011;Etyemezian et al., 2014). Since the aim of this study was to understand the emissions of loose substrates and crusts in the field, this omission did not limit the scope of this study. ...
... At the end of the run, the RPM was brought back to zero in 10 s. An RPM of 2250 is in accordance with a friction velocity of 0.56 m s − 1 assuming an alpha value of 0.90 as described by Etyemezian et al. (2014). This is a friction velocity that is similar to the one used in most PI-SWERL (Sweeney et al., 2011;Sweeney et al., 2008;Sweeney & Mason, 2013;von Holdt et al., 2019). ...
The Free State has been identified as the region with the most dust sources in South Africa. These dust sources can be linked with the large, heavily cultivated cropland areas in this province, which leaves fields vulnerable to wind erosion after the harvest in the winter. For this study, the focus was on the factors that influence the emission from bare, flat surfaces on agricultural lands in this region. The Portable In-Situ Wind Erosion Laboratory (PI-SWERL) was used to measure the emission flux from adjacent crusted and loose surfaces, which was combined with shear strength, moisture, and soil texture measurements. Boosted regression tree (BRT) analyses were used to identify the variable with the highest relevance on the emission flux.
On the whole dataset, that the shear strength is the most important variable that controls the emission. This is reflected in the significantly lower emission from the crusted surfaces (0.49 mg m⁻² s⁻¹) compared to that of loose surfaces (2.34 mg m⁻² s⁻¹). However, for crusted surfaces, the presence of abraders appeared to be the most significant factor in emission, showing a power relationship between the abrader count and the emission flux (R² = 0.76). In the case of the loose surfaces, the presence of clay and silt was a major influence in emissivity, with a linear relationship between the two variables (R² = 0.68). This difference in factors depending on the agricultural disturbance, asks for a more holistic approach when predicting emission from such arid cropland areas.
... The friction velocity applied by the PiSwerl to the surface (3300 rpm = u* of 0.55-0.58 ms − 1 ) equated to a wind speed of approximately 8 ms − 1 at 10 m height when adjusted for surface roughness (Etyemezian et al., 2014) and represented a threshold at which saltation was initiated (Fryberger, 1979;Stout, 2007). The analysis of remote sensing data for dust emission in the Namib confirms the appropriateness of this threshold friction velocity for dust emission . ...
The characterisation of mineral dust at emission sources is essential for quantifying the wider-scale environmental impacts dust has, as well as improving its incorporation in modelling. Methods of sampling sediments at these source areas for the purposes of dust characterisation are varied and can produce different representations of emitted dust. This study systematically compared dust characterisations from three established approaches for estimating dust emission potential, namely: Big Spring Number Eight (BSNE) traps, a Portable In-situ Wind Erosion Laboratory (PI-SWERL) wind tunnel, and bulk surface sampling, at a known Namib Desert dust source. Individual particle analysis by auto-SEM (QEMSCAN) allowed comparison of size, shape, mineralogy and elemental composition at micrometre-scale for samples from the three approaches. BSNE samples consisted of a lower proportion of fine sediment (<20 µm particle diameter) than PI-SWERL-derived samples, with this performance possibly influenced by atmospheric humidity. In comparison, PI-SWERL characterised the dust with relatively fewer particles between 63 and 100 µm, a fraction that was more evident in both the BSNE-derived and surface sediments. The reduced representation of this coarser fraction resulted in appreciable differences in particle shape and mineralogic characteristics compared to BSNE and surface-derived samples. The different representations by the three methods return variable dust characteristics at source across fundamental properties of particle size, shape and mineralogy. Awareness of the different representations of dust caused by sampling technique remains essential for the appropriate physical and geochemical characterisation of aeolian dust and highlights how standardised techniques are important for meaningful comparisons, while methods to achieve accurate characterisation remain a priority for the discipline.
... This different behaviour of the shear stress led to underestimation of the threshold friction velocity for rough surfaces by the original relationship between RPM and friction velocity used for the PI-SWERL (Sweeney et al, 2008). To compensate for this underestimation, a surface roughness correction factor must be used for rough surfaces when calculating friction velocities based on the RPMs (Etyemezian et al., 2014). ...
... Values of α are based on four roughness categories, where the lowest surface roughness is linked to category A, a silt-clay crusted playa with minimal cracking and sparse gravel, and the highest surface roughness refers to category D, a gravel covered surface. Furthermore categories B (sandy loam desert soil with <5% gravel cover) and C (a rippled dune surface with <10% gravel cover) are defined (Etyemezian et al., 2014). An α of 0.94, corresponding to roughness category B, was chosen to convert the set target value of 3175 RPM to a friction velocity of 0.67 m s − 1 (Eq. ...
... Here, C dust (mg m − 3 ) is the average PM 10 concentration in the PI-SWERL's chamber, FR (L min − 1 ) is the flow rate through the chamber and A eff refers to the effective area of the instrument, which was estimated to be 0.035 m 2 for this model (Etyemezian et al., 2014). Unfortunately, it was impossible to measure dust emissions over a vertical profile and calculate the vertical PM 10 fluxes for the PWRS, because only a single DustTrak II monitor was installed in the wind tunnel. ...
Experiments in large wind tunnels have made vital contributions to our knowledge of aeolian processes. However, the size of these instruments makes them impractical for field application. To facilitate field measurements on the dust emission potential of soils, the Portable In-Situ Wind Erosion Lab (PI-SWERL) was developed. Previous research shows that the PI-SWERL can be used to quantify dust emission potentials and (threshold) friction velocities. Studies that compare the PI-SWERL to traditional wind tunnels mainly focus on the dust emission potential at various friction velocities. In the present study, we quantified the threshold friction velocity for PM10 emission using a PI-SWERL and compare it to results obtained with a straight-line wind tunnel: the Portable Wind and Rainfall Simulator of the University of Basel (PWRS). Tests were performed on two types of substrate: fine sand (NS1) and loamy sand (DS1). For NS1, a threshold friction velocity of 0.33 m s⁻¹ was identified from both the PI-SWERL and the PWRS data. For DS1, identified threshold friction velocities showed differences: 0.25 m s⁻¹ by the PI-SWERL and 0.39 m s⁻¹ by the PWRS. The position of the DustTrak II monitor’s inlet tube and variations of the fan’s speed by different operators could explain the difference in identified thresholds. Although different threshold friction velocities were obtained for one of the substrates, we believe that comparable results can be achieved by adjusting the experimental design in future research. Therefore, the PI-SWERL can be successfully used to quantify thresholds, facilitating dust emission studies in more remote regions.
... The hybrid test was chosen over a continual RPM ramp because It identifies surfaces that are not supply limited. Similar hybrid tests have been used for previous studies (Kavouras et al. 2009;Etyemezian et al. 2014;Fick et al. 2019). Following test initiation, the PI-SWERL was controlled by and all test data, including data from the DustTrak, was logged in an imbedded computer. ...
In drylands around the world, ephemeral lakes (playas) are common. Dry, wind-erodible playa sediments are potent local and regional sources of dust and PM 10 (airborne particles with diameters less than 10 μm). Dust clouds often cause sudden and/or prolonged loss of visibility to travelers on downwind roadways. Lordsburg Playa, in southwestern New Mexico, USA is bisected by Interstate Highway 10. Dust storms emanating from the playa have been responsible for numerous visibility-related road closures (including 39 road closures between 2012 and 2019) causing major economic losses, in addition to well over a hundred dust-related vehicle crashes causing at least 41 lost lives in the last 53 years. In order to improve understanding of the surfaces responsible for the dust emissions, we investigated the critical wind friction velocity thresholds and the dust emissivities of surfaces representing areas typical of Lordsburg Playa's stream deltas, shorelines, and ephemerally flooded lakebed using a Portable In-Situ Wind ERosion Laboratory (PI-SWERL). Mean threshold friction velocities for PM 10 entrainment ranged from less than 0.30 m s − 1 for areas in the delta and shoreline to greater than 0.55 m s − 1 for ephemerally flooded areas of the lakebed. Similarly, we quantified mean PM 10 vertical flux rates ranging from less than 500 μg m − 2 s − 1 for ephemerally flooded areas of lakebed to nearly 25,000 μg m − 2 s − 1 for disturbed delta surfaces. The unlimited PM 10 supply of the relatively coarse sediments along the western shoreline is problematic and indicates that this may be the source area for longer-term visibility reducing dust events and should be a focus area for dust mitigation efforts.
