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Atmos. Chem. Phys., 24, 9155–9176, 2024
https://doi.org/10.5194/acp-24-9155-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
Characterization of the particle size distribution,
mineralogy, and Fe mode of occurrence of dust-emitting
sediments from the Mojave Desert, California, USA
Adolfo González-Romero
1,2,3
, Cristina González-Flórez
1,3
, Agnesh Panta
4
, Jesús Yus-Díez
5
,
Patricia Córdoba
2
, Andres Alastuey
2
, Natalia Moreno
2
, Melani Hernández-Chiriboga
1
,
Konrad Kandler
4
, Martina Klose
6
, Roger N. Clark
7
, Bethany L. Ehlmann
8
, Rebecca N. Greenberger
8
,
Abigail M. Keebler
8
, Phil Brodrick
9
, Robert Green
9
, Paul Ginoux
10
, Xavier Querol
2
, and
Carlos Pérez García-Pando
1,11
1
Barcelona Supercomputing Center (BSC), Barcelona, Spain
2
Institute of Environmental Assessment and Water Research (IDAEA-CSIC),
Spanish Research Council, Barcelona, Spain
3
Polytechnical University of Catalonia (UPC), Barcelona, Spain
4
Institute of Applied Geosciences, Technical University Darmstadt, Darmstadt, Germany
5
Centre for Atmospheric Research, University of Nova Gorica, Ajdovš
ˇ
cina, Slovenia
6
Institute of Meteorology and Climate Research Troposphere Research (IMKTRO), Karlsruhe Institute of
Technology (KIT), Karlsruhe, Germany
7
PSI Planetary Science Institute, Tucson, AZ, USA
8
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
9
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
10
NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
11
Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
Correspondence:
Adolfo González-Romero (agonzal3@bsc.es) and Xavier Querol
(xavier.querol@idaea.csic.es)
Received: 14 February 2024 – Discussion started: 27 February 2024
Revised: 6 June 2024 – Accepted: 10 June 2024 – Published: 22 August 2024
Abstract.
Constraining dust models to understand and quantify the effect of dust upon climate and ecosystems
requires comprehensive analyses of the physiochemical properties of dust-emitting sediments in arid regions.
Building upon previous studies in the Moroccan Sahara and Iceland, we analyse a diverse set of crusts and ae-
olian ripples (
n
=
55) from various potential dust-emitting basins within the Mojave Desert, California, USA.
Our focus is on characterizing the particle size distribution (PSD), mineralogy, aggregation/cohesion state, and
Fe mode of occurrence. Our results show differences in fully and minimally dispersed PSDs, with crusts ex-
hibiting average median diameters of 92 and 37 μm, respectively, compared to aeolian ripples with 226 and
213 μm, respectively. Mineralogical analyses unveiled strong variations between crusts and ripples, with crusts
being enriched in phyllosilicates (24 % vs. 7.8 %), carbonates (6.6 % vs. 1.1 %), Na salts (7.3 % vs. 1.1 %), and
zeolites (1.2 % and 0.12 %) and ripples being enriched in feldspars (48 % vs. 37 %), quartz (32 % vs. 16 %), and
gypsum (4.7 % vs. 3.1 %). The size fractions from crust sediments display a homogeneous mineralogy, whereas
those of aeolian ripples display more heterogeneity, mostly due to different particle aggregation. Bulk Fe con-
tent analyses indicate higher concentrations in crusts (3.0
±
1.3 wt %) compared to ripples (1.9
±
1.1 wt %), with
similar proportions in their Fe mode of occurrence: nano-sized Fe oxides and readily exchangeable Fe represent
1
.
6 %, hematite and goethite
15 %, magnetite/maghemite
2
.
0 %, and structural Fe in silicates
80 % of
the total Fe. We identified segregation patterns in the PSD and mineralogy differences in Na salt content within
Published by Copernicus Publications on behalf of the European Geosciences Union.
9156
A. González-Romero et al.: Characterization of sediments from the Mojave Desert
the Mojave basins, which can be explained by sediment transportation dynamics and precipitates due to ground-
water table fluctuations described in previous studies in the region. Mojave Desert crusts show similarities with
previously sampled crusts in the Moroccan Sahara in terms of the PSD and readily exchangeable Fe yet exhibit
substantial differences in mineralogical composition, which should significantly influence the characteristic of
the emitted dust particles.
1 Introduction
Desert dust produced by the wind erosion of arid and semi-
arid surfaces has significant effects on climate, ecosystems,
and health (Weaver et al., 2002; Goudie and Middleton,
2006; Sullivan et al., 2007; Crumeyrolle et al., 2008; De
Longueville et al., 2010; Karanasiou et al., 2012; Pérez
García-Pando et al., 2014; among others). Dust affects en-
ergy and water cycles through its absorption and scatter-
ing of both shortwave (SW) and longwave (LW) radiation
(Perez et al., 2006; Miller et al., 2014) and exerts influence
on cloud formation, precipitation patterns, and the associ-
ated indirect radiative forcing by serving as nuclei for liq-
uid and ice clouds (e.g. Harrison et al., 2019). Dust also un-
dergoes heterogeneous chemical reactions in the atmosphere
that enhance particles’ hygroscopicity and modify their opti-
cal properties (Bauer and Koch, 2005), and when deposited
into ocean waters, its bioavailable iron content acts as a cat-
alyst for photosynthesis by ocean phytoplankton, thereby in-
creasing carbon dioxide uptake and influencing the global
carbon cycle (Jickells et al., 2005). Dust primarily originates
from arid inland basins, which include various sedimentary
environments such as aeolian deposits, endorheic depres-
sions, and fluvial- and alluvial-dominated systems (Bullard
et al., 2011). Wind typically mobilizes loose sand from adja-
cent ripples or dunes, which then erodes more consolidated
surfaces, typically paved sediments and crusts, to release dust
(Stout and Lee, 2003; Shao et al., 2011). Atmospheric dust
emission models have improved by identifying preferential
dust sources using criteria like topography and hydrology
(Ginoux et al., 2001). However, these models still struggle
with capturing small-scale variability, partly due to the lack
of relevant soil measurements in arid regions, despite ad-
vancements in understanding the geomorphological and sed-
imentological factors influencing dust emissions (Bullard et
al., 2011). For instance, the particle size distribution (PSD)
and cohesion of the sediments affect saltation bombardment
and aggregate disintegration processes involved in dust emis-
sion (Shao et al., 1993).
Understanding the mineral composition of dust is also cru-
cial for assessing its climate impact. Dust contains various
minerals such as quartz, clay minerals, feldspars, carbonates,
salts, and iron oxides. The climate effects of dust are influ-
enced by these minerals’ relative abundances, sizes, shapes,
and mixing states. For example, iron oxides control solar
radiation absorption by dust (Formenti et al., 2014; Engel-
brecht et al., 2016; Di Biagio et al., 2019; Zubko et al., 2019),
nano-sized Fe oxides and easily exchangeable Fe increase
the fertilizing effect of dust in ocean and terrestrial ecosys-
tems (Hettiarachchi et al., 2019, 2020; Baldo et al., 2020),
K-feldspar and quartz impact ice nucleation in clouds (Atkin-
son et al., 2013; Harrison et al., 2019; Chatziparaschos et
al., 2023), and calcite influences acid reactions on dust sur-
faces (Paulot et al., 2016). The mineralogical composition of
dust can vary significantly across different regions due to ge-
ological and climatic factors (Claquin et al., 1999; Journet
et al., 2014). However, most models assume a globally uni-
form dust composition due to limited global data on parent
soil sources. Only a few models account for dust mineralogi-
cal composition variations (e.g. Scanza et al., 2015; Perlwitz
et al., 2015; Li et al., 2021; Gonçalves Ageitos et al., 2023;
Obiso et al., 2024) using global soil type atlases that are
based on the extrapolation of a limited number of soil analy-
ses (Claquin et al., 1999; Journet et al., 2014). These atlases
rely on assumptions about soil texture and colour and often
base their data on soil samples taken from depths deeper than
those relevant to wind erosion, and the method used to char-
acterize particle size and associated mineralogy fully breaks
down natural soil aggregates.
Since 2022, the EMIT mission has been acquiring com-
prehensive measurements of surface mineralogical compo-
sition for use in Earth system models (Green et al., 2020).
EMIT employs imaging spectroscopy across the visible to
short-wavelength infrared (VSWIR) spectral range from the
International Space Station to map the occurrence and esti-
mate the abundance of 10 key dust source minerals. Addi-
tionally, EMIT has the potential to estimate surface soil tex-
ture. While identifying dominant surface minerals has tradi-
tionally been a strength of spectrometers, quantifying these
minerals poses significant challenges. Factors such as min-
eral grain size and composition can affect spectral absorp-
tions, certain dominant materials like quartz and feldspar ex-
hibit minimal absorption features, and the presence of other
materials can further complicate the analysis.
Overall, there is a notable lack of comprehensive mea-
surements characterizing relevant properties of surface sedi-
ments in dust source regions. This gap hampers our ability to
evaluate and constrain mineral abundance derived from re-
flectance spectroscopy and to improve dust emission mod-
elling. Addressing this issue, the FRontiers in dust miner-
AloGical coMposition and its Effects upoN climaTe (FRAG-
MENT) project has, over recent years, conducted a series
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
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of coordinated and interdisciplinary field campaigns across
remote dust source regions. The project’s objectives in-
clude enhancing the understanding and quantification of dust
source properties, examining their relationship with emit-
ted dust characteristics, refining spaceborne spectroscopy re-
trievals of surface minerals (Green et al., 2020; Clark et
al., 2024), and improving the representation of dust min-
eralogy in Earth system models (Perlwitz et al., 2015; Li
et al., 2021; Gonçalves et al., 2023; Obiso et al., 2024).
The FRAGMENT field campaigns involved regional sedi-
ment sampling in several regions and detailed wind erosion
and dust emission measurements at selected sites. Studies
stemming from these activities include those by González-
Romero et al. (2023), González-Flórez et al. (2023), Panta et
al. (2023), and González-Romero et al. (2024). These cam-
paigns have been executed in various geographic locations,
such as the Moroccan Sahara (2019), Iceland (2021), the Mo-
jave Desert in the USA (2022), and Jordan (2022). Through
these efforts, the FRAGMENT project contributes to filling
critical knowledge gaps in dust source characterization.
Following our previous studies in the Moroccan Sahara
(González-Romero et al., 2023) and Iceland (González-
Romero et al., 2024), this study focuses on the character-
ization of dust-emitting sediments collected from the Mo-
jave Desert in 2022. The Mojave Desert is a closed-basin
wedge-shaped region located in the southwestern USA, be-
tween California and Nevada. The region is surrounded by
mountain ranges and traversed by the Mojave River and other
intermittent rivers for over 200 km from the San Bernardino
Mountains to the east (Dibblee, 1967; Reheis et al., 2012).
Despite its limited global importance (dust emission from
North America represents only
3 % of global dust emis-
sion; Kok et al., 2021), the Mojave Desert is an important re-
gional dust source (Ginoux et al., 2012), with most emission
occurring in the playa lakes and alluvium deposits near playa
lakes (Reheis and Kihl, 1995; Reheis et al., 2009; Urban et
al., 2018). Reynolds et al. (2009) observed 71 d with dust
plumes during 37 months of camera recording at the Franklin
Lake playa. According to remote sensing data (MODIS),
aerosol optical depth (AOD) is higher in spring and sum-
mer and reaches a minimum in winter (Frank et al., 2007).
However, from November to May, eastward flows of the jet
stream affect the Mojave Desert, which, in combination with
topography, favour the development of northerly winds that
can lead to dust emission (Urban et al., 2009). Up to 65 % of
emission in the Mojave Desert is estimated to be due to nat-
ural factors, whereas 35 % is due to anthropogenic activities,
including off-road recreation practices, mine operations, and
military training and livestock grazing (Frank et al., 2007).
The AOD in this region is also affected by dust transported
from other regions (Tong et al., 2012) and pollution trans-
ported from the Los Angeles Basin (Frank et al., 2007; Ur-
ban et al., 2009). In the Mojave Desert, Reynolds et al. (2009)
noted an association between wet periods and dust emission,
directly related to the generation of new, thin crusts and salt
crust removal.
The Mojave Desert includes several significant playa
lakes, such as Rogers, Rosemond, Owens Lake, Death Valley
(Badwater), Panamint Valley, Bristol, Cadiz, Danby, Sear-
les Lake, Soda Lake, and Mesquite Lake (Reheis and Kihl,
1995; Reheis, 1997; Potter and Coppernoll-Houston, 2019).
Reynolds et al. (2007, 2009) distinguished between two types
of playa lakes, wet playas, influenced by groundwater, and
dry playas, unaffected by groundwater, though both can ex-
perience surface-water runoff. Goudie (2018) further delin-
eated wet playas as having a groundwater table within 5 m of
the surface, while dry playas have a groundwater table deeper
than 5 m. Additionally, Goudie (2018), Buck et al. (2011),
Nield et al. (2015), and Nield et al. (2016b) observed that
the interaction between salt minerals and the groundwater
table on wet playas leads to the formation of fluffy surfaces
through salt reworking by water during evapotranspiration.
In the Mojave Desert, three different Aridisols are present
in the Rand Mountains’ alluvial fan, corresponding to xeric
soils or Aridisols according to Eghbal and Southard (1993),
which are typical in arid and semi-arid regions, with low or-
ganic matter content and low structures. The uppermost layer
of those Aridisols, ranging from 0 to 1 cm in depth, exhibited
a texture of 15 %–30 % gravel, 69 %–74 % sand, and 10 %–
11 % clay. Reheis et al. (1995) described soils (
<
2 mm)
primarily composed of silt (30 %–70 %) and clay (20 %–
45 %). The mineralogy of those samples was dominated by
quartz, feldspars, amphiboles, and clay minerals, including
smectite, mica, and kaolinite (Eghbal and Southard, 1993).
The Cronese lakes and Soda Lake playas are documented
to contain salt precipitates, but mineralogy is not speci-
fied. Mesquite Lake playa is noted for its gypsum deposits
(Reynolds et al., 2009). At Franklin Lake playa, surfaces
are characterized by silt- and clay-sized particles (Goldstein
et al., 2017), with mineralogical descriptions provided in
Reynolds et al. (2009) indicating fluffy surfaces comprised
of halite, thenardite, trona, burkeite, calcite, illite, smectite,
and kaolinite. Similar mineralogical results are described at
Soda Lake by Reheis et al. (2009), with a higher proportion
of Na salts, quartz, gypsum, and carbonates. Furthermore,
Goldstein et al. (2017) identified a diverse array of minerals
at Franklin Lake playa, including clays; zeolites; plagioclase;
K-feldspar; quartz; calcite; dolomite; and salt minerals such
as trona, halite, burkeite, and thenardite.
This study characterizes the particle size distribution, min-
eralogy, and modes of occurrence of Fe of selected potential
dust-emitting sediment surfaces from the Mojave Desert. In
addition, the mineralogy of different size fractions is anal-
ysed, based on a sieving protocol that minimally disturbs
sediments. We further discuss the potential effect of sedimen-
tary transport on the particle size and mineralogy across the
sampled basins, building upon previous studies in the litera-
ture. Finally, our results are broadly compared with current
EMIT standard (semi-quantitative) products and with those
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Atmos. Chem. Phys., 24, 9155–9176, 2024
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
obtained using similar protocols in previous FRAGMENT
campaigns in other regions (González-Romero et al., 2023,
2024).
2 Methodology
2.1 Study area
The Mojave Desert, located between California and Nevada,
has a diverse geological history spanning from the Cambrian
and Precambrian eras to the Holocene (Fig. 1). This geolog-
ical complexity encompasses volcanic, plutonic, metamor-
phic, and sedimentary units (Jennings et al., 1962; Miller et
al., 2014). In areas once submerged during the Last Glacial
Maximum (LGM), we now find ephemeral playa lakes that
have existed for thousands of years since the LGM, offering a
glimpse into the region’s dynamic past (Miller et al., 2018).
These playa lakes, together with alluvial fans, floodplains,
and other features, are surrounded by a variety of source
rocks, exhibit diverse particle sizes and compositions, and
can potentially emit dust under favourable wind conditions.
The regional distribution of the annual frequency of oc-
currence (FoO) of dust events with dust optical depth ex-
ceeding 0.1, derived from MODIS Deep Blue C6.1 Level
2 data following the methodology of Ginoux et al. (2012),
is illustrated in Fig. 2. The FoO provides an overall esti-
mate of dust emission frequency above a certain threshold
at a resolution of 0.1°
×
0.1° over the region. Sediment sam-
ples were collected from various locations within the Mojave
Desert region, including areas with relatively high FoO (see
locations in Figs. 1 and 2). Among these locations is Soda
Lake and its surroundings, near Baker, CA, which is linked
to Silver Lake to the north and is surrounded by igneous,
volcanic, and carbonate rocks, as well as dune fields, to the
south (Fig. 1). The area is influenced by aeolian, alluvial, and
fluvial processes and experiences annual precipitation of 80–
100 mm (Urban et al., 2018). This ephemeral lake contains
salts resulting from the evaporation of groundwater sourced
from an aquifer nestled in the Zzyzx Mountains (Honke et
al., 2019). Dust emissions are a recurrent phenomenon, orig-
inating from fine sediments accumulated in the lake’s central
areas during sporadic flooding, from the white evaporite sur-
faces in the lake, and from the alluvial deposits to the south
of the playa lake (Urban et al., 2018). According to the FoO,
the areas with higher dust emissions are the southern part of
the lake and the alluvial deposits to the southwest, extending
up to Afton Canyon.
Samples were also collected from the Cronese lakes,
Mesquite Lake, Ivanpah Lake, and Coyote Lake (Fig. 1),
which lie in areas with significant FoO signals (Fig. 2) and
have been documented as dust sources in Reheis and Kihl
(1995) and Reheis et al. (2009). The Cronese lakes are adja-
cent to the Soda Lake area to the west, sharing a similar geo-
logic context (Figs. 1 and 2). Mesquite Lake, located on the
border between California and Nevada, is encircled by car-
bonate and igneous rocks, mirroring the geological setting
of the nearby Ivanpah Lake. Notably, Mesquite Lake playa
is the only playa affected by a gypsum mine pit, as docu-
mented by Reynolds et al. (2009). Further contributing to the
diversity of the region’s geological makeup is Coyote Lake,
flanked by Miocene and Pleistocene sediments. These playa
lakes, characterized as endorheic ephemeral lakes, receive
groundwater inputs in some cases, enriching the lakes with
salts that subsequently precipitate on the surfaces of their
central regions (Whitney et al., 2015; Urban et al., 2018).
Other areas with relatively high FoO not sampled in our
study include the Ashford Junction alluvial deposits and the
Fort Irwin area, where the northern valley, including Nelson
Lake, may be more prone to dust emission due to signifi-
cant anthropogenic disturbance. It is important to note that
the FoO may tend to highlight areas such as playas and their
surroundings, where in some cases the most dust per unit
area could be produced (Floyd and Gill, 2011; Baddock et
al., 2016). However, some alluvial regions with lower emis-
sion rates not surpassing the FoO threshold may produce
more dust overall due to their greater areal extent (Reheis and
Kihl, 1995; Baddock et al., 2016). Additionally, many other
types of dust-producing surfaces active in the Mojave Desert,
such as gravel roads, agricultural lands, and recreational off-
road tracks, are rarely observed by satellite retrievals (Urban
et al., 2018).
The new EMIT sensor on board the International Space
Station offers a glimpse of the mineralogical diversity in the
Mojave Desert (Green et al., 2020). Figure 3 displays stan-
dard Tetracorder RGB colour composite semi-quantitative
products for EMIT. Tetracorder is a software system that em-
ploys a set of algorithms within an expert system decision-
making framework to identify and map compounds (Clark,
2024; Clark et al., 2024). Figure 3 shows a true colour image,
along with standard products for mineral electronic absorp-
tion bearing Fe
2
+
and Fe
3
+
(including hematite and goethite)
in the visible to very near infrared spectral range. It also
displays standard products for the EMIT-targeted minerals,
excluding hematite and goethite: calcite, chlorite/serpentine,
dolomite, gypsum, illite/muscovite, kaolinite-dioctahedral
group, montmorillonite group, and vermiculite. These prod-
ucts highlight areas where the presence of each mineral or
component is significant, measured in terms of band depth
fit, where the fit represents the least squares correlation coef-
ficient from a feature fit of observed and reference library
spectra. These analyses reveal the widespread presence of
phyllosilicates such as kaolinite, smectite, montmorillonite,
and illite across the area. The northeastern sector, particularly
around Mesquite Lake, exhibits notable concentrations of
carbonates and gypsum. Additionally, goethite and hematite
are detected, with a more pronounced presence of goethite in
the northern portion and hematite in the southern part of the
region. The detection of mixtures of Fe
2
+
and Fe
3
+
within
various minerals enriches our understanding of the region’s
mineralogical diversity.
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
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Figure 1.
Study area map including the playa lakes studied together with a geologic map, simplified from Jennings et al. (1962) and Miller
et al. (2014). The star represents the Zzyzx complex and green dots the samples used in this study. Basemap: imagery data from © Google
Earth Pro v: 7.3.6.9345.
Quantitative surface mineralogy (mineral mass abun-
dances of the 10 EMIT-targeted minerals) and soil texture
products are currently being developed by the EMIT team
for use in Earth system models. Their publication and evalu-
ation will be the focus of forthcoming publications. Thus, it is
beyond the scope of this study to perform a detailed quantita-
tive comparison between our analyses and comparable EMIT
products. However, in the Results section, we broadly com-
pare these standard products with the results of our in situ
analyses.
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
Figure 2.
Map of frequency of occurrence (FoO) of dust optical depth (DOD)
>
0
.
1 over the study region derived from MODIS C6.1 Aqua
(13:30 LT equatorial passing time) Level 2 Deep Blue aerosol products at 0.1° resolution. A dust occurrence is counted when DOD
>
0.1,
Ångström exponent
<
0.3, and DOD at 412 nm
>
DOD at 470 nm. Blue iso-contours represent 5 % and 10 % of daily occurrences per year
averaged over 20 years (2003–2022). Green dots represent the samples collected and used in this study. Basemap: imagery data from ©
Google Earth Pro v: 7.3.6.9345.
2.2 Sampling
Representative surfaces of dust-emitting sediments were
sampled in May 2022, with depths of up to 3 cm, using a
5 cm
2
inox shovel. Samples were stored in a plastic bag; la-
belled; documented with photographs, descriptions, and co-
ordinates; and transported to the laboratories for subsequent
analyses. The type of samples considered is crusts (semi-
cohesive fine sediments accumulated during flooding in de-
pressions) and ripples (aeolian ripples that are built up under
favourable winds and supply sand for saltation) (Fig. 4). A to-
tal of 55 surface sediments and ripples (32 from Soda Lake,
9 from Mesquite Lake, 1 from Ivanpah Lake, 11 from the
Cronese lakes, and 2 from Coyote Lake) were sampled for
laboratory analysis. Once in the laboratory, the samples were
dried for 24–48 h at 40–50 °C and sieved to pass through a
2 mm mesh.
Our rationale for selecting crusts and ripples is twofold.
On the one side, dust emission is primarily driven by two
mechanisms: saltation bombardment and aggregate disinte-
gration. In saltation bombardment, dust is ejected from soil
aggregates (typically crusts and paved sediments rich in clay
and silt particles) when impacted by saltating sand parti-
cles. In aggregate disintegration, dust is released from saltat-
ing soil aggregates (Shao et al., 1993; Alfaro et al., 1997;
Shao, 2001). By characterizing the PSD (both dry- and wet-
sieved) and mineralogy of ripples (concentrating sand par-
ticles) and crusts (concentrating clay and silt particles), we
provide comprehensive and valuable information for devel-
oping and refining dust emission models. On the other side,
in arid regions, quartz and feldspar typically dominate sed-
iment mass. However, current spaceborne hyperspectral in-
struments (such as EMIT) cannot directly identify feldspar
and quartz because their absorption features lie outside the
instrument’s spectral range. This poses a significant chal-
lenge in quantifying surface mineral abundances from re-
mote spectroscopy. At all FRAGMENT sampling locations
(Morocco, Iceland, Mojave in the USA, and Jordan), we
measured reflectance spectra using an ASD FieldSpec3. By
characterizing and contrasting ripples (with high quartz and
feldspar content and larger particle sizes) and crusts, we aim
to provide information to enhance understanding and im-
prove modelling assumptions for estimating surface mineral
abundances and soil texture from remote spectroscopy in
subsequent studies.
We acknowledge that the limited number of samples col-
lected may not fully represent the potential variability among
crusts and ripples within the studied locations due to vary-
ing conditions (Buck et al., 2011). However, our samples
broadly represent the composition and particle size distribu-
tions (PSDs) of this type of sediment in these areas, allowing
for meaningful comparisons with sediments from other loca-
tions.
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
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Figure 3.
EMIT scenes emit20231015T215209_color-visRGB and emit20230728T214142_color-visRGB at 60 m px
1
showing the diver-
sity of Fe
2
+
, Fe
3
+
, and minerals bearing Fe
2
+
and Fe
3
+
and the carbonate, salt, and phyllosilicate minerals.
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
Figure 4.
Examples of samples collected in the Mojave Desert in-
cluding crusts
(a)
; aeolian ripples
(b)
; a massive compact crust
(c)
;
and a salty, spongy crust
(d)
.
2.3 Analyses
2.3.1 Particle size distribution
Particle size distributions (PSDs) of bulk samples (
<
2 mm)
were analysed as described in González-Romero et al. (2023)
for the evaluation of the aggregation state. First, we con-
ducted a minimally dispersed PSD (MDPSD) analysis,
which minimizes the breaking of the aggregates that are
encountered in natural conditions. Second, we conducted a
fully dispersed PSD (FDPSD) analysis, which breaks the ag-
gregates. Wet dispersion was done according to Sperazza et
al. (2004), using water and sodium hexametaphosphate dis-
persion for 24 h. Both PSDs (MDPSD and FDPSD) were ob-
tained by a laser diffractometer with the Malvern Mastersizer
2000 Hydro G and Scirocco for the fully and minimally dis-
persed conditions, respectively. We note that under wet dis-
persion, at least some salt minerals may dissolve.
In addition, we separated 20 selected samples from differ-
ent sources, including 16 crusts and 4 aeolian ripples, into
different size ranges to understand how mineral composi-
tion changes with size. We used a series of sieves with mesh
sizes of 2 mm, 1 mm, 500 μm, 250 μm, 80 μm, 63 μm, 40 μm,
and 20 μm. The sieving process involved hand-shaking the
full column for 1 min, followed by ultrasound sonication for
1 min for the 500, 80, 40, and 20 μm size fractions. This
method ensured the effective separation of the size fractions
for subsequent mineralogical analysis.
2.3.2 Mineralogical composition
To quantify the different contents of crystalline minerals and
amorphous components, X-Ray diffraction (XRD), coupled
with a Rietveld quantitative method, was used (Rietveld,
1969; Cheary and Coelho, 1992; Young, 1993; TOPAS,
2018). Adding a known amount of an internal standard ma-
terial allowed, via the Rietveld method, the quantification of
a mixture of minerals and any non-crystalline material in the
mixture not included in the Rietveld method (De la Torre et
al., 2001; Madsen et al., 2001; Scarlett and Madsen, 2006;
Machiels et al., 2010; Ibañez et al., 2013). For the analy-
sis, a measured amount of dry-ground sample was mixed and
dry-ground again with 10 %–20 % of fluorite (CaF
2
powder,
Merck), used here as an internal standard for quantitative pur-
poses. The XRD patterns of the samples were analysed by a
Bruker D8 A25 ADVANCE powder X-ray diffractometer op-
erated at 40 kV and 40 mA with monochromatic Cu K
α
radi-
ation (
=
1.5405 Å). This device uses a Bragg–Brentano ge-
ometry and a LynxEye 1D sensitive detector. Diffractograms
were recorded from 4 to 120° of 2
θ
and steps of 0.015° in
1 s and maintained rotation (15 min
1
). For the clay iden-
tification, samples were analysed using the oriented aggre-
gate method by XRD, decanting clay fractions from sam-
ples and smearing the slurries in glass slides. After, three
treatments were applied including air drying (AO), glyco-
lation with ethylene glycol (AG), and heating at 550 °C for
2 h (AC) with its three different diffractograms. Finally, the
three diffractograms allow us to corroborate the presence
of illite, chlorite, palygorskite, and montmorillonite through
Thorez (1976) and USGS (2024) procedures. Data collected
were evaluated using the Bruker AXS DIFFRAC.EVA soft-
ware package (Bruker AXS, Karlsruhe, Germany, 2000), and
the Rietveld analyses were performed with the TOPAS 4.2
program (Bruker AXS, 2003–2009). A Chebyshev function
of level 5 was used to fit the background, and abundances
of crystalline and amorphous phases were normalized to
100 %. Fits were evaluated by visual comparison, i.e.
R
wp
(
R
-weighted pattern),
R
exp
(
R
-expected), and goodness of fit
(GOF).
2.3.3 Mode of occurrence of Fe
As XRD is not precise enough for Fe-oxide quantification,
wet chemistry and sequential extractions of Fe are needed for
quantification of the mode of occurrence of Fe (González-
Romero et al., 2023, 2024). Samples were analysed with a
two-step acid digestion for the total Fe (FeT) content follow-
ing Querol (1993, 1997). A reference material (NIST-1633b,
coal fly ash) was used for quality control in every batch. The
sequential extraction presented in Shi et al. (2009), Baldo et
al. (2020), and González-Romero et al. (2024) was used to
quantify readily exchangeable Fe ions and nano-sized Fe ox-
ides (FeA), the amount of crystalline Fe oxides as goethite
and hematite (FeD), and crystalline magnetite (FeM). For
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
9163
the first extraction, 30 mg samples were leached with 10 mL
of an ascorbate solution (extractant solution) and shaken in
dark conditions for 24 h and filtered. Another 30 mg was
leached with 10 mL of a dithionite solution (extractant so-
lution), shaken for 2 h in dark conditions, and filtered for the
second extraction. The solid residue was then leached again
in 10 mL of an oxalate solution for 6 h in dark conditions
and filtered for the third extraction. The extracted solution of
each phase (FeT, FeA, FeD, and FeM) was analysed to quan-
tify dissolved Fe by inductively coupled plasma atomic emis-
sion spectrometry (ICP-AES). FeA is obtained with the first
extraction, and FeD is obtained by subtracting the amount
of Fe from the first extraction from the second extraction.
Finally, the FeM is related to the third extraction. At the
end, the equivalent to the Fe as structural Fe was obtained,
FeS
=
FeT
FeA
FeD
FeM, which is included in other
minerals and amorphous phases. To test accuracy, 30 mg of
Arizona Test Dust (ATD; ISO 12103-1, A1 Ultrafine Test
Dust; Powder Technology Inc.) was subjected to the same
extraction procedure in every batch and extraction.
The average Fe content of the reference material 1633b
was 7.6
±
0.5 % (certified 7.8 %). Furthermore, the aver-
age values of the sequential Fe extraction of the ATD ref-
erence material were 0.073
±
0.012 %, 0.47
±
0.01 %, and
0.042
±
0.002 % for FeA, FeA
+
FeD, and FeM, respec-
tively, while the certified contents are 0.067 %, 0.48 %, and
0.047 %, respectively.
3 Results
3.1 Particle size distribution
The PSD and median particle diameter of fully and min-
imally disturbed samples are key parameters for under-
standing the cohesion and aggregation state of sediments
(González-Romero et al., 2024). We note that in the Mojave
Desert, some basins are enriched in salts, which can cause
some artefacts in the FDPSD. The dissolution of salts during
wet dispersion for bulk PSD analysis (
<
2 mm) can remove
aggregating agents. This salt cementation of the crusts might
reduce the dust emission potential of the surface.
The average PSDs of crusts across different basins exhibit
remarkable similarity, yet disparities between FDPSDs and
MDPSDs are pronounced, indicating varying degrees of par-
ticle cohesion and aggregation at Cronese, Mesquite, Ivan-
pah, and Coyote lakes. In these locations, FDPSDs feature
a dominant mode at 8–10 μm alongside a coarser mode at
100 μm, while MDPSDs are characterized by a dominant
coarser mode (Fig. 5). In contrast, Soda Lake crusts exhibit
similarity between FDPSDs and MDPSDs. Average FDPSDs
and MDPSDs of aeolian ripples from the Mojave Desert are
found to be similar, typically featuring a major size mode
between 100 and 300 μm. However, distinctions arise when
analysing specific lakes. Aeolian ripples from Soda, Cronese,
and Coyote lakes showcase a dominant coarse mode at 200–
300 μm, whereas those from Mesquite Lake show a dominant
mode at a finer scale, approximately at 100 μm (Fig. 5).
The crusts’ means of all median particle diameters (mean
median) in the analysed Mojave Desert dust source sedi-
ments reveal a coarser MDPSD compared to the FDPSD,
with values of 92 and 37 μm, respectively. In contrast, the
mean median particle diameter is similar for aeolian rip-
ples (226 and 213 μm, respectively) (Table S1 in the Supple-
ment). Analysing specific locations, the mean median par-
ticle diameter from the MDPSD of crusts varies, with the
finest crust observed at Ivanpah Lake (35 μm) and the coars-
est at Mesquite Lake (141 μm). For the FDPSD, the finest
crust originates from Coyote Lake (8.4 μm), while the coars-
est is from Soda Lake (52 μm) (Table S1). Similarly, for aeo-
lian ripples, the mean median particle diameters for both the
MDPSD and FDPSD are finer at Mesquite Lake (167 and
67 μm, respectively) and coarser at Cronese lakes (264 and
234 μm, respectively) (Table S1). The high degree of parti-
cle aggregation observed in crusts, contrasting with the lower
aggregation state in ripples, aligns with findings reported for
dust-emitting sediments from Morocco by González-Romero
et al. (2023).
The mean median particle diameters of crusts collected
in the Mojave Desert are similar to those from Morocco
described by González-Romero et al. (2023). Specifically,
the mean median MDPSD diameter for the Mojave Desert
(92
±
74 μm) closely resembles that of the Lower Draâ basin
in Morocco (113
±
79 μm), albeit slightly finer, and is no-
tably coarser than that of Iceland (55
±
62 μm) (González-
Romero et al., 2023, 2024). Furthermore, the finest crust
sampled in the Mojave Desert (Ivanpah with 35 μm) is al-
most twice as coarse as the finest from Morocco (L’Bour
with 20 μm). For the FDPSD, the Icelandic top sediment sur-
face is the coarsest (56
±
69 μm), followed by both Morocco
and Mojave crusts (37
±
77 and 37
±
48 μm, respectively).
Additionally, average MDPSD median diameters of aeolian
ripples from the Mojave Desert source samples closely re-
semble those from Morocco (226 and 221 μm, respectively),
while those from Iceland are slightly coarser (280 μm).
Dry-sieved size fractions of dust-emitting sediments show
the highest percentage of mass in the 250–500 and 80–
250 μm fractions, with minimal mass within 500–1000 μm,
1–2 mm, and the finer fractions (20–40 and
<
20 μm) (Fig. 6,
Table S2). In both cases, the size fractions from 80 to 500 μm
accumulated a total of 75 % to 90 % of the total mass fraction
(Table S2).
Close to the centre of the Soda Lake, where numerous
crust samples were collected, before reaching massive crust
cementation by evaporite minerals, the FDPSD median di-
ameter reaches very fine sizes (8–15 μm) (Fig. S1 in the Sup-
plement). In contrast, towards the edges of the basin (closer
to the mountains surrounding this endorheic lake), the size
markedly increases, ranging from 22 to 87 μm (Fig. S1).
Similar patterns, yet with coarser sizes, are observed for the
MDPSD. As described in previous studies, the fluctuation of
https://doi.org/10.5194/acp-24-9155-2024
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9164
A. González-Romero et al.: Characterization of sediments from the Mojave Desert
Figure 5.
Fully dispersed particle size distribution (FDPSD) and minimally dispersed particle size distribution (MDPSD) for crusts and
aeolian ripples from the Mojave Desert (median PSD from all the samples) and Soda, Mesquite, Cronese, Ivanpah, and Coyote lakes. In
purple and pink shading, the standard deviation of each PSD is shown (no. of samples used in Table 1), except for Ivanpah and Coyote lakes
(only one sample each).
the groundwater table in the centre of the basin can lead to
a massive precipitation of salts, resulting in the formation
of compact crusts (Fig. 4) (Reynolds et al., 2007; Nield et
al., 2016a, b; Urban et al., 2018) that should effectively re-
duce dust emission. However, at the edges, where the precip-
itation of salts is less frequent and reworking of the crusts
by fluctuations in the groundwater table occurs, salty and
spongy crusts are formed (Fig. 4) (Nield et al., 2016a, b).
These spongy crusts, being less compact, are more easily
broken by saltating particles, potentially leading to high-salt
dust emissions.
The slight particle size segregation, with finer particles ac-
cumulating towards the centre of the lake, can be attributed
to the transport of sediments from the surrounding mountains
to the lake’s centre by runoff waters during rain episodes.
Initially, the coarser particles are deposited, followed by the
finer particles that remain suspended in the water for a longer
duration. Nevertheless, the crusts in the surroundings alluvial
fans of the Soda Lake are fine enough (22–87 μm in the edges
compared to 8–15 μm in the centre; Fig. S1) and surrounded
by dunes (availability of saltators for saltation bombardment)
to have a high potential dust emission under favourable con-
ditions (Reynolds et al., 2006; Reheis et al., 2009; Urban et
al., 2018).
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
9165
Table 1.
Full range (
<
2000 μm),
<
63 μm, and
>
63 to 2000 μm mean diameter and standard deviation (SD), min, and max for minimally
dispersed particle size distribution (MDPSD) and fully dispersed particle size distribution (FDPSD). NaN: not a number.
Surface
Location
N
MDPSD
Full range
63 μm
>
63 to 2000 μm
Mean of medians
±
SD [min, max]
Crusts
Mojave
35
92
±
74
[
10
,
349
]
22
±
6
.
4
[
11
,
34
]
254
±
71
[
155
,
489
]
Ripples
20
226
±
88
[
88
,
418
]
37
±
6
.
0
[
20
,
46
]
276
±
80
[
130
,
424
]
Crusts
Soda
17
63
±
47
[
10
,
156
]
21
±
6
.
5
[
11
,
31
]
234
±
82
[
155
,
489
]
Cronese
9
109
±
60
[
35
,
195
]
18
±
2
.
2
[
15
,
22
]
280
±
40
[
238
,
357
]
Mesquite
7
141
±
117
[
31
,
349
]
28
±
5
.
6
[
21
,
34
]
257
±
79
[
157
,
387
]
Ivanpah
1
35
±
NaN
[
35
,
35
]
16
±
NaN
[
16
,
16
]
314
±
NaN
[
314
,
314
]
Coyote
1
101
±
NaN
[
101
,
101
]
20
±
NaN
[
20
,
20
]
254
±
NaN
[
254
,
254
]
Ripples
Soda
15
231
±
87
[
88
,
418
]
39
±
3
.
5
[
29
,
43
]
275
±
77
[
130
,
424
]
Cronese
2
264
±
147
[
160
,
368
]
40
±
8
.
8
[
34
,
46
]
292
±
120
[
208
,
377
]
Mesquite
2
167
±
112
[
110
,
225
]
26
±
8
.
9
[
20
,
32
]
286
±
146
[
183
,
389
]
Ivanpah
0
NaN
NaN
NaN
Coyote
1
179
±
NaN
[
179
,
179
]
32
±
NaN
[
32
,
32
]
236
±
NaN
[
236
,
236
]
Surface
Location
N
FDPSD
Full range
63 μm
>
63 to 2000 μm
Mean of medians
±
SD [min, max]
Crusts
Mojave
35
37
±
48
[
4
.
9
,
240
]
18
±
6
.
6
[
8
.
4
,
35
]
306
±
237
[
106
,
1093
]
Ripples
20
213
±
92
[
28
,
362
]
29
±
8
.
3
[
15
,
48
]
335
±
99
[
213
,
561
]
Crusts
Soda
17
52
±
61
[
8
.
4
,
240
]
19
±
5
.
3
[
12
,
27
]
321
±
212
[
113
,
815
]
Cronese
9
17
±
23
[
4
.
9
,
77
]
12
±
3
.
1
[
8
.
4
,
19
]
381
±
345
[
144
,
1093
]
Mesquite
7
34
±
28
[
11
,
91
]
24
±
7
.
7
[
16
,
35
]
185
±
104
[
106
,
336
]
Ivanpah
1
12
±
NaN
[
21
,
21
]
15
±
NaN
[
15
,
15
]
347
±
NaN
[
347
,
347
]
Coyote
1
8
.
4
±
NaN
[
8
.
4
,
8
.
4
]
12
±
NaN
[
12
,
12
]
187
±
NaN
[
187
,
187
]
Ripples
Soda
15
234
±
82
[
92
,
362
]
31
±
7
.
9
[
21
,
48
]
346
±
97
[
238
,
561
]
Cronese
2
236
±
126
[
147
,
325
]
18
±
NaN
[
18
,
18
]
295
±
108
[
219
,
371
]
Mesquite
2
67
±
56
[
28
,
107
]
27
±
3
.
5
[
24
,
29
]
336
±
173
[
213
,
458
]
Ivanpah
0
NaN
NaN
NaN
Coyote
1
156
±
NaN
[
156
,
156
]
15
±
NaN
[
15
,
15
]
245
±
NaN
[
245
,
245
]
3.2 Mineralogy
Dust-emitting sediments from the Mojave Desert primar-
ily consist of feldspars (41
±
12 %, including albite/anor-
thite and microcline), quartz (22
±
11 %), and clay miner-
als (18
±
12 %, such as kaolinite, montmorillonite, and il-
lite). Additionally, minor contents of carbonate minerals
(6.6
±
6.6 %), amphiboles (pargasite) (4.1
±
1.5 %), and iron
oxides (maghemite/magnetite) (0.77
±
0.54 %) are observed
(Fig. 7, Tables 2 and S3). At Soda, Mesquite, and Cronese
lakes, Na salts such as halite, thenardite, trona, and burkeite
are also present, with an average salt content of 5.0
±
11 %.
Additionally, zeolites (0.77
±
1.1 % to 8.5 %) including lau-
montite and analcime are detected at Soda, Cronese, and
Coyote lakes (the southern sites), with the highest content
observed at Coyote Lake. High amounts of gypsum are found
at Mesquite Lake (15
±
29 %) (Fig. 7, Tables 2 and S3).
Moreover, Mesquite Lake crusts exhibit high contents of
dolomite and calcite (15
±
11 %) compared to other basins
(3.6
±
2.6 % to 7.2 %) (Table 2).
The overall mineral composition of the dust-emitting sedi-
ments originates primarily from the source rocks prevalent in
the region. These include dominant Mesozoic granitic rocks,
as well as pre-Tertiary, Tertiary, and Quaternary volcanic
rocks, and Pre-Cambrian and Mesozoic metamorphic rocks
(Fig. 1). In the northern, northeastern, and eastern areas of
Mesquite Lake, an important limestone and dolostone mas-
sif from the Palaeozoic era contributes notably to the high
content of calcite and dolomite in the sediments of this lake
(Fig. 1). Zeolite content in the sediments may be attributed
to the weathering of volcanic outcrops in the region or to
precipitation in alkaline lakes. This diverse bedrock min-
eralogy results in a wide variety of minerals in the dust-
https://doi.org/10.5194/acp-24-9155-2024
Atmos. Chem. Phys., 24, 9155–9176, 2024
9166
A. González-Romero et al.: Characterization of sediments from the Mojave Desert
Figure 6.
Percentage of mass fractions from the dry-sieved size fractions (250–500, 80–250, 63–80, 40–63, 20–40, and
<
20 μm). The range
of the enrichment factors of each mineral group for each dry size fraction of the 16 crust samples (blue) and of the 4 aeolian ripple samples
(red).
emitting sediments. The form of iron oxide detected in the
samples, identified via XRD, is maghemite. However, dis-
tinguishing between maghemite and magnetite using XRD
is challenging (Vandenberghe et al., 2000), and magnetite
has been found to be ubiquitous in Mojave dust (Reheis
et al., 2009; Reynolds et al., 2006). Therefore, we refer to
“maghemite/magnetite” to account for the potential pres-
ence of both minerals in the samples. In comparison to ae-
olian ripples, the average composition of the crusts shows
enrichment in clay minerals (24
±
11 % vs. 7.8
±
2.3 %
Atmos. Chem. Phys., 24, 9155–9176, 2024
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A. González-Romero et al.: Characterization of sediments from the Mojave Desert
9167
Figure 7.
Box plot showing average mineral contents for all samples, crusts, and aeolian ripples (wt %).
Table 2.
Average and standard deviations of the mineral contents (wt %) from crust and aeolian ripple samples from the Mojave Desert and
the different study basins. Maghemite denotes the potential presence of both maghemite and magnetite. NaN: not a number.
Clays
Carbonate
Salts
Zeolites
Maghemite
Quartz
Feldspars
Gypsum
Amphiboles
Crusts
24
±
11
6
.
6
±
6
.
6
7
.
3
±
13
1
.
2
±
1
.
9
0
.
92
±
0
.
59
16
±
7
.
2
37
±
9
.
7
3
.
1
±
14
4
.
1
±
1
.
5
Soda
22
±
11
3
.
6
±
2
.
6
8
.
9
±
17
0
.
77
±
1
.
1
0
.
97
±
0
.
66
18
±
7
.
7
40
±
6
.
7
0
.
29
±
0
.
68
4
.
5
±
1
.
6
Cronese
31
±
11
5
.
4
±
1
.
8
2
.
2
±
3
.
4
2
.
4
±
1
.
7
1
.
0
±
0
.
28
14
±
7
.
3
40
±
5
.
5
<
0
.
1
3
.
4
±
1
.
5
Coyote
28
7.2
1.2
8.5
0.48
11
37
<
0
.
1
5.6
Ivanpah
36
6.9
<
0
.
1
<
0
.
1
1.2
15
36
<
0
.
1
3.5
Mesquite
17
±
8
.
2
15
±
11
12
±
14
<
0
.
1
0
.
71
±
0
.
75
14
±
5
.
8
24
±
12
15
±
29
2
.
8
±
1
.
4
Ripples
7
.
8
±
2
.
3
1
.
1
±
2
.
2
1
.
1
±
3
.
7
0
.
12
±
0
.
52
0
.
49
±
0
.
28
32
±
9
.
5
48
±
13
4
.
7
±
20
4
.
1
±
1
.
6
Soda
7
.
4
±
1
.
8
0
.
47
±
0
.
73
0
.
19
±
0
.
46
<
0
.
1
0
.
49
±
0
.
25
35
±
4
.
5
52
±
4
.
7
<
0
.
1
4
.
3
±
1
.
5
Cronese
8
.
4
±
0
.
60
1
.
2
±
1
.
7
<
0
.
1
<
0
.
1
0
.
83
±
0
.
33
32
±
9
.
0
53
±
0
.
03
<
0
.
1
4
.
7
±
3
.
2
Coyote
7.9
2.3
<
0
.
1
2.3
0.60
28
52
<
0
.
1
3.5
Ivanpah
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
NaN
Mesquite
10
±
6
.
1
4
.
8
±
6
.
8
9
.
4
±
9
.
9
<
0
.
1
0
.
19
±
0
.
27
10
±
14
15
±
21
47
±
60
3
.
7
±
1
.
5
in crust and ripples, respectively), carbonates (6.6
±
6.6 %
vs. 1.1
±
2.2 %), Na salts (7.3
±
13 % vs. 1.1
±
3.7 %), ze-
olites (1.2
±
1.9 % vs.
0.12
±
0.52 %), and maghemite/-
magnetite (0.92
±
0.59 % vs. 0.49
±
0.28 %), while being
depleted in quartz (16
±
7.2 % vs. 32
±
9.5 %), feldspars
(37
±
9.7 % vs. 48
±
13 %), and gypsum (3.1
±
14 %
vs. 4.7
±
20 %) and showing a similar amphibole content
(4.1
±
1.5 % vs. 4.1
±
1.6 %) (Fig. 7, Tables 2 and S3). These
mineral enrichment and depletion trends in crusts are ob-
served in all the playa lakes, except for Mesquite Lake, which
is discussed below.
In Soda Lake, the concentration of Na salts in crusts in-
creases towards the inner part of the lake, ranging from 5 %–
10 % at the margins to 45 %–50 % in the centre, where com-
pact and fully salt-cemented crusts form. This phenomenon
is illustrated in Fig. 8, which presents a geological and min-
eralogical cross-section of Soda Lake. In addition to the wa-
ter transport to this central part of the basin during the rain
episodes, groundwater discharge from the Zzyzx Mountains
occurs. There, the groundwater table is close to the surface,
and its interaction with the surface causes the massive mobi-
lization of Na salts that consolidate the crusts (Fig. 4) (Nield
https://doi.org/10.5194/acp-24-9155-2024
Atmos. Chem. Phys., 24, 9155–9176, 2024