Mineral dust particles dominate aerosol mass in the atmosphere and directly modify Earth's radiative balance through absorption and scattering. This radiative forcing varies strongly with mineral composition, yet there is still limited knowledge on the mineralogy of atmospheric dust. In this study, we performed X-ray diffraction (XRD) and reflectance spectroscopy measurements on 37 different dust deposition samples collected as airfall in an urban setting to determine mineralogy and the relative proportions of minerals in the dust mixture. Most commonly, XRD has been used to characterize dust mineralogy; however, without prior special sample preparation, this technique is less effective for identifying poorly crystalline or amorphous phases. In addition to XRD measurements, we performed visible and short-wave infrared (VSWIR) reflectance spectroscopy for these natural dust samples as a complementary technique to determine mineralogy and mineral abundances. Reflectance spectra of dust particles are a function of a nonlinear combination of mineral abundances in the mixture. Therefore, we used a Hapke radiative transfer model along with a linear spectral mixing approach to derive relative mineral abundances from reflectance spectroscopy. We compared spectrally derived abundances with those determined semi-quantitatively from XRD. Our results demonstrate that total clay mineral abundances from XRD are correlated with those from reflectance spectroscopy and follow similar trends; however, XRD underpredicts the total amount of clay for many of the samples. On the other hand, calcite abundances are significantly underpredicted by SWIR compared to XRD. This is caused by the weakening of absorption features associated with the fine particle size of the samples, as well as the presence of dark non-mineral materials (e.g., asphalt) in these samples. Another possible explanation for abundance discrepancies between XRD and SWIR is related to the differing sensitivity of the two techniques (crystal structure vs. chemical bonds). Our results indicate that it is beneficial to use both XRD and reflectance spectroscopy to characterize airfall dust because the former technique is good at identifying and quantifying the SWIR-transparent minerals (e.g., quartz, albite, and microcline), while the latter technique is superior for determining abundances for clays and non-mineral components.
Mineral dust aerosols are lofted from the surface into the atmosphere, mainly in the arid regions of the world, either affecting the area nearby or traveling long distances causing global impacts (Goudie and Middleton, 2006). Suspended mineral particles affect air temperature by scattering and absorption of incoming sunlight and outgoing long-wave radiation (Miller and Tegen, 1998). Mineral dust–radiation interactions (e.g., absorption and scattering) directly modify Earth's radiative balance and energy budget, consequently contributing to climate change (Tegen and Lacis, 1996; Tegen et al., 1996). Past studies have discussed that dust particles' distinctive radiative forcing strongly depends on their particle size distribution (PSD) and mineral composition (Sokolik and Toon, 1999; Sokolik et al., 2001; Ginoux 2017). Atmospheric dust particles contain a diverse mix of minerals. Such dust is dominantly composed of quartz, carbonates, iron oxides, clays, sulfates, and feldspars (Engelbrecht et al., 2016, their Supplement Sects. S2.1 and S2.2). Therefore, the relative quantity of the various minerals defines the optical properties of these aerosols.
As a common approach, particulate matter deposited by airfall is collected at different geographic locations to determine mineralogical composition and abundance, as well as particle size distribution. Despite the fact that the physico-chemical properties of minerals have a substantial impact on dust-related radiative forcing, there is no ideal measurement technique for identifying these properties. To date, X-ray diffraction (XRD) has been frequently used in various research studies as a primary or complementary technique to measure the mineral content of dust particles (e.g., Caquineau et al., 1997; Kandler et al., 2009; Engelbrecht et al., 2009, 2016, 2017; Nowak et al., 2018). For example, Engelbrecht et al. (2017) performed XRD measurements on 27 dust samples collected from the Arabian Red Sea coast in order to obtain mineralogy and fractional abundances of minerals. In that study, they found that the dust samples were mainly dominated by quartz, feldspars, micas, clays, and halite and to a lesser extent by carbonates, iron oxides, and gypsum. While XRD is a powerful technique for characterizing crystalline phases, it is less effective at measuring poorly crystalline and amorphous phases (Moore and Reynolds, 1997).
In this research, we use visible and short-wave infrared (VSWIR) reflectance spectroscopy as a complementary method to obtain mineral identification and abundances. To date, very limited studies have used VSWIR to determine natural dust particle mineralogy (e.g., Reynolds et al., 2020); however, it can provide quantitative measurements and identify both amorphous and crystalline phases (Clark, 1999). This approach has been widely used to obtain mineral compositional information in laboratory and remote sensing applications with particular attention to mineral mixtures (e.g., Mustard and Pieters, 1987; Combe et al., 2008). Reflectance spectra of mixtures are modeled using radiative transfer (RT) theories, such as that developed by Hapke (1981), or linear spectral mixing (LSM) (e.g., Ramsey and Christensen, 1998). LSM is employed when a sample reflectance spectrum is simply a linear combination of the constituents' spectra, whereas RT is commonly utilized when materials are intimately mixed, and light is interacting with several minerals, resulting in a nonlinear relationship between abundance and spectral feature strength. Since planetary surfaces are mostly composed of intimately mixed minerals with nonlinear spectral interactions, RT has been found to be an effective way to derive mineral abundances from reflectance spectra measured from spacecraft and in the laboratory (e.g., Mustard and Pieters, 1987, 1989; Hiroi and Pieters, 1994; Lucey, 1998; Cheek and Pieters, 2014; Robertson et al., 2016; Lapotre et al., 2017). Additionally, many studies have employed RT to model reflectance spectra of synthetic or laboratory mineral mixtures, validating the derived abundances. For example, Robertson et al. (2016) demonstrated that physical mixtures of clay and sulfate at varying abundances were accurately determined (within 5 %) using a Hapke RT model.
Map (© Google Earth) shows the distribution of samplers throughout Ilam. Annotations note sample numbers identified in Appendix A, Table A1. Latitudes and longitudes are the coordinates for the corners of the map.
Moreover, multiple past studies have shown that the mineral abundances (for rocks and rocking forming fine-grained mineral samples) derived from visible and infrared reflectance spectra are in good agreement with mineral abundances that are obtained using XRD (e.g., Pan et al., 2015; Thorpe et al., 2015; Leask and Ehlmann, 2016). For example, Leask and Ehlmann (2016) performed measurements on 15 rock samples (with various particle sizes) collected from Oman, and they found that VSWIR reflectance spectroscopy paired with linear spectral unmixing yields quantitative mineral abundance estimates that are consistent (within 10 %–15 %) with XRD abundance estimations.
Here, we used both XRD and reflectance spectroscopy as complementary techniques to investigate the variation of both mineral composition and abundance in natural samples of atmospheric dust deposited in the city of Ilam, Iran. We estimated mineral abundances of these homogenous samples using their reflectance spectra and a Hapke RT model combined with linear mixing, and we compared those results with semi-quantitative abundances determined by XRD. We examined the ability of widely used spectral mixing approaches to determine if they can be used accurately to quantify mineral abundances in dust samples collected in urban settings.
For this study, we conducted measurements on 37 samples of dust captured
with marble dust collectors (MDCOs), located in Ilam, Iran. Based on an
original design by Ganor (1975), we chose MDCOs due to the efficiency and
popularity in desert research (e.g., Offer et al., 1992; Goossens and Offer,
1994; Goossens and Rajot, 2008). In general, the representation of dust in
the sample depends on the selected sampling method, which may result in
underestimation or overprediction of some important minerals (von Holdt et
al., 2021). MDCOs (like many other dust catchers) are less efficient in dust
collection in high-wind regimes (Goossens, 2005). However, they were proven to
be efficient at collecting dry deposition and less sensitive to local
weather conditions (Goossens and Offer, 1994; Sow et al., 2006; Goss et al.,
2013). Sadrian et al. (2012) selected Ilam as their study area because
it is located in western Iran and is affected by large dust sources in
neighboring countries including Iraq, Kuwait, and Saudi Arabia (Shahsavani
et al., 2012), and thus it is commonly impacted by severe dust storms. To
collect deposition of airborne dust, 13 dust samplers were distributed and
installed throughout the city area (Fig. 1). Deposited dust was collected in
three intervals from September 2011 through June 2012 (Table A1). Specific 3-month periods were 23 September to 21 December 2011
(fall) and 22 December 2011 to 19 March 2012 (winter), and 20 March to
20 June 2012 (spring). As part of sample collection procedure, first, dust
samples in MDCOs were dried at room temperature to preserve the mineralogical
and physical properties of the surface soils from which they were
transported. Then, the dry samples were collected from the samplers by
thoroughly cleaning the dust depositions using a brush. All samples were
transferred to separate plastic bottles for the further experiments. A total
of 39 samples were collected in order to determine their mineralogy, heavy
metal content, and deposition rate in different areas of Ilam (Sadrian
et al., 2012). In the current research, we revisit the compositional
information of these dust samples. The mass for the collected dust samples
ranges from minimum
XRD is a technique used to obtain the unique crystal structure of a
material. Diffracted beams are measured over a range of angles (2-theta) and
peaks at specific angles are related to the crystal structure of the mineral
(Klein, 2002). For the Ilam samples we used a Bruker D2 Phaser benchtop
X-ray diffractometer. Qualitative phase identification was performed using
XRD evaluation software (DIFFRAC.EVA), which helps to identify phases in a
specimen by comparison with standard patterns existing in a library. Figure 2 displays standard reference minerals with unique diffraction patterns
extracted from an accessible, established dataset (American Mineralogist
Crystal Structure Database (AMCSD); Downs and Hall-Wallace, 2003) compared
with unknown peaks in an Ilam sample (S11). As shown in Fig. 2, matches for
quartz (Q), calcite (C), albite (Al), microcline (M), gypsum (G), kaolinite
(K), and actinolite (Ac) (representative amphibole) were found in S11. The
identification of the illite peak in Fig. 2 uses data from the published
literature such as from Gualtieri (2000) and Drits et al. (2010). While
this peak pattern was available for our analysis in the DIFFRAC.EVA software,
we were not able to export the reference patterns in order to show them in
Fig 2. We used the AMCSD database for other minerals shown in Fig. 2, but
this database does not include a pattern for illite. Montmorillonite was
readily identified in most of the samples using spectroscopy (Fig. 3).
However, in XRD plots it is difficult to discriminate without special sample
preparation (e.g., clay separation). Because the volumes of dust samples
were low, XRD sample preparation specifically for clay minerals was not
conducted. Also, we could not follow sample preparation developed for low-mass atmospheric dust samples (Caquineau et al., 1997) due to a lack of
access to specialized equipment. In order to account for montmorillonite, we
included the standard reference pattern in all diffractograms and mineral
abundance determinations. Semi-quantitative (S-Q) assessment of mineral
abundances was obtained through integrated band area ratios and relative
intensities of several lines after removing background and source peak
noise. The result from S-Q analysis of all dust samples is discussed in
Sect. 3.1. S-Q abundances made from the diffraction measurements are derived
from the relative proportion of minerals in the sample (weight percentage
%) and should add up to 100 %. Given that the XRD is less effective at
detecting and quantifying poorly crystalline minerals and amorphous phases
(Moore and Reynolds, 1997), the obtained abundance results for other
existing crystalline minerals can be overestimated. Past studies
reported a detection limit which is generally
XRD pattern of sample S11 is compared with those of standard reference minerals from AMCSD. Dotted lines connect the diagnostic XRD peak in quartz (Q), calcite (C), albite (Al), microcline (M), gypsum (G), kaolinite (K), and actinolite (Ac) to the corresponding XRD patterns in S11, confirming the presence of these minerals in this particular dust sample. Illite (I) was identified as described in the text.
Minerals have distinctive spectral characteristics, and band center,
strength, shape, and width are utilized to confidently identify species
(Gaffey et al. 1993; Clark, 1999). In the VSWIR (350 to 2500 nm) diagnostic
absorption bands arise from transition electrons (generally caused by iron
oxides) in various crystallographic sites and from the overtones and
combinations of the fundamental vibrations of species such as hydroxyl,
water, and carbonate (Hunt, 1977; Clark et al., 1990). VSWIR reflectance
measurements of dust samples were carried out using a fine-resolution and
high-sensitivity spectral evolution (SE), model RS-5400 portable
spectroradiometer. To collect sample spectra, dust samples were placed in a
holder, and a contact probe with a halogen light source was used to capture
VSWIR data. As part of routine calibration, the contact probe measures a
white Spectralon plate. All sample measurements are automatically ratioed to
the Spectralon calibration target. We subsequently multiplied measured
spectra by the absolution reflectance of Spectralon, resulting in a
measurement that is in reflectance (Kokaly et al., 2017). Sample spectra
were measured with a 0
Mineralogy for the reflectance spectra of the Ilam dust samples was
determined by comparing the samples with the well-characterized USGS library
(Kokaly et al., 2017). Mineral constituents were identified with an
iterative procedure and inspection in which phases were identified on the basis
of H
SWIR spectra for three representative samples (S25, S26, S30) and library spectra of pure minerals showing diagnostic features for calcite, montmorillonite, illite, and gypsum. All spectra are offset for clarity. Arrows near 1400, 1900, 2200, and 2345 nm call out features arising from OH, water, and Al–OH in mineral structures, common to many clay minerals such as montmorillonite and illite. Arrows targeting 2340 and 2480 nm show the wavelengths of dominant absorption features in calcite. The arrow at 1945 nm represents the unique spectral signature attributed to water in sulfates such as gypsum.
An Olympus petrographic optical microscope was used to assess mineralogical
composition and relative abundance of minerals in the samples. Mineral
grains were mounted on a glass slide immersed with Cargille 1.544 refractive
index oil. Particles were identified based on their diagnostic properties
such as color, cleavage, refractive index, and texture. We were able to
detect some coarser particles such as quartz, carbonates, and amphibole (Fig. 4a and b); however, fine-grain clay minerals were not identifiable due to
the petrographic microscopy limitation for grain sizes less than 10
Panels
PSD was determined for all Ilam samples using a Malvern Mastersizer 3000.
This instrument is based on a compact optical system that uses laser
diffraction to measure particle size distribution for both wet and dry
dispersions (known as hydro and aero methods). We selected the wet
dispersion method for PSD analysis because this technique will separate
sand-sized micro-aggregates of particles into their smaller constituents for
the final results (Hartshorn et al., 2021). This method also allows for full
sample recovery. For subsequent analysis, the particle size fractions that
make up the samples were categorized into three groups: clay (
Ternary diagram showing volume distributions for 37 dust samples
analyzed with the Malvern Mastersizer 3000. Silt is the most prevalent size
class in the samples with a minimum
In order to determine dust mineral abundances from reflectance spectra, we
initially used linear spectral mixing (LSM) of the reflectance spectra. This
approach assumes that the spectrum of the sample is a linear combination of
the spectra of individual minerals (endmembers), and it has been extensively
used to characterize materials on the surface of Earth (e.g., Metternicht
and Fermont, 1998; Roberts et al., 1998; Dennison and Roberts, 2003) and
Mars (e.g., Bell et al., 2002; Combe et al., 2008). Based on LSM, the
reflectance spectra of a mixture can be expressed as (Keshava and Mustard,
2002)
To solve Eq. (1) for
To derive fractional abundances, the NNLS MATLAB solver is used to input a
matrix of mineral endmember SSA and dust sample
Panels
Panels
S-Q analysis, as described in Sect. 2.2, resulted in mineral mass abundances shown in Fig. 8. The XRD bar chart (Fig. 8) indicates that individual mineral abundances vary from sample to sample, yet there is some regularity. Quartz and albite (plagioclase), followed by illite (clay), are the most common minerals in the samples. Kaolinite and montmorillonite (clays) are dominantly detected in minor and trace levels in the samples and thus make up a small fraction of the total abundances. Some minerals in the XRD bar chart are more variable both in their presence and abundance. Calcite (carbonate) shows the highest variation with a range between 0 % and 63 % of the total mineral abundance. Microcline (K-feldspar), actinolite (amphibole), and gypsum (sulfate) are among the least common minerals. XRD detected gypsum in only three samples collected close to construction sites. Since sulfate is a common mineral on many construction sites, its infrequent and rare presence may be derived from nearby building materials.
Bar charts demonstrate the relative phase concentration (wt %) calculated from the total diffracted peak area of various minerals obtained by XRD analysis.
As discussed in Sect. 2.6, all spectra were modeled to derive mineral abundances. The goodness of the fit is highly dependent on the input endmembers. While additional endmembers can improve the quality of the model, incorporating extra endmembers just to improve the fit can lead to erroneous abundances. Therefore, we included only the phases that were identified with SWIR and XRD based on diagnostic features. Figure 9 demonstrates mineral abundance variations obtained from linear mixing of SSA. This figure depicts a high abundance of microcline, quartz, and albite in the samples, although these minerals are featureless in the SWIR range (Fig. 3). As also shown in Fig. 3, pure library minerals have much stronger absorption features (greater depth) than those observed in the Ilam samples. This is referred to as higher spectral contrast. Therefore, the model automatically uses microcline, quartz, and albite as neutral endmembers to create a model spectrum that fits weaker features. By incorporating featureless material, the overall spectral contrast is reduced at all wavelengths (Hamilton et al., 1997, 2000). This results in relatively low abundances of other minerals (Fig. 9). In order to better compare to XRD, we removed microcline and other spectrally neutral minerals (quartz and albite) and then re-normalized the abundances for both XRD and SWIR (Fig. 10).
Bar charts show the relative mass fraction (%) calculated from a linear combination of SSA of minerals and asphalt. The unrealistically large proportions of microcline, quartz, and albite are discussed in the text.
Figure 10 displays normalized mineral fractions (%) after removing microcline, quartz, and albite from both SWIR and XRD. A comparison of these bar charts reveals that SWIR models are dominated by the abundance of clays, with often a lower abundance of carbonate and asphalt. Montmorillonite, kaolinite, and illite are the most prevalent components and are highly variable in the samples. Since it is difficult to distinguish montmorillonite from illite using XRD, we will compare the abundance of all clay minerals in the next section. Surprisingly, asphalt has a relatively high fraction and is included in the models of the majority of samples but would not be observed by XRD due to its lack of crystal structure. This suggests that asphalt may act as an agent to reduce spectral contrast and contribute to the lower relative abundance of carbonate, similar to that of microcline and other transparent minerals. The three samples that contain gypsum are the same in both SWIR and XRD. Although actinolite (amphibole) is a variable component in the XRD data, it is not apparent or used in the SWIR models at a detectable level.
Bar charts show XRD
Due to the contribution of the non-mineral materials in the samples, many model fits were poor (e.g., Fig. 6c) and hence did not retrieve mineral abundances correctly. Poor models may omit, underestimate, or even overestimate the abundance value for specific minerals. In order to better compare the mineral abundances derived from the spectra and XRD S-Q results, a thorough examination inspected both model fit match quality and RMSE (Appendix C, Fig. C1) and identified 21 samples that had well-matched absorption feature centers and strengths (check marks in Fig. 10 and Appendix C, and, e.g., Fig. 6a and b) and RMSE values below 0.07. In order to compare equivalent abundances, transparent mineral amounts were first removed from both SWIR and XRD (Fig. 10), and then endmember fractions that had non-zero values were re-normalized to 100 %. Illite and kaolinite are among the most common minerals detected with XRD, but in SWIR, both display a very high variability. Montmorillonite presents as a small fraction in XRD abundances but is often quite high in SWIR. In order to compare illite, kaolinite, and montmorillonite abundances from XRD and SWIR, we collected their abundances together into a clay group. Figure 11 compares the abundance of the dominant non-transparent mineral components (clays and carbonates) for the 21 samples having good spectral fits. Figure 11 demonstrates a positive correlation for both clay and carbonate abundance values from XRD and SWIR. However, whereas the best fit correlation for clays displays a linear relationship between abundances generated from these two approaches, the one-to-one comparison of the fractions mostly shows an underestimation of the amount of clay by XRD. On the other hand, the best fit correlation plot for calcite (Fig. 11b) indicates that SWIR significantly underestimates calcite abundances compared to the corresponding XRD percentages.
Plots display the difference between abundance values (in
wt %) derived from XRD and SWIR. Data and the best fit line are in blue, and
a
In this study, we obtained compositional information and mineral mass abundances for dust samples from both XRD and SWIR. The goal was to compare spectrally derived abundances with S-Q-determined abundance values via XRD. We also aimed to evaluate if combining the Hapke model for SSA and the LSM can accurately predict mineral abundances in natural dust samples collected in urban areas. SWIR vastly overpredicts microcline, quartz, and albite abundances as these spectrally neutral minerals are automatically employed in modeling to uniformly decrease spectral contrast between measured spectra and model fit. After normalizing both datasets for the influence of transparent minerals on the SWIR data, our analysis illustrates that XRD somewhat underpredicts total clay mineral content (Fig. 11a) but underpredicts montmorillonite by a significant margin (Fig. 10). In contrast, spectral modeling predicts a considerable amount of montmorillonite (up to 47 %) in the samples. A comparison of clay abundances from these two techniques showed a positive correlation. However, individual sample comparisons mostly showed a higher abundance for clays derived from spectroscopy. Calcite abundances determined from XRD and SWIR also have a linear correlation (Fig. 11b), although SWIR greatly underestimates its abundance. These results also reveal that SWIR is highly sensitive to non-mineral components such as man-made and plant materials (Figs. 6c, 7a, and b). In Fig. 10, asphalt is one of the most common constituents detected by SWIR, and it substantially contributes to the total abundances for many samples. XRD detected actinolite in a few samples, with varied levels of abundance; however, the SWIR models did not use this mineral even though it was included in the endmember bundle. Possible reasons for the discrepancies in the results obtained from XRD and SWIR are discussed next.
X-ray diffraction (XRD) is the most frequent technique used to characterize dust mineralogy; nevertheless, it is less effective at detecting weakly crystalline or amorphous phases. Given that S-Q mineral abundances tend to underpredict clay mineral abundances, when the sum of all phases in the mixture is normalized to 100 %, the abundance value for calcite and other crystalline minerals may then be overestimated. SWIR spectroscopy, being sensitive to molecular bonding, provides additional information. In SWIR, clay minerals have unique features and strong absorptions; hence their abundances can be best estimated using this wavelength range. Our result determined that XRD underpredicts total clays and, in particular, montmorillonite abundances compared to SWIR. Therefore, we recommend using SWIR in combination with XRD for identifying and quantifying mineral dust particles as the latter traditional approach may overlook some clay phases in the sample.
Natural samples have a range of particle sizes, and the minerals in the
library used for modeling should match the particle size of the sample.
Variable size classes (clay, silt, and sand) were present in our dust
mixtures, which substantially altered the strengths of absorption features
(Fig. 3) and the overall brightness of the reflectance in each sample
spectrum (Gaffey, 1986; Cooper and Mustard, 1999). Gaffey (1986) showed that
calcite absorption feature depth is weakened with decreasing particle size.
The well-characterized suite of minerals used in the USGS spectral library
(Kokaly et al., 2017) often contains minerals at smaller grain sizes, but
for the most part, published data use a grain size of 74–250
We explored whether sample particle size distribution had an effect on the
quality of the model fit, particularly for the fraction of particle sizes
greater than 30
Inspection of all model fits identified 16 samples that had poor matches (e.g., Appendix C and Fig. 6c). These samples showed a strong contribution of known and unknown man-made and plant materials (Fig. 7a and b) in their measured spectra. Among the possible additional materials are a variety of particles such as asphalt, tar, styrofoam, plastic, and dry grass, some of which were visually identified. Absorption from these materials can contribute strongly to the measured spectra preventing a good match. Additionally, many studies have demonstrated that mixing dark grains with other minerals can diminish the mixture's reflectance and considerably weaken the absorption bands observed (Nash and Conel, 1974; Singer, 1981; Clark, 1983). We note that the absolute reflectance values for asphalt and tar in the USGS library (Kokaly et al., 2017) are less than 23 %, thus contributing as dark agents in dust samples. Calcite has a strong diagnostic absorption feature around 2340 nm, but this appears only weakly in our measurements (e.g., Figs. 3 and 6). The absence of this feature may be due not only to fine grain size but also to the contribution of strong absorption from dark man-made constituents. This also leads to the underestimation of calcite abundance obtained from SWIR. XRD is not sensitive to non-crystalline phases and thus is not sensitive to their presence in the samples. Therefore, it is preferable to use XRD to obtain abundances for crystalline phases when mixed with other materials. To characterize and quantify urban dust, reflectance spectroscopy should also be utilized to account for non-mineral materials that are present in mixtures as XRD would miss them. As Fig. 7 displays, SWIR can quickly identify non-mineral diagnostic absorptions (such a hydrocarbon bonds). These materials can contribute strongly to dust mixtures collected from urban settings. Including various additional urban materials in spectral libraries would probably help improve the model fit, but this was not in the scope of this research.
XRD detected both actinolite and kaolinite in trace and minor levels. In SWIR, however, actinolite was included in endmember bundles, but it was not selected by the models. Spectrally derived kaolinite, on the other hand, had highly variable amounts (0 %–50 %), although we did not uniquely observe its diagnostic absorption features in any of the samples. The absence of abundance values for actinolite and unique spectral signatures for kaolinite could be due to their absorption features being suppressed when mixing with other minerals and with dark grains. In addition to the effect of non-mineral components, kaolinite absorption features can be weakened or disappear as montmorillonite abundances increase in the mixture (e.g., Ducasse et al., 2020).
In the VSWIR, reflectance spectra are shaped by electronic and vibrational
transitions (Hunt, 1977) allowing detection of compositional information of
surface materials. Clay minerals commonly display sharp and narrow
diagnostic absorption bands in this wavelength range (Fig. 3) and thus can
be best identified and abundances estimated. For other minerals, the
vibrational absorptions detectable in VSWIR are weaker signals compared to
corresponding features in the long-wave infrared (LWIR,
In this research, we set out to test if SWIR reflectance spectroscopy combined with a Hapke model and linear spectral mixing of SSA can accurately estimate mineral abundance consistent with semi-quantitative values determined by XRD. The techniques showed better agreement after normalizing for the use of transparent minerals to match weak features in the measured spectra. Both total clay content and carbonate are linearly correlated between the two techniques. However, XRD underpredicted total clay content, and SWIR significantly underpredicted carbonate content. Our analysis showed that SWIR is well-suited to identify clay phases that would be missed by XRD techniques and is also a quick and effective way to survey a group of samples with little preparation. Figure 11a shows that spectrally derived clay abundances correlate well with XRD-derived abundances, but the latter technique underpredicts clay abundances unless samples undergo time-consuming additional sample preparation (e.g., clay separations). From the evaluation of SWIR spectra of dust samples, we conclude that calcite-dominant absorption features are weakened when mixtures are composed of very fine-grained minerals combined with dark man-made materials. This limitation consequently leads to underprediction of calcite in the SWIR abundance determinations. SWIR is advantageous in detecting absorption features attributed to non-mineral materials in samples. These materials are common in urban settings and may also be important for radiative forcing in the atmosphere. Optical microscope images confirm the presence of black and angular-shaped materials, but their composition is not readily identified with this technique. XRD, on the other hand, is not sensitive to non-crystalline phases, so it does not have the ability to characterize them. While each of these approaches are useful for estimating abundances of different types of particles, a combination of the two for full characterization of urban dust has yielded complementary results. However, because quartz and feldspars are substantial fractions of total mineral abundances of dust samples (Fig. 8), we suggest the use of XRD as an initial reliable method for mineral identification and quantification. Based on our analysis, we recommend that future research include spectral measurements in both VSWIR and LWIR as the latter spectral range can be complementary to the former and obtain abundances for VSWIR-transparent minerals (e.g., quartz and feldspars). As a result, the present minerals in the bulk sample can be qualitatively and quantitatively assessed by both VSWIR and LWIR, and then confidently compared with XRD-determined mineral abundances.
Because our analysis uses VSWIR and contributes to fundamental measurements of dust, it can guide further dust mineralogy investigations by satellite imaging spectrometers such as the Earth Surface Mineral Dust Source Investigation (EMIT) (Green et al., 2020). VSWIR reflectance spectroscopy can readily identify clays, carbonates, and iron oxides and distinguish them from non-mineral materials that are components of dust mixtures.
Locality of 13 deposition samplers in Ilam. Sample numbers shown with N/A did not have enough sample volume for analysis. Root mean square errors (RMSEs) for spectral model fit are also shown. Bold and italic fonts on sample number and RMSE indicate those with a good spectral model fit as described in the text and shown with checks in Fig. 10 and Fig C1.
Mineral spectra from USGS library (Kokaly et al., 2017) used by model to retrieve mineral abundances for natural dust samples.
Spectral model fit (red line) and RMSE are shown for all 37 dust sample SSA spectra (blue line). Check marks represent that the model fits relatively well based on visual inspection and relatively low RMSE.
Data and code used in this study are available in Kokaly et al. (2017) and on request to msadrian@nevada.unr.edu or wcalvin@unr.edu.
MRS and WMC collaborated on project conceptualization, funding, and goals. MRS performed all measurements and data analysis. JM provided OM image interpretation. MRS prepared the manuscript with contributions from all co-authors.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors thank UNR Chemistry Department Shared Instrumentation Laboratory for making its XRD facilities available, Janina Ruprecht for assisting with XRD training, Mohammad Jafari who helped with writing algorithms in MATLAB, and Patrick Arnott and Hans Moosmuller for their helpful comments on the draft manuscript. We appreciate the detailed external reviews provided by the anonymous reviewer, Gregg Swayze, and Longyi Shao that improved the manuscript content and clarity.
This work has been supported in part by the UNR Graduate Student Association Graduate Research Grant and Travel Grant programs, the College of Science Dean's Office, Nevada NASA EPSCoR Research Infrastructure Seed Grant #18-83 from Federal Award Number NNX15AK48a, and co-author Wendy M. Calvin's discretionary funds.
This paper was edited by Mingjin Tang and reviewed by Gregg Swayze, Longyi Shao, and one anonymous referee.