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\begin{document}

\title{LIBS and FTIR-ATR spectroscopy studies of mineral-organic associations
in salinized soil}



\author[1]{Fei Ma}%
\author[1]{Changwen Du}%
\author[2]{Yiqiang Zhang}%
\author[1]{Xuebin Xu}%
\author[1]{Jianmin Zhou}%
\affil[1]{Institute of Soil Science, Chinese Academy of Sciences}%
\affil[2]{ Institute of Water Science, Bayannur}%


\vspace{-1em}



  
  \date{May 10, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The direct characterization of the spatial distribution of elements and
compound binding of salt-organic associations in soil is imperative for
understanding the mechanisms of organic matter decomposition and
nutrient release in soil degradation and development processes. Modern
spectroscopic techniques provide a feasible method for analysis at the
microscale. In this study, mid-infrared attenuated total reflectance
spectroscopy (FTIR--ATR) was used to obtain molecular functional group
information, laser-induced breakdown spectroscopy (LIBS) was apply to
obtain micro-level distribution features of elements in soil, and
two-dimensional correlation spectroscopy (2DCOS) analysis was conducted
to illustrate the binding combination features of mineral-organic
associations in salinized from the Hetao Irrigation District in China.
The results showed that the distributions of Mg, Ca, Na, and K were
heterogeneous at the micro-level; the spatial distributions of Mg and Ca
showed a significant correlation (r = 0.90***), while K displayed a
negative correlation with the SOM contents. In soil with lower SOM
contents, the elements were distributed at the top of the ablation area
and enhanced with the increasing SOM content, which reflected the trends
of the SOM layer thickness outside the mineral--organic associations at
the micro-level. Furthermore, 2DCOS analysis suggested that the hydrogen
bonds in silicate groups were stronger than those of organic functional
groups, such as C=O/C=C, when combined with salt-related compounds, and
Mg, Ca, Na, and K did not originate from clay mineral compounds in
salinized soil, but partially originated from deposited organic
associations.%
\end{abstract}%



\sloppy


\textbf{ABSTRACT}

The direct characterization of the spatial distribution of elements and
compound binding of salt-organic associations in soil is imperative for
understanding the mechanisms of organic matter decomposition and
nutrient release in soil degradation and development processes. Modern
spectroscopic techniques provide a feasible method for analysis at the
microscale. In this study, mid-infrared attenuated total reflectance
spectroscopy (FTIR--ATR) was used to obtain molecular functional group
information, laser-induced breakdown spectroscopy (LIBS) was apply to
obtain micro-level distribution features of elements in soil, and
two-dimensional correlation spectroscopy (2DCOS) analysis was conducted
to illustrate the binding combination features of mineral-organic
associations in salinized from the Hetao Irrigation District in China.
The results showed that the distributions of Mg, Ca, Na, and K were
heterogeneous at the micro-level; the spatial distributions of Mg and Ca
showed a significant correlation (\emph{r} = 0.90\textsuperscript{***}),
while K displayed a negative correlation with the SOM contents. In soil
with lower SOM contents, the elements were distributed at the top of the
ablation area and enhanced with the increasing SOM content, which
reflected the trends of the SOM layer thickness outside the
mineral--organic associations at the micro-level. Furthermore, 2DCOS
analysis suggested that the hydrogen bonds in silicate groups were
stronger than those of organic functional groups, such as C=O/C=C, when
combined with salt-related compounds, and Mg, Ca, Na, and K did not
originate from clay mineral compounds in salinized soil, but partially
originated from deposited organic associations.

\textbf{Keywords:} Mineral--organic association, infrared attenuated
total reflectance spectroscopy, laser induced breakdown spectroscopy;
two-dimensional correlation spectroscopy, spatial distribution,
microscale

\textbf{INTRODUCTION}

Soil salinization adversely affects the physical, chemical, and
biological processes of soil, which leads to land degradation and
productivity loss (Liu et al., 2018; Sidike et al., 2014; Zovko et al.,
2018). The identification of salt-affected soil processes and assessment
of the degree of salinization are essential for sustainable agricultural
management (Daliakopoulos et al., 2016; Farifteh et al., 2008). The
surfaces of minerals and their incorporation into aggregates could
physically prevent soil organic matter from extensive decomposition
(Lehmann \& Kleber, 2015; J. Xiao et al., 2018). On one side, saline
soils have been found to have variable effects on carbon and nitrogen
mineralization, which are crucial for the decomposition of organic
matter and release of nutrients required to sustain productivity and
promote a higher fraction of plant intake in the accumulated organic
matter (Daliakopoulos et al., 2016; Pathak \& Rao, 1998; Xiao et al.,
2019; Zhang et al., 2019). On the other side, the addition of salts in
soil get negative results of soil organic carbon loss as decreased
fertility and adversely affects stability of soil aggregates (Singh,
2016; Six et al., 2000). The processes increase dispersion of clay
particles as well as soil erosion rates (de la Paix et al., 2013).
Therefore, land degradation has recently become a particular focus on
the study of soil carbon turnover process (Su et al., 2010) in the
context of salt contents soils (Su et al., 2010; J. Xiao et al., 2018),
which would be benefit for understanding the formation and stability of
organic-mineral complexes in soils.

The Hetao Irrigation District (HID) is a plain located in an
arid/semi-arid region that was formed by the Yellow River along its
north bank. This area is fertile and densely populated and has been
irrigated with water from the Yellow River since ancient times.
Irrigated agriculture uses approximately 90\% of the total surface water
resources (Wu et al., 2017; Xue \& Ren, 2017). For a considerable time,
soil salinization in the area has been aggravated by flood irrigation,
increased soil erosion, and water deterioration induced by excessive
fertilization (Wu et al., 2017). Meanwhile, soil salinity management
relies upon the identification of proper methods and techniques for
monitoring and accessing salt-affected soils. Salts are contained in
minerals in the form of carbonates, halides, sulfates, and borates
(Klein \& Hurlbut Jr, 1999). However, \emph{in situ} records of the
associations of organic and inorganic groups in soils at the microscale
have been obstructed due to methodological and analytical limitations.

Recently, spectral-based analytical technologies have been widely used
in soil research because they provide \emph{in situ} , microscale soil
information rapidly (Xing et al., 2019). Mid-infrared attenuated total
reflectance spectroscopy (ATR) reflects information concerning molecular
bonds, such as the modes of molecular functional groups (e.g.
stretching/wagging vibration) and clay in minerals, and has been used
for soil identification as well as nitrate, clay, sand, and soil carbon
measurements (Kira et al., 2014; Linker et al., 2005; Ma et al., 2019).
Laser-induced breakdown spectroscopy (LIBS) is an atomic emission
spectroscopy technique. As the plasma cools, continuum, ionic, and
atomic emissions occur, revealing the elemental composition of the
samples. LIBS could obtain microscale information along the vertical
scale from each shot (Ilhardt et al., 2019). Therefore, it can be
applied to characterize the distribution of soil organic matter, clay
minerals, and salt--related elements at the microscale. Additionally,
the sensitivity and spatial resolution of LIBS are high (de Oliveira et
al., 2019; Kim et al., 2013; Suyanto et al., 2017; Zaytsev et al.,
2018).

The two techniques can be used simultaneously to expand upon the current
information concerning minerals--organic association at microscale.
Considering the possible overlapping spectral features and the
heterogeneity of soil samples, two-dimensional correlation spectroscopy
(2DCOS) was employed to present the spectral intensity trends in
relation to a perturbation sequence (such as time, temperature,
concentration, or spatial distance) over a second dimension (Noda, 2018;
Sun et al., 2017; Sun et al., 2019). 2DCOS is typically displayed as
contour maps of correlation intensities, as functions of two independent
wave numbers/wavelengths. 2DCOS can probe the specific sequence of any
subtle spectral changes in response to external perturbations (Noda,
2018; Ruan \& Zhou, 2008; Xu et al., 2018), which can be employed to
detect changes in organic and inorganic functional groups and structural
relationships in mineral-organic processes along spatial and
microscales.

Thus, we operated the experiments aimed to investigate the
minerals--organic complex employing spectral based techniques at
microscale, which would provide another sight to understand the relation
of mineral and soil organic matter association process in saline soil.
The objectives of this study were to: i) investigate the molecular
functional groups feature in saline soil using FTIR-ATR spectroscopy;
ii) describe the salinization-related Mg, Ca, Na and K element
distribution of soil samples using LIBS spectra; and iii) explore the
mineral-organic association in saline soils using 2DCOS analysis.

\textbf{2. MATERIALS AND METHODS}

\textbf{2.1. Soil sample preparation}

Soil samples (0--20 cm) were collected from Hetao Basin, Inner Mongolia
Autonomous Region which is along with the Yellow River in China
(latitude 40.719-41.1841N, longitude 107.278-108.7599E) (Fig. 1). The
main soil textures are loam, sandy loam, and silt sand (Xue \& Ren,
2017). There is a typical continental arid/semi-arid climate in the
area.

\textbf{2.2. Determination of soil properties}

The air-dried soil samples were sieved through a 2-mm mesh to remove any
plant matter or debris prior to analysis. Prior to spectroscopic
measurements, the soil properties of pH value, SOM contents, the
concentration of Mg\textsuperscript{2+}, Ca\textsuperscript{2+},
K\textsuperscript{+} + Na\textsuperscript{+} were determined using
conventional chemical methods (Sparks, 1996) and are listed in Table 1.

In this study, six different salt contents with various SOM contents
were selected from the above soil samples. The low salt contents were
1.08 and 1.09 g/kg, the medium salt contents were 9.49 and 9.55 g/kg,
and the high salt contents were 13.31 and 16.10 g/kg. The corresponding
SOM contents were 3.53, 11.38, 8.60, 15.24, 5.78, and 7.52 g/kg,
respectively.

\textbf{2.3. Spectral measurements}

Soil samples were pressed flat against the diamond for improved contact,
and a blank reference was used before each sample was scanned. ATR
spectra were detected using a handheld attenuated total reflectance
infrared Fourier transform spectrometer (Trudefender FT, Thermo
Scientific, Waltham, MA, USA). The spectral scanning region was 4000 to
600 cm\textsuperscript{-1}, with a spectral resolution of 4
cm\textsuperscript{-1}. The background noise was subtracted from each
scan to correct for atmospheric and instrumental effects.

LIBS spectroscopy was performed using a MobiLIB system (IVEA, Orsay,
Essonne, France) with a fourth-harmonic Nd: YAG laser operating at 266
nm, with a 5-ns pulse energy of 16 mJ per pulse, and a high-resolution
CCD (Sony ILX511B, Tokyo, Japan) spectrometer (Mechelle 5000 Echelle,
Andor Technology, Belfast, Northern Ireland) ranging from 200 to 975 nm.
Samples were pressed into pellets with a 1 cm diameter and 0.2 cm
height. The beam diameter at the focal point was 20 \selectlanguage{greek}μ\selectlanguage{english}m. The shot points
were 4 \selectlanguage{ngerman}× 4 matrices with 4 layers of shots at each point (Fig. 2). The
ablation spot after each shot was approximately 150 \selectlanguage{greek}μ\selectlanguage{english}m. The distance
between two horizontal points was 0.5 mm. As the study was focused on
spectra at the micro-level, and the intensity of LIBS spectra can be
affected by the shots, we only obtained three shots at the vertical
level.

\textbf{2.4. Spectral data processing}

The FTIT-ATR and LIBS spectra were pre-processed with baseline
correction and a Savitzky--Golay smoothing filter (Savitzky \& Golay,
1964), to reduce the baseline float/noise and improve the
signal-to-noise ratio. Matlab software (Mathworks, USA) was used to
smooth the data and for all other statistical analyses performed.

The 2DCOS analysis was performed using the method established by Noda
and Ozaki (Noda, 2018; Noda \& Ozaki, 2005). The intensity of the
synchronous 2D correlation spectrum, \selectlanguage{russian}Ф (\selectlanguage{greek}λ\selectlanguage{english}\textsubscript{1},
\selectlanguage{greek}λ\selectlanguage{english}\textsubscript{2}), represents the simultaneous or coincidental changes
in the spectral intensity at wavenumbers \selectlanguage{greek}λ\selectlanguage{english}\textsubscript{1} and
\selectlanguage{greek}λ\selectlanguage{english}\textsubscript{2} with an external perturbation. The peaks on the
diagonal line are called auto peaks, while the peaks on the off-diagonal
line are called crosspeaks. The asynchronous spectrum, \selectlanguage{greek}Ψ
\selectlanguage{english}(\selectlanguage{greek}λ\selectlanguage{english}\textsubscript{1}, \selectlanguage{greek}λ\selectlanguage{english}\textsubscript{2}), represents the out-of-phase or
sequential changes in spectral intensities with external perturbation.
There is no diagonal peak, but there is a cross peak in off-diagonal
areas (Noda, 2018). The red and blue colors on the map represent
positive and negative intensities, respectively.

In this study, different salt contents were applied as the external
perturbation for the FTIR spectra, while the different vertical
detecting points were used as external perturbations for the LIBS
spectra. In our study, the spectral synchronous and asynchronous
correlation maps were generated from the analysis of the ATR and LIBS
spectra, respectively.

\textbf{3. Results}

\textbf{3.1. FTIR-ATR spectra of soil samples}

The average FTIR-ATR spectra of the soil samples are shown in Fig. 3.
The peak at 3610 cm\textsuperscript{-1} was attributed to the stretching
vibration of O-H in clay minerals, water, carboxyl groups, and hydroxyl
groups (Madejova \selectlanguage{english}\& Komadel, 2001; Nayak \& Singh, 2007; Peltre et al.,
2014; Xu et al., 2018). The peak at approximately 1660
cm\textsuperscript{-1} was attributed to the stretching vibrations of
C=C/C=O in amides and aromatics (Madejova \selectlanguage{english}\& Komadel, 2001). The
stretching vibration of H--O--H from clay bound water has been
highlighted for the peak at 1630 cm\textsuperscript{-1}. The peak at
1434 cm\textsuperscript{-1} has been attributed to the stretching
vibration of CO\textsubscript{3}\textsuperscript{2-} in carbonate
(Soriano-Disla et al., 2013). The stretching vibration of C--H in
methyls explains the peak spanning 1445--1350
cm\textsuperscript{-1}(Bernier et al., 2013; Calder\selectlanguage{ngerman}ón et al., 2013;
Movasaghi et al., 2008). The stretching vibration of Si--O in silicates
(quartz from clay minerals) is present in the 1030--950
cm\selectlanguage{english}\textsuperscript{-1} region (Madejova, 2003). The peak at 915
cm\textsuperscript{-1} has been attributed to the bending vibration of
Al--OH from kaolinite and smectite (Madejova\selectlanguage{english} \& Komadel, 2001). The
stretching vibration of C--O from carbonate is visible via the peak at
872 cm\textsuperscript{-1}. The out-of-plane vibrations of amine
(NH\textsubscript{2}) explain the peak at 761 cm\textsuperscript{-1}
(Smidt \& Meissl, 2007).

\textbf{3.2. LIBS spectra of soil samples}

The average LIBS spectra of the soil samples, from 200 to 1000 nm, are
displayed in Fig. 4. The main Si, Mg, Ca, Na, and K lines were marked
according to the National Institute of Standards and Technology (NIST)
electronic database (Kramida, 2018). The Ca II (ionic) lines were at
315.89, 317.93, 393.37, 396.85, and 422.67 nm, while the Ca I (atomic)
lines were at 558.88, 559.45, and 610.28 nm. The Mg II (ionic) lines
were at 279.55 and 280.27 nm, while the Mg I (atomic) lines were at
517.27 and 559.45 nm. The Na I lines were at 588.99 and 589.59 nm. The K
I lines were at 766.49 and 769.90 nm. The Si line was at 288.15 nm. In
general, choosing suitable lines from spectra is an important and
challenging task in LIBS analysis. Researchers (R\selectlanguage{ngerman}ühlmann et al., 2018)
have mentioned that the Ca I line at 422.67 nm and Ca II lines at 393.37
and 396.85 nm have been employed for Ca determination detection, but did
not show good performance for Ca nutrient quantification in soil because
of self-absorbance. Thus, the ionic lines of Mg I at 517.27 nm, Ca I at
558.88 nm, Na I at 588.99 nm, and K I at 766.19 nm were selected for
further study.

\textbf{3.3. Spatial distribution of metal element contents}

The Mg, Ca, Na, and K lines intensities of six selected samples were
obtained from the LIBS spectra (depth in micrometers). The analyzed soil
sample properties and calculated correlation coefficients are shown in
Fig. 5. Fig. 5a indicates that the SOM was almost uncorrelated with the
pH (r = -0.001) and was not significantly correlated with the pH, salt,
Ca\textsuperscript{2+}, Mg\textsuperscript{2+}, K\textsuperscript{+}+
Na\textsuperscript{+}, or CEC contents. Additionally, the pH was not
significantly correlated with the salt content or CEC. The salt content
was significantly correlated with the CEC (\emph{r} =
0.98\textsuperscript{***}), K\textsuperscript{+} +
Na\textsuperscript{+}(\emph{r} = 0.93\textsuperscript{***}),
Mg\textsuperscript{2+} (\emph{r}= 0.73\textsuperscript{***}), and
Ca\textsuperscript{2+} (\emph{r} = 0.42\textsuperscript{***}). The
correlation coefficients of Ca\textsuperscript{2+} with
Mg\textsuperscript{2+} (\emph{r} = 0.43\textsuperscript{***}) and CEC
(\emph{r} = 0.30\textsuperscript{***}) were significant. The
Ca\textsuperscript{2+}contents were neither uncorrelated nor
significantly correlated with the K\textsuperscript{+} +
Na\textsuperscript{+} contents (\emph{r} = 0.09), but the
K\textsuperscript{+} + Na\textsuperscript{+} contents were highly
significantly correlated with the CEC contents (\emph{r} =
0.96\textsuperscript{***}). The element contents at each ablation spot
could not be obtained using the traditional method, owing to technical
limitations. However, the LIBS spectral line intensity was positively
related with the element contents. Thus, the elemental spectra of six
selected samples were analyzed, the relationships for which are shown in
Fig. 5b. The Mg line intensity showed the highest significant
correlation with Ca (\emph{r} = 0.90\textsuperscript{***}), while the Na
line intensity showed a higher correlation coefficient with K (\emph{r}=
0.77\textsuperscript{***}). The correlation coefficient between Mg and K
was 0.73\textsuperscript{***}. The spatial distribution maps of Mg, Ca,
Na, and K were plotted based on the intensity of the element in Fig. 6.

For the low-salt content samples (columns C1 and C2), the contents of
the four elements differed (C1: salt 1.08 g/kg and SOM 3.53 g/kg; and
C2: salt 1.09 g/kg and SOM 11.38 g/kg). It was found that Mg, Ca, Na,
and K were mainly distributed in the upper area of each sample in column
C1. However, the distributions of the elements were more even in column
C2. It was noted that the distribution of Mg was similar to that of Ca,
while the distribution of Na was similar to that of K. The elemental
distribution of the medium-salt content samples (column C3: salt 9.49
g/kg and SOM 13.80 g/kg; and C4: 9.55 g/kg and SOM 5.60 g/kg) differed
from that of the low-salt content samples. When the SOM content of the
medium-salt samples was higher (13.80 g/kg), Mg, Ca, and K were mainly
distributed in the middle and bottom areas of the sample. However, the
four elements showed greater distribution trends with the lower SOM
contents (5.60 g/kg). For high salt-content samples (column C5: salt
10.17 g/kg and SOM 13.20 g/kg; and C6: salt 16.10 g/kg and SOM 7.52
g/kg), the four elements were mainly distributed from the top to the
middle areas.

\textbf{3.4. Two-dimensional correlation spectroscopy (2DCOS) analysis}

To clarify the association between the salt-related elements and SOM,
FTIR--ATR was employed. For complicated materials, such as soils, the
peaks of the functional groups and molecule bonds located in different
submicron domains may be heterogeneous and overlap in 1 D FTIR--ATR
spectra. However, the overlapping peaks could have different responses
to external perturbations along spatial and micro scales. 2DCOS is built
on the classical cross-correlation analysis of spectral intensity
variations observed for a sample system of interest under the influence
of external perturbations. The concentration of salt was set as the
external perturbation.

The generalized two-dimensional synchronous and asynchronous salt
content--dependent correlation spectra of the soil samples are plotted
in Fig. 7. Auto-peaks appearing at diagonal positions represent the
overall susceptibility of the corresponding spectral region to change in
spectral intensity, which could be applied to the study system as an
external perturbation. In Fig. 7a, the auto-peaks along the diagonal
line of the synchronous spectrum, located at \selectlanguage{russian}Ф (1698, 1698), Ф (1548,
1548), Ф (1264, 1264), and Ф (995, 995) cm\textsuperscript{-1}, have
been assigned to the C=C stretching in amides and carboxylic acids (1698
cm\textsuperscript{-1}), N--H and C--N stretching in Amide II (1548
cm\textsuperscript{-1}), C--OH and C--H of phenols and aromatics (1264
cm\textsuperscript{-1}), and Si--O in clay minerals (995
cm\textsuperscript{-1}). This indicates the components involved in the
binding of salt compounds in soil. The main auto-peak, Ф (3700, 3700)
cm\textsuperscript{-1} (Fig. 7c), showed that the O--H stretching in
clay minerals was involved in the binding of salt-related compounds (Sun
et al., 2019). The off-diagonal peaks in the synchronous map exhibited
correlated signals (Yu et al., 2012). The negative peaks at Ф (995,
1548) and Ф (995, 1264) cm\textsuperscript{-1} indicate that the
intensity of Si--O increased while that of N--H/C--N and C--OH/C--H
decreased. The negative cross-peaks at Ф (2303, 3717) and Ф (2303, 3642)
cm\textsuperscript{-1} suggest that the intensity of the CO--H
stretching in carbohydrates changed directions, to that of O--H in clay
minerals.

The asynchronous maps were anti-symmetric, which could provide
information on the sequential order of functional groups when combined
with analysis of the synchronous maps. In the range of 1500--650
cm\textsuperscript{-1} (Fig. 7b), there were four main negative peaks at
\selectlanguage{greek}Ψ \selectlanguage{english}(995, 1680), \selectlanguage{greek}Ψ \selectlanguage{english}(995, 1502), \selectlanguage{greek}Ψ \selectlanguage{english}(995, 1451), and \selectlanguage{greek}Ψ \selectlanguage{english}(995, 1241).
According to Noda and Ozaki's principle (Noda and Ozaki, 2005; Noda,
2018), the sequence of binding affinities of bands with salt-related
compounds followed the order: 995 - 1680 cm\textsuperscript{-1} and 995
- 1502 cm\textsuperscript{-1}. This means that the binding of functional
groups in soil with salt-related compounds followed the sequence: Si--O
\textgreater{} C=O and Si--O \textgreater{} C=C. Similarly, the negative
peaks at \selectlanguage{greek}Ψ \selectlanguage{english}(893, 995) and \selectlanguage{greek}Ψ \selectlanguage{english}(760, 995) indicated that the binding
affinities of C--H (893 cm\textsuperscript{-1}) and
NH\textsubscript{2}(757 cm\textsuperscript{-1}) with salt-related
compounds were prior to that of Si--O. The main off-diagonal peaks
distributed in the region of 4000--3000 cm\textsuperscript{-1} (Fig. 7d)
were related to the O--H/N--H in clay minerals, water, alcohols,
phenols, carboxyl groups, and hydroxyl groups (Ma et al., 2019).

\textbf{3.5. FTIR-ATR --LIBS Heterospectral 2DCOS}

In addition, 2D heterospectral correlation maps were used to examine the
covariation between bands in FTIR-ATR and LIBS spectra. A molecular and
atomic 2D heterospectral correlation map was used, as this could reflect
the functional compounds at positive/negative peaks derived from the
same origin (Yu et al., 2012).

In the synchronous map (Fig. 8), the negative cross-peaks were located
at 995 (Si--O in clay mineral) and 4000--2940
cm\textsuperscript{-1}(O--H in clay mineral) in the FTIR-ATR spectra and
at 517 (Mg I), 558 (Ca I), 588 (Na I), and 766 (K I) nm in the LIBS
spectra.

\textbf{4. DISCUSSIONS}

From the element distribution in different soil samples, it can be
concluded that i) the distribution trends of Mg and Ca were similar in
each column, which was consistent with the highly significant
correlation between Mg and Ca (\emph{r} = 0.90\textsuperscript{***}).
The distribution of Na was partly similar to that of K, with a
correlation coefficient of 0.77\textsuperscript{***}. This means that Mg
and Ca might be associated with the minerals in the samples. ii) The
color bar on the right in Fig. 6 indicates the uniform intensity for the
distribution of each element. It can be seen that K distributions in the
C1 sample were much higher (intensity value larger than 5500 a.u.) than
those of the other samples. Meanwhile, for the samples with the same
salt content, the K distribution was higher in the samples with a lower
SOM content. For instance, the K distributions in C1 (SOM 3.53 g/kg), C4
(SOM 5.60 g/kg), and C6 (SOM 7.52 g/kg) were higher. The SOM was
negatively correlated with the salt content (\emph{r} = -0.07) and
K\textsuperscript{+} + Na\textsuperscript{+} (\emph{r} = -0.14). iii)
The distribution of elements in samples with lower SOM contents (columns
C1, C4, and C6) was mainly in the top and middle areas, while the
samples with higher SOM contents showed greater distributions of
elements in the middle and bottom areas, especially for samples with low
and medium salt contents (columns C2 and C3). According to FTIR--PAS
spectra, the SOM was a function of depth (Du et al., 2014). The surface
layer of SOM in minerals or the exterior of aggregates is the interface
between the soil and atmosphere. Alternatively, soil pores and SOM form
heterogeneous coatings on the mineral surface via the sorption process
(Du et al., 2014; Kleber \& Johnson, 2010). Thus, it could be deduced
that the SOM layer outside the mineral aggregate was thinner in soil
with a low SOM content, and the laser beam could ablate the mineral
after one or two shots. Therefore, Mg, Ca, Na, and K can be detected and
described at the top of the microscale (C1, C4, and C6). However, when
the SOM layer was thicker, the first laser shot was potentially focused
on organic materials. Therefore, the mineral aggregates would be ablated
and monitored from the second shot (C2 and C3).

As a result of FTIR-ATR Heterospectral 2DCOS maps, microscale
differences in the binding of minerals and organic matter could be
illustrated, and the intra-hydrogen bonds in silicate groups were
stronger than those of organic functional groups, such as C=O and C=C,
but weaker than N--H and NH\textsubscript{2} when combined with
salt-related compounds (J. Xiao et al., 2018). Furthermore, from the
FTIR-ATR--LIBS Heterospectral 2DCOS maps, it was revealed that the
intensities of Mg, Ca, Na, and K were not closely associated with the
Si--O and O--H in clay minerals. Therefore, the elements did not
originate from clay mineral compounds (Duarte et al., 2015;
Forouzangohar et al., 2013; Yu et al., 2012). Additional positive peak
correlations between 850--680 (C--H bending vibration in aromatics) and
2600--1100 cm\textsuperscript{-1} (organic functional groups) suggested
that Mg, Ca, Na, and K partially originated from organic compounds
decomposition (Forouzangohar et al., 2013; Ruan \& Zhou, 2008; Yu et
al., 2012) in saline soil in Hetao Area in China.

\textbf{5. CONCLUSIONS}

In this study, six soil samples with different salt and SOM contents
were analyzed using LIBS, FTIR-ATR, and 2DCOS methods. The main
functional groups and salt-related metal elements were analyzed from
FTIR-ATR and LIBS spectra. The results revealed that the spatial
distribution of Mg and Ca showed a significant correlation, while Na and
K also showed a significant correlation. In samples with a lower SOM
content, Mg, Ca, Na, and K were distributed at the top of the ablation
area. However, the distributions broadened as the SOM content increased.
In samples with a lower SOM content, the SOM layer outside of the
mineral aggregate was thinner, nd the laser beam could ablate the
mineral after one or two shots. The hydrogen bonds in silicate groups
were stronger than those of organic functional groups, such as C=O/C=C,
when combined with salt-related compounds. Mg, Ca, Na, and K were not
originally from clay mineral compounds, but did partially originate from
organic functional groups.

\textbf{CONFLICT INTEREST}

The authors declare no potential conflict of interest.

\textbf{ORCID}

Du Changwen 0000-0002-9064-3581

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\textbf{TABLE 1} Soil properties of the total soil samples\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
& Mean & Medium & Standard Deviation & Range\tabularnewline
\midrule
\endhead
SOM g/kg & 10.49 & 10.47 & 3.35 & 3.36--17.70\tabularnewline
pH & 8.16 & 8.19 & 0.24 & 7.58--9.55\tabularnewline
Salt g/kg & 2.42 & 1.62 & 2.43 & 0.84--17.11\tabularnewline
Ca\textsuperscript{2+} mg/kg & 144.89 & 100.2 & 195.92 &
40.08--2284.48\tabularnewline
Mg\textsuperscript{2+} mg/kg & 78.33 & 48.6 & 105.69 &
12.15--911.25\tabularnewline
K\textsuperscript{+} + Na\textsuperscript{+} mg/kg & 527.08 & 299.00 &
740.34 & 115.00--5945.50\tabularnewline
CEC cmol/kg & 0.71 & 0.45 & 0.81 & 0.32--5.27\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure Legends}

\textbf{FIG. 1} Geographical distribution of the soil sample sites

\textbf{FIG. 2} Laser shot points on a soil sample pellet

\textbf{FIG. 3} Average FTIR-ATR spectra of the soil samples

\textbf{FIG. 4} Average LIBS spectra of the soil samples

\textbf{FIG. 5} (a) Correlations between the properties of the soil
samples. (b) Correlations between the intensities of the LIBS spectra of
six selected samples.

\textbf{FIG. 6} Spatial distribution of Mg, Ca, Na, and K based on the
LIBS spectra intensity in an area of 2 \selectlanguage{ngerman}× 2 mm, with the depth in
micrometers. Column C represents the distribution of elements in each
sample. C1: salt 1.08 g/kg and SOM 3.53 g/kg; C2: salt 1.09 g/kg and SOM
11.38 g/kg; C3: salt 9.49 g/kg and SOM 13.80 g/kg; C4: 9.55 g/kg and SOM
5.60 g/kg; C5: salt 10.17 g/kg and SOM 13.20 g/kg; and C6: salt 16.10
g/kg and SOM 7.52 g/kg

\textbf{FIG. 7} Synchronous (a, c) and asynchronous (b, d) FTIR--ATR 2D
salt content--dependent correlation maps generated from 2500--650
cm\textsuperscript{-1} and 4000--2000 cm\textsuperscript{-1},
respectively

\textbf{FIG. 8} Synchronous (a) and asynchronous (b) FTIR-ATR --LIBS
Hetero 2DCOS generated from salt content--dependent maps. Red and blue
colors represent positive and negative correlations, respectively. The
darker color indicates a stronger correlation than the lighter color\selectlanguage{english}
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