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. 2024 Nov 3;14:26499. doi: 10.1038/s41598-024-73526-1

Methods to identify and distinguish the effects of weathering and landslides on sediment granulometry

Király Csilla 1,2, Jakab Gergely 1,2,3, Páles Mariann 3, Gresina Fruzsina 1,2,3, Szeberényi József 1,2,✉, Viczián István 1,2, Kónya Péter 4, Falus György 4, Cseresznyés Dóra 4, Varga György 1,2, Kovács István 5, Szalai Zoltán 1,2,3
PMCID: PMC11532404  PMID: 39489754

Abstract

Urbanization has resulted in the widespread development of built-up areas, often without considering the local geology and geomorphology. To improve risk assessments related to landslides, it is essential to determine the physical and chemical properties of sediments. The aim of this study is to exemplify an already mobilized and reworked layer based on granulometric properties of the sediments and characterize the chemical and physical properties which have changed during or after the mass movement. Ten red clay layers were sampled near Kulcs (Hungary) from a sliding surface and its environment. We measured grain size distribution, major element and modal composition, furthermore, conduct particle shape analysis, respectively. Grain size distribution suggest that samples from the sliding surface are weathered, with the exhibiting reworking characteristics. We calculate the weathering index from the major elements, which, in combination with the particle shape analyses of the silt fractions, indicate that the morphological parameters of the samples are in relation with weathering index. According to our results, while a fresh sliding event leaves marks on the granulometric properties, the main process which affects the grain morphology is the chemical abrasion (post the mass movement) and not the landslide event.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-73526-1.

Keywords: Landslide, Granulometry, Sliding surface, Chemical index of alteration, Grain morphology, Weathering

Subject terms: Geomorphology, Mineralogy, Petrology, Natural hazards, Geochemistry

Introduction

Sediment movement is one of the most essential land formation processes. Larger particles such as core sand are mostly delivered by water (fluvial transport), whereas smaller particles such as silt (2–63 μm) are generally driven by wind (aeolian transport)1. The type of transportation environment, along with the distance and energy of transportation, are also important factors that affect particle morphology and grain size2. In this study, we focus on smaller grain sizes, which are primarily of aeolian origin, such sediment is the loess-paleosol sequence. This focus is due to the fact that smaller grain sizes have a higher specific surface area, which increases the impact of various weathering processes3. Furthermore, loess-paleosol sequences are widespread worldwide (e.g., in China4,5 and in Europe6,7). Due to their high clay content, impermeable (or less penetrable) layers, such as red clay or paleosol, can serve as sliding surfaces within the loess sequence. Landslides often occur along these critical boundary layers, which have lower shear strengths and more prevalent weathering processes than do the surrounding layers8,9. The risk of landslides is greater when the river deepens its valley into such unconsolidated strata, creating steep valley sides and cliffs. This natural process can be characterized by a complex set of geological/geomorphological parameters.

The stability of bluffs is a dynamic, nonlinear problem that depends on the heterogeneity of the paleosol-loess layers, weathering conditions, climate-rainfall amount, groundwater level changes, vegetation, etc10,11. The increased load from human infrastructure redounds landslides. Moreover, landslides severely impact the biosphere, infrastructure, and economy. As such, it is essential to understand the key drivers and mechanisms of landslides.

However, why are the given layer slides questionable? The particle size distribution and morphological properties of potential sliding surfaces may help explain this phenomenon and are likely useful for predicting the occurrence of sliding events. Primary loess undergoes extensive chemical and physical changes during postdepositional modifications12–17. These alterations include an increase in clay fractions due to chemical weathering18,19, a fine-particle mixture of windblown material20,21, and loess dominated by silt-size particles. Clay mineral accumulations with < 2 μm particle fractions can be observed due to physicochemical weathering processes near the sliding surface. Furthermore, the shape of the particles is strongly influenced by transport processes2 such as aeolian, fluvial, or sliding events22 and post depositional effects23.

Our investigations were carried out at a site that (is frequently encountered worldwide) exemplifies a typical geomorphological and geological complex of parameters. A large area of the Danube River in Hungary is covered by sequences of glacial loess and interglacial paleosol sediments. This loess region is particularly exposed to mass movements24–26, as with the increasing population urbanization is increasing, which influences the water balance of the bluff along the right bank of the Danube and potentially destabilizes the clay layers.

We collected samples from ten layers within the loess-paleosol sequences along the Danube, near Kulcs, Hungary (Fig. 1). Here, the porous, loose sedimentary loess layers overlie the relatively impermeable red clay. In case of the studied red clay the most significant transportation process was aeolian, and the deposited material underwent weathering during the soil formation processes27. The weight of loess can increase significantly due to the moisture content, which in turn enables slip along the plastic clay surface. Geographical locations such as Kulcs (Hungary) are influenced by fluctuations in water levels28, the submergence of water in the river, and the congestion of groundwater29.

Fig. 1.

Fig. 1

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Study area, background and selected samples. (a) Location of the investigated area (red dots), where yellow dots indicate landslide areas; (b) Digital elevation model of the investigated area with the overlaying main sediments and main geomorphological characteristics; (c) Geological section of the high bluff at Kulcs. Legend: (1) young loess, (2) young sandy loess, (3) sand, (4) fossil brown soil, (5) reddish brown paleosol, (6) old loess, (7) old clayey loess with deposition horizons of marshes, (8) sandy aleurite, (9) clayey aleurite, (10) red clay, 11. Clay (Zagyva Formation of the Dunántúli Formation Group, Pannonian Sediment), 12. reworked debris slope material, 13. ground water horizons, 14. slip face, and arch of collapses; 15. inferred fault, 16. spring; (d). Photos from the sampling situations. (d). Photos from the sampling situations. The Fig. 1(a) and (b), and (c) were created using CorelDRAW Graphics Suite 2017 and Surfer 8 software, respectively.

Therefore, prior to new developments, it is important to study the stability of bluffs. However, over time, the macromorphology of the sliding surface will be eliminated. Complex geochemical and granulometric studies could help to identify signs of landslides. With the results obtained, we aim to detect the physical and chemical processes affecting these particles and to quantify reworking processes in red clay systems.

Study area and samples

The investigated area is located in Kulcs (Hungary, Central Europe), where two regions with significantly different geological and geomorphological conditions (the Mezőföld and Danubian Plains of the Great Hungarian Plain) meet on two sides of the Danube River (Fig. 1a). The east bank is an alluvial plain characterized by Late Pleistocene and Holocene deposits and landforms of the Danube, while the west bank is consisting of steep, almost vertical bluffs made up 40–50 m thick loess and loess-like sediments, interrupted by thick paleosol horizons and sand layers. 30,31, (Fig. 1b). The bluff began to form when the Danube’s former southeast flow direction shifted to its current north-south orientation due to the subsidence of the Baja-Kalocsa depression, which started 30,000 to 40,000 years ago and is located 100–150 km south of Budapest32. This significant shift in the river’s course caused extensive lateral erosion, undermining the edge of the Mezőföld. The bluff of the Danube River has been experiencing landslides, slow surface creeps, and rotational slides for the last few decades. The recurring mass movements in 1964, 1966, 1977, 2006, 2011 and 2013 made the landslides of Kulcs a relevant research area30,31,33.

Although the river Danube typically flows in a stable, regulated, and incised channel, elevated water levels can erode the base of these deposits. Landslides are often triggered by hydrological changes in aquifers located between the paleosol and red clay layers. The lowermost layer above the red clay supplies a series of springs along the riverbank. When groundwater flow to the Danube in these aquifers is obstructed—by high river levels or landslide deposits—the groundwater table rises34, resulting in increased water content at the foot of the deposits on the bluff. This leads to the formation of more plastic clay layers (slide planes) and heavier sediments above them. Landslides can be triggered by abrupt changes in the hydrological conditions of sediment layers due to shifts in precipitation, Danube discharge, groundwater flow, or extreme weather events35. studied the connection among landslides, precipitation and level of the river. According to their statistical analyses, the correlation among these factors is not strong enough. Of the twelve detected changepoint horizons, only two overlapped with landslides (in 1977 and 2010), and only one case showed a connection between changes in the level of the Danube and landslides (in 1977). Furthermore, the study analyzed the geochemical changes in loess due to varying water inputs and examined the impact of human activity in the area. Human activity, including water supply failures, inadequate sewage networks, or improper drainage, can also significantly increase the risk of landslide events. The sliding events occurred in the Pliocene and Pleistocene layers of the sediment (loess and loess-like) sequence. The upper and middle sections of the succession are divided by early and middle Pleistocene reddish fossil soils (Fig. 1c), while the lower part of the sediment succession is the Tengelic Red Clay Formation (Fig. 1c, ~ 40 m depth). The latter provides evidence of Pliocene red clays (2.58 Ma)39 in Hungary, which can be basically subdivided into a younger illite-smectite-rich Tengelic Member and an older kaolinite-rich Beremendi Member36. Notably, only the Tengelic Member has been exposed in the study area. This red clay is known from several locations in Hungary and acts as a sliding surface in four bluffs along the Danube (Érd-Ercs, Kulcs-Dunaújváros, Dunaföldvár-Dunakömlőd, and Báta-Dunaszekcső)31. Red clay formed as a post-Pannonian sediment, which is a complex consisting of several paleosol layers37. The base of the entire Pliocene–Pleistocene sediment succession was deposited in the upper Miocene sandy strata25.

The main sections of the investigated sedimentary units were exposed during a landslide on38 April 2013, which was preceded by several movements. Landslides occurred near the riverbank in the hazardous area of Kulcs. According to the field description, the soil did not play a role during the landslide. The mass movement was during a sliding surface, which is the Tengelic Red Clay Formation31,39. During the stabilization process (in 2014), a transect was created that exposed a red clayey layer38. Sampling and preliminary examinations were performed by Udvardi et al.24,39. for comparison purposes. Samples of the following red clay samples from the Tengelic Red Clay Formation were collected from three different sampling sites in Kulcs. Samples were taken from the recently moved landslide body, which contained the sliding surface (k8-k9) and its surrounding layers above (k10) and below (k5, k6, k7) the sliding surface (Table 1; Fig. 1d). According to the lithology, k5 and k6 are loess layers, and k7 is red clay (Table 1). Furthermore, samples were also collected from the riverside (k4), approximately 15 m from the landslide. Additionally, transect sampling (k1-k3) was implemented approximately 10 m from the edge of the landslide (Table 1). The fresh sliding surface samples exhibited macroscopic signs of sliding (Fig. 1d), which was not detected in the other two areas (transects or riversides). Nevertheless, water outflow was evident above the transect sample.

Table 1.

Brief description of the studied samples.

Sample name Location Lithology
k1 Transect Calcareous red clay
k2 Transect Red clay (less calcareous)
k3 Transect Calcareous red clay
k4 Riverside (15 m below sliding surface) Red clay
k5 5 cm below sliding surface Loess
k6 10 cm below sliding surface Loess
k7 1 cm below sliding surface Red clay
k8 sliding surface Light red clay
k9 sliding surface Dark red clay
k10 above sliding surface Calcareous red clay
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Results

Particle size distribution of the samples

The samples below and above the sliding surface exhibit a bimodal particle size distribution. However, the sliding surface showed four modalities (Fig. 2). The samples from the riverside had three modii, and those from the transect had 5–6 modii (Fig. 2).

Fig. 2.

Fig. 2

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Grain size distribution curves, where the black line is the reference curve of the Tengelic Red Clay Formation27.

The clay fraction in the samples (< 2 μm) was 11–36 v/v%. The lowest clay fraction was measured below the sliding surface (sample name), and the maximum fraction was measured from the transect area (sample name) (Table 2). An inverse relationship exists between the silt and clay volumes. The ratio of the silt fraction ranged from 45 to 87 v/v% (Table 2). The highest values (values) of silt were obtained from samples below the sliding surface. The amount of sand was 3–21 v/v%. The maximum volume of the sand fraction was detected in samples from the transect area (Table 2). The clay fraction according to the grain size distribution analyses by Horiba decreases from above the sliding surface to 10 cm below the sliding surface. The ratio of the clay fraction from the riverside samples is similar to that from the sliding surface samples. Most clayey samples (data) occurred in the transect area (Table 2).

Table 2.

Grain fractions of the studied samples.

Sample Clay
v/v%		 Silt
v/v%		 Sand
v/v%
Transect k1 36.16 44.76 19.08
k2 21.56 57.60 20.85
k3 22.54 58.10 19.37
Riverside k4 10.46 79.24 10.30
Below Sliding Surface k5 4.13 85.97 9.91
k6 3.54 86.28 10.19
k7 6.42 87.35 6.24
Sliding surface k8 9.21 79.16 11.62
k9 12.45 74.83 12.72
Above sliding surface k10 11.31 85.76 2.93
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Mineral composition of the bulk samples

According to the XRD analyses, the major minerals in the samples are quartz (25–56 m/m%), sheet silicates (17–35 m/m%), carbonates (0–53 m/m%), feldspars (0–12 m/m%), goethite (1–4 m/m%), and the amorphous phase (0–2 m/m%) (Table 3). The main clay minerals are smectite (5–19 m/m%), illite + muscovite (3–11 m/m%), chlorite (0–6 m/m%), and kaolinite (2–3 m/m%) (Table 3).

Table 3.

The modal composition of the samples is m/m%, and t is the trace amount (< 1 m/m%).

sample smectite illite + muscovite kaolinite Chlorite quartz k-feldspar plagioclase calcite dolomite goethite amorph
Transect k1 19 10 1 3 41 1 18 2 4 1
k2 16 5 1 3 56 17 1 1
k3 15 3 3 55 t 1 21 T 1 1
Riverside k4 11 17 1 3 51 3 9 3 2
Below sliding surface k5 9 11 1 5 44 2 7 12 6 2 1
k6 5 8 1 3 22 2 5 49 4 1
k7 12 9 t 4 34 2 6 24 3 4 2
Sliding surface k8 15 11 2 5 30 2 5 26 2 1 1
k9 13 10 2 5 25 2 3 32 5 2 1
Above sliding surface k10 17 10 2 6 25 2 4 27 4 2 1
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Shape distribution of the silt fraction

The morphological parameters (e.g. solidity, convexity, aspect ratio and HS circularity) of the particles on the sliding surface are similar. Those of the samples adjacent to the sliding surface are remarkably similar to those of the sliding surface. However, the morphological details of the riverside samples show slight differences (Table 4). Significant differences in the morphological properties were observed between the other samples and the transect samples. The following properties were more common in the transect samples than in the other samples: HS circularity, convexity, solidity, and aspect ratio (Table 4).

Table 4.

Morphological data of the studied samples. Abbreviations: av is average, sd is standard deviation.

Sample HS Circularity Convexity Solidity Aspect Ratio
av sd av sd av sd av Sd
Transect k1 0.842 0.073 0.990 0.008 0.978 0.015 0.714 0.148
k2 0.840 0.070 0.990 0.008 0.978 0.014 0.713 0.148
k3 0.829 0.071 0.987 0.016 0.971 0.017 0.716 0.133
Riverside k4 0.799 0.085 0.981 0.018 0.963 0.029 0.691 0.137
Below sliding surface k5 0.811 0.068 0.981 0.014 0.963 0.021 0.721 0.135
k6 0.798 0.084 0.978 0.019 0.961 0.024 0.699 0.135
k7 0.813 0.071 0.982 0.015 0.967 0.020 0.727 0.138
Sliding surface k8 0.814 0.082 0.981 0.015 0.965 0.025 0.709 0.139
k9 0.814 0.080 0.980 0.019 0.965 0.025 0.739 0.134
Above sliding surface k10 0.818 0.085 0.985 0.013 0.968 0.022 0.719 0.147
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The grains from below the sliding surface exhibit the least abrasion (k6, HS circularity: 0.8, convexity: 0.98, solidity: 0.96), while the layers above the sliding surface form the most abraded part of the landslide (k10; HS circularity: 0.82, convexity: 0.99, solidity: 0.97). The morphological properties of the riverside surface are also considerably similar to those of the sliding surface (k4 solidity: 0.96, aspect ratio: 0.69, convexity: 0.98). The particle morphology of the transect samples indicates more rounding (k1-k3 solidity: 0.98, aspect ratio: 0.71, convexity: 0.99) than the other morphology types. However, only the solidity properties of the transect samples differed significantly from those of the samples from the sliding surface and riverside (Table 4).

Major element composition

The major element compositions (Al2O3, Na2O, CaO, K2O) of the studied sediments are summarized in Table 5.

Table 5.

Major element compositions of the studied samples in oxide m/m% and the calculated CIA, CN and K weathering indices.

SiO2 TiO2 Al2O3 Fe2O3 MnO MgO CaO Na2O K2O P2O5 A = CIA CN K
Transect k1 66.75 0.69 10.82 4.90 0.04 1.94 14.01 0.22 0.45 0.18 89.93 6.02 4.05
k2 72.89 0.65 10.21 4.33 0.02 1.50 9.56 0.18 0.47 0.17 90.31 5.17 4.52
k3 69.52 0.69 10.26 3.45 0.04 1.81 13.18 0.25 0.61 0.17 87.26 7.08 5.66
Riverside k4 72.80 0.96 13.94 5.53 0.14 1.77 1.28 1.04 2.32 0.22 70.20 17.15 12.64
Below sliding surface k5 62.76 0.82 12.96 4.94 0.06 3.30 11.59 1.08 2.32 0.17 68.10 18.72 13.18
k6 38.40 0.58 7.60 3.47 0.75 2.35 44.76 0.57 1.31 0.21 69.70 17.26 13.04
k7 55.61 0.74 11.47 5.67 0.07 2.87 21.46 0.70 1.22 0.18 75.97 15.31 8.73
Sliding surface k8 56.97 0.76 14.03 4.52 0.07 3.06 17.90 0.65 1.85 0.18 77.23 11.74 11.03
k9 51.68 0.64 11.14 4.66 0.05 3.45 26.67 0.37 1.13 0.19 81.97 9.00 9.03
Above sliding surface k10 52.77 0.65 11.97 5.25 0.09 3.20 24.17 0.49 1.23 0.19 80.32 10.76 8.92
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The analyses indicate that the most weathered samples according to the calculated CIA (Table 6) are from the transect layer with high CIA values (k1, k2, and k3; CIA: 87.26–90.31). The least weathered samples, with the lowest CIA values, are loess materials originating from the sliding surface (k5 and k6, CIA: 68.10–69.70).

Table 6.

Results of correlation analyses among properties that may be connected to weathering processes: morphological parameters, weathering indices, grain size and modal composition. The bold areas indicate a positive correlation (> 0.8), and the unbold areas indicate a negative correlation (<-0.8). Abbreviations: HS Circ: high sensitivity circularity, Conv: convexity, Sol: solidity, AR: aspect ratio, < 2 μm: clay fraction (grain size), Kfs: K-feldspar, plg: plagioclase, fs: all feldspars, sm: smectite, ill: illite, kln: kaolinite, Ch: chlorite, clay: amount of clay minerals. CIA: chemical index of alteration; CN: (Na2O + Na2O)/(Na2O + Na2O + K2O + Al2O3); K: (K/(Na2O + Na2O + K2O + Al2O3).

A CN K HS Circ Conv. Sol AR < 2 μm Kfs plg fs sm ill Kln ch
CN -0.99
K 0.97 0.92
HS Circ. 0.92 -0.88 -0.94
Conv. 0.88 -0.82 -0.91 0.95
Sol 0.91 -0.85 -0.94 0.97 0.98
AR 0.32 -0.31 -0.32 0.30 0.09 0.13
< 2 μm 0.87 -0.83 -0.87 0.87 0.88 0.88 0.05
Kfs -0.77 0.73 0.79 -0.82 -0.71 -0.75 -0.31 -0.68
plg -0.87 0.87 0.79 0.76 -0.55 -0.69 -0.49 -0.68 0.86
fs -0.93 0.91 0.90 -0.90 -0.82 -0.86 -0.29 -0.78 0.92 0.99
sm 0.83 -0.82 -0.80 0.81 0.81 0.79 0.27 0.77 -0.46 -0.44 -0.62
ill -0.83 0.85 0.77 -0.73 -0.62 -0.62 -0.46 -0.43 0.92 0.96 0.87 -0.50
kln 0.35 -0.44 -0.21 0.16 0.12 0.04 0.20 0.18 -0.50 0.82 -0.32 0.34 -0.51
ch -0.13 0.06 0.23 -0.15 -0.24 -0.31 0.56 -0.38 0.00 -0.57 0.20 0.17 -0.02 0.71
clay 0.44 -0.50 -0.34 0.34 0.35 0.35 -0.02 0.52 0.06 0.04 -0.15 0.71 0.83 0.24 0.06
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Discussion

The studied sliding surface in Kulcs is the Tengelic Member of the Tengelic Red Clay Formation38. To test the methodology, first, the main physical and chemical properties of the red clay acquaintance were evaluated.

The grain-size distribution of the Tengelic Red Clay is bimodal. The primary maximum of the curve is approximately 30 μm, and the secondary fraction is approximately 4 μm. The sand fraction is less than 1 v/v% according to20,44. The main minerals in the Tengelic Member of the Tengelic Red Clay Formation are quartz (50–60 m/m%), feldspars (mainly plagioclase), micas and clay minerals. Pyroxene, hornblende, FeO-minerals and amorphous matter are also present. Clay minerals occur as illite, kaolinite smectite and chlorite27. One part of clay minerals is the result of weathering and weathering index (CIA) was calculated from the major element composition44,45. The main major elements are SiO2, Al2O3, Fe2O3, CaO, MgO and K2O. The CIA value is highly variable (64–93, Tengelic Member: 70–80). In the Al2O3-CaO + Na2O-K2O (A‒CN‒K) diagram (the gray area in Fig. 3) The sediments plot parallel to the A-CN line. This suggested that the Ca-Na concentration decreases (dissolved plagioclase) and that the K content increases during weathering processes46.

Fig. 3.

Fig. 3

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Ternary A–CN–K diagram40 of the samples with more important references (upper continental crust41, GAL – global average loess42, PAAS – post-Archaean Australian shale43.

It can be inferred from the particle size analyses that the clay content below the sliding surface is similar to that of the Tengelic Red Clay Formation27. However, the morphologies of the grains in the sliding surface and above the sliding surface exhibit some differences. On the sliding surface, one modifier in the sand fraction appeared, and one maximum in the clay fraction (Fig. 2). Modus in the sand fraction may originated from the loess, and the maximum in clay fraction is the sign of weathering or the effect of grain crushing26. These grain size changes may indicate weathering and reworking processes15. Above the sliding surface, only two modes are observed, one in the clay fraction and one at approximately 10 μm, which may be the result of weathering processes. Nevertheless, the major element compositions (calculated CIA index, Table 5) of the bulk rock indicate that the red clay from the sliding surface and that directly above it is not weathered compared with the Tengelic Member of the Tengelic Red Clay Formation (CIA:70–80)27. However, it should be mentioned that the samples below the sliding surface (CIA: 68–76) are less weathered than the sliding surface and above the sliding surface (CIA: 77–81) (Fig. 3). It should be noted that the samples contain high amounts of calcite (12–49 m/m%), which is not characteristic of the Tengelic Member of the Tengelic Red Clay Formation27. For this reason, the CN values are modified by the amount of calcite, which is questionable when calcite is present in the samples. The presence of calcite is an important issue in the case of mass movements28,47, where cohesion decreases with increasing amount of calcite in red clay. Furthermore, shear strength is related to the amount of calcite. Calcite can dissolve from loess and recrystallize on the sliding surface. According to Király et al.35 the geochemical models from the study area sign calcite precipitation in loess (wet and flood period), however, the site of precipitation may be on the border of clayey layers, such as red clay.

The morphological properties of the particles were not studied in the case of the Tengelic Red Clay Formation; therefore, the results of this study were compared with granulometry results of paleosols from Hungary48. The morphological properties of paleosols from Central European loess sequences Hungary49 indicate that the different particle size fractions (e.g. silt, sand or clay) of the samples have different morphological parameters. This study focused only on the 20–63 μm fraction, which corresponds to the coarse silt fraction49. According to the results, the average solidity and aspect ratio are similar for paleosol samples (solidity: 0.94, aspect ratio 0.71) and samples from the sliding surface and adjacent environment (solidity: 0.96, aspect ratio: 0.72). Nevertheless, a remarkable deviation is observed in the convexity values (paleosol: 0.73, sliding surface and its environment: 0.98). These differences may be the result of landslides or weathering processes. However, different optical properties may also be involved.

Based on the observations of the degree of weathering (CIA) and morphological features (Fig. 4), we propose that the sample above the sliding surface is more weathered than the sample from the sliding surface. Therefore, the least weathered layers are located below the sliding surface (k8-k9). Moreover, the more rounded particles of the silt fraction also originated from above the sliding surface (k10), and the less rounded particles originated from below the sliding surface (k6). This difference was substantiated by the modal composition of the samples. The detrital minerals originate from loess and are composed of quartz, feldspars (K-feldspar, albite), mica, dolomite, and calcite. Feldspars can form smectite and kaolinite during chemical weathering processes in warm climates49. These results could suggest that granulometric properties may indicate landslides in an area. We must note that weathering processes are necessary for mass movements. However, according to recent results, it is questionable which processes caused (weathering or mass movement) the differences between the Tengelice Red Clay Formation and the samples from the sliding surface.

Fig. 4.

Fig. 4

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Positive correlation between weathering indices (CIA) and morphological properties (convexity, solidity, HS circularity). Typical grains can observe in the figure; the data of the selected particles is in Figure_S1.

According to the previous chapter, the samples from the sliding surface differ from those from the Tengelic Red Clay Formation. In this chapter, weathering processes are the focus because we can understand which processes cause differences between the Tengelice Red Clay Formation and recently studied samples.

The less weathered sample (k4, Fig. 3) originates from the riverside and displays physical characteristics similar to those of the sliding surface. However, the grain size distribution of the sample shows some signs of weathering. The CIA values of the riverside samples differ from those of the sliding surface samples (CIA on riverside (k4): 70.2; CIA on sliding surface (k8-k9): 77.23–81.97) (Fig. 3). The CIA values below the sliding surface (sample name) are close to those of the riverside sample, but the riverside sample does not contain calcite (Table 3).

CIA values (data) for the transect samples (sample name) suggest intense weathering. A complex particle size distribution of the transect samples is evident from three models obtained for the sand, clay, and silt fractions (Fig. 5). The morphological properties (HS circularity, solidity, convexity) were found to be related to the CIA (Table 6; Fig. 4). This was attributed to the extensive correlation of morphological properties with post depositional weathering. The mass movement and reworking processes also influence the intensity of weathering during landslides. The texture of red clay can change during reworking processes. E.g. The increasing of the sand/silt ratio can be a proxy of this process. This proxy can be tracked on the grain size distribution curves (Fig. 2). The reworking process may also have an influence on the fluid‒rock ratio due to the compaction, fracturing together with fragmentation50 and the grain size distribution were changed.

Fig. 5.

Fig. 5

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The main morphological properties were calculated with a Morphologi G3ID. From these properties, high-sensitivity circularity, convexity, aspect ratio and solidity were used (Malvern Morphologi G3ID Handbook).

Furthermore, the continuous stress field also changed after the landslide. This process effected the pore pressure 50, which can alter the fluid rock ratio. If fluid-rock ratio change, the balance between rock and pore water will cease and new chemical reactions appear52. These phenomena can be traced by CIA.

Weathered alumina-silicates can be physically disintegrated during mass movement and reworking processes53. This physical decay could cause the increasing of specific surface area54, thus intensifying the weathering of these minerals. Consequently, the change of granulometric and CIA parameters can be a proxy for a potential landslide event in a sedimentary deposit with loess – paleosol/redclay sequences. The continuous temporal monitoring (depends on the weather and hydrological conditions) of these parameters can be a tool to predict the hazard of a landslide event.

According to previous chapters, one aspect of weathering involves the formation of clay minerals from feldspar54. This can lead to a negative correlation (-0.78) between the quantity of feldspar and the clay size fraction55 (Table 6). However, no discernible relationship is apparent when we compare the clay size fraction or feldspar content with the quantity of clay minerals (Table 6). Additionally, the quantity of clay minerals does not correlate with weathering indices or morphological data. This lack of correlation may be attributed to the settling of a substantial amount of clay minerals from the air, containing varying proportions of kaolinite, illite, and chlorite56,57.

Nevertheless, when smectite and illite are individually compared with weathering indices and morphological data, some relationships become evident. Notably, a stronger relationship may arise in the case of smectite, as this mineral is present in every sample. In contrast, illite is absent in four samples (k5, k6, k7, and k10).

Landslides occurrences are connected to various factors, such as geological parameter, distance from the river, land use and precipitation58,59. In the case of Kulcs, the impact of, precipitation, geology and human activity was specifically studied. The results indicated that hydrological properties influence weathering processes35. However, a direct statistical correlation between precipitation and the Danube River’s water level could not be established. Weathering processes play a crucial role in all types of landslides, as they contribute to changes in the mineralogy and stability of the rock60. Furthermore, the studied processes, which connect to the occurrences of landslides also effect to the weathering processes58,59. The factors associated with landslide occurrences also affect weathering processes. Geological parameters determine mineralogy, while precipitation, soil, land use, slope, and proximity to rivers influence hydrological conditions. Chemical weathering results from the reaction between pore water and the mineral assemblage35. It is well-known that the particle shape of sediment is influenced by the transportation of the material2. However, recent findings suggest that these morphological properties can also change during weathering processes. Therefore, studying the morphological parameters of sedimentary rock may help predict a high degree of chemical weathering, which, in turn, increases the potential for landslides.

Conclusion

Our results suggest that the granulometric properties of a sediment layer can help determine reworking and weathering episodes. According to the morphological parameters, solidity exhibited high variability, followed by convexity and HS circularity.

The results substantiate that weathering can increase the abrasion of the samples. Furthermore, the effect of weathering can modify the analyzed parameters after a sliding event (evident from the differences between the transect sample and sliding surface). The results also imply that the geochemical parameters (CIA) and morphological parameters are related. For this reason, the main process that transforms shape properties is weathering, not sliding.

The modal composition is related to the grain size distribution and CIA. Furthermore, the increase in the clay fraction (and clay minerals) is related to the amount of feldspars because of weathering of feldspars to clay minerals.

Overall, fresh sliding events leave a mark in the granulometric properties; we plan to investigate this further in the future. Older sliding effects are not visible because of chemical weathering effects on the granulometric properties.

Methods

The application methods

We approached this problem through an investigation. In our opinion, complex geochemical and granulometric studies, such as those investigating the particle size distribution and morphological properties of potential sliding surfaces, may help identify signs of landslides. The particle size distribution changes in red clay, which can indicate landslides (reworking processes)15 and weathering processes18. The morphological properties of the studied samples were measured by a 2D image analyzer. From the measured HS circularity, aspect ratio, convexity and solidity data, the roundness of the particles was determined. X-ray diffraction was used to determine the mineralogical composition of the studied samples. Furthermore, chemical alteration indices were calculated from the major element compositions, which were studied via ICP‒AES.

Particle size distribution

The number of modi in the particle size distribution (by Horiba) curves may be related to the reworking processes due to interactions between neighboring layers15. The particle size distribution was determined using a Horiba Partica 950-V2 LA laser diffraction particle size analyzer. The wet dispersion was applied with continuous circulation of distilled water (circulation speed, 9/15; agitation speed, 7/15; Horiba User Guide: https://www.horiba.com). The particle size distribution was calculated according to the Mie theory model using nr = 1.33 for water, nr = 1.45 for the real part, and ni = 0.5 for the imaginary part of the complex refractive index values61,62. The calculated particle size fractions were as follows: clay (< 2 μm), silt (2–63 μm), and sand (> 63 μm).

For particle size analysis, the samples were prepared using the following procedure: (1) 10% hydrochloric acid (HCl) was added to the samples to remove carbonate aggregates; (2) 5% sodium pyrophosphate (Na4P2O7) was applied to protect against coagulation of the particles; and (3) the samples were dispersed in an ultrasonic bath.

2D image analysis and Raman spectroscopy

Particle size and shape analyses were performed with a Malvern Morphologi G3-ID instrument equipped with a Kaiser Raman Rxn1 spectroscope. The size, shape, and transparency parameters were determined by Morphologi G3-ID. The instrument allows the measurement of dry powders owing to its integrated air and liquid dispersion unit. Dry powder samples were examined to prevent particle aggregation in the liquid.

At least 3 × 104 individual particles were scanned for the measurement using the 5x magnification lens (0.56 μm pixel size) recommended by63. Based on preliminary attempts, sorting was performed to filter out the granules and incorrectly scanned particles. Four parameters were employed to statistically analyze the shape of the samples. Four parameters were employed to statistically analyze the shape of the samples. Four parameters were employed to statistically analyze the shape of the23,64–66 (Morphologi G3 ID Manual) (Fig. 5): high-sensitivity circularity (HS C), convexity (K), solidity (S), and aspect ratio. Simple statistical indicators such as the mean, standard deviation, and minimum and maximum values were calculated.

The presence of a clay fraction (< 2 μm) may result in the aggregation of particles and the formation of a fine coating on larger particles, thereby modifying the measurement results. Hence, the particles were separated into three particle size fractions (> 63 μm, 20–63 μm, and < 20 μm) by wet sieving. The loess samples, comprising predominantly the coarse silt fraction (20–63 μm), were used for the measurement process.

Modal composition

The mineral composition of red clay samples is necessary for understanding geochemical properties and weathering processes. For this reason, the modal compositions of all the samples were analyzed via X-ray powder diffraction (XRD) (Phillips PW 1730 diffractometer) using a Cu cathode, 20 kV, 30 mA tube current, graphite monochromator, and goniometer with a speed of 2°/min. Prior to the measurements, the samples were powdered to a particle size of < 63 μm. The relative amounts of the phases were determined using XDB Powder Diffraction Phase Analytical software 2.7. Bruker AXS (2018): TOPAS v.6.0.0.9: General profile and structure analysis software for powder diffraction data, Bruker AXS, Karlsruhe, Germany.

Major element composition

The major element composition was determined using a Jobin Yvon Ultima 2 C Inductively Coupled Plasma Atomic Emission Spectroscope (ICP‒AES). During the measurements, the instrument was calibrated with GBW 07111 granodiorite and GBW 07112 gabbro. The measured elements were as follows: Al, Ba, P, Ca, K, S, Mg, Mn, Na, Sr, Si, Ti, and Fe. Weathering indices were calculated from the major element compositions of the samples. The chemical index of alteration (CIA) and the A‒CN‒K [Al2O3-(CaO + Na2O)-K2O] diagrams were plotted according to38,40.

Statistical analysis

Pearson correlation analysis was performed among the properties to determine the relationships between morphological parameters, weathering indices, grain size and modal composition. The cutoff values were defined as 0.8 for a positive correlation and − 0.8 for a negative correlation.

A ternary A–CN–K diagram was created to determine the relationships of our samples with Hungarian red clays, loess sediments, and some more important references worldwide, such as upper continental crust41, GAL – global average loess42, and PAAS – post-Archaean Australian shale43. This method was developed by40.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (266.7KB, docx)
Supplementary Material 2 (3.6MB, xlsx)
Supplementary Material 3 (5.1MB, xlsx)
Supplementary Material 4 (6.4MB, xlsx)
Supplementary Material 5 (7.9MB, xlsx)
Supplementary Material 6 (6.1MB, xlsx)
Supplementary Material 7 (17.5MB, xlsx)
Supplementary Material 8 (8.6MB, xlsx)
Supplementary Material 9 (17.5MB, xlsx)
Supplementary Material 10 (3.9MB, xlsx)
Supplementary Material 11 (3.4MB, xlsx)
Supplementary Material 12 (63.9KB, xlsx)

Acknowledgements

The project was funded by Hungarian Scientific Research Fund FK128230 and by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.

Author contributions

Cs. K. measuring, interpretation, writingG. J. interpretation, writingM. P.; measuringF. G.; measuring, writing, visualizationJ. Sz.; writing, visualization, corresponding authorI. V., writingP. K., measuringGy. F.; interpretationD. Cs.; writingGy. V.; interpretationI.K., sampling, visualizationZ. Sz. interpretation.

Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Supplementary Materials

Supplementary Material 1 (266.7KB, docx)
Supplementary Material 2 (3.6MB, xlsx)
Supplementary Material 3 (5.1MB, xlsx)
Supplementary Material 4 (6.4MB, xlsx)
Supplementary Material 5 (7.9MB, xlsx)
Supplementary Material 6 (6.1MB, xlsx)
Supplementary Material 7 (17.5MB, xlsx)
Supplementary Material 8 (8.6MB, xlsx)
Supplementary Material 9 (17.5MB, xlsx)
Supplementary Material 10 (3.9MB, xlsx)
Supplementary Material 11 (3.4MB, xlsx)
Supplementary Material 12 (63.9KB, xlsx)

Data Availability Statement

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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