Peng Chen1,2,3
Jiahao Qi- 1Langfang Integrated Natural Resources Survey Center, China Geological Survey, Tianjin, China
- 2Lishu Field Scientific Observation and Research Station for Earth Critical Zone on Black Soil, China Geological Survey, Tianjin, China
- 3School of Water resources and Environment, China University of Geoscience (Beijing), Beijing, China
Introduction: The Songnen Plain is a significant black soil region in China, where the sedimentary processes and provenance of sediments are crucial for reconstructing the regional environmental evolution.
Methods: In this study, end-member modeling analysis coupled with geochemical proxies of major and trace elements was applied to systematically investigate the grain-size distribution and sediment sources in the northwestern margin of the Songnen Plain. Paired samples from surface (0.2 m) and subsurface (1.0 m) layers across different geomorphic units were compared.
Results: Four to six distinct grain-size end members were identified, with their number and characteristics varying across geomorphic units.
Discussion: These include long-range aeolian dust (EM1), mid-range suspended aeolian material (EM2), mid-to short-range wind-sorted sand (EM3), fluvial suspended and channel deposits (EM4-5), and slope mud-flow deposits (EM6). Aeolian end-members (EM1-3) dominate both surface and subsurface sediments (>71%), whereas fluvial components (<29%) were mainly derived from the Greater Hinggan Mountains and local sources. Provenance analysis further indicates that the Horqin Sandy Land, rather than the Songnen Sandy Land, served as the primary aeolian source. This study provides new sedimentological evidence for understanding the provenance and depositional mechanisms on the northwestern margin of the Songnen Plain.
1 Introduction
Grain size distribution constitutes a fundamental sedimentological parameter, profoundly shaping the hydrological, gaseous, and energy fluxes within terrestrial environments. As an intrinsic archive, granulometric characteristics encapsulate vital information regarding sediment transport regimes, depositional processes, and sediment provenance, thereby serving as a robust proxy for reconstructing paleoenvironmental conditions and sedimentary dynamics (Hartmann and Flemming, 2007; Li et al., 2004; Visher, 1969; Xiao and Li, 2006). The heterogeneity of grain size is predominantly dictated by hydrodynamic sorting, sedimentary mechanisms, and post-depositional alterations, reflecting the complexity of sediment pathways and depositional settings. In recent decades, grain size analysis has evolved from reliance on conventional statistical descriptors and curve-fitting approaches (Blott and Pye, 2001; Ding et al., 2005; Doeglas, 1968; McLaren, 1981) to the adoption of advanced quantitative techniques such as end-member modeling. This methodology enables the deconvolution of complex grain size spectra into statistically independent components, each corresponding to discrete sediment transport and depositional processes (Blott and Pye, 2012; Liu Y. et al., 2021; Paterson and Heslop, 2015; Weltje and Prins, 2007; Zhang et al., 2020). The advent of dedicated analytical platforms—such as AnalySize, built upon the MATLAB environment—has significantly broadened the applicability of end-member modeling in diverse sedimentary contexts, including fluvial, lacustrine, aeolian, and loessic systems (Dietze et al., 2012; Hateren et al., 2018; Liu Y. et al., 2021; Paterson and Heslop, 2015; Seidel and Hlawitschka, 2015; Yu et al., 2016; Yuan et al., 2024; Zhang et al., 2020; Zhao Q. et al., 2024; Zhou et al., 2023).
The Songnen Plain, situated in northeastern China, represents one of the most extensive black soil regions globally, underpinning national food security due to its exceptional fertility and intensive agricultural exploitation (Han and Li, 2018; Wang et al., 2022; Yang et al., 2023). The ongoing preservation and sustainable management of these mollisols are imperative for maintaining agroecosystem productivity and regional ecological equilibrium. However, the sedimentological framework of the Songnen Plain remains insufficiently constrained, particularly in terms of spatial variability, genetic heterogeneity, and sediment provenance across its diverse geomorphic units. Previous investigations have predominantly targeted monogenetic sediment sources or isolated stratigraphic profiles, with limited integration of spatially distributed surface and subsurface datasets (Cui and Zhao, 2019; Hou et al., 2023; Sun J. et al., 2022; Sun Y. et al., 2022; Zhang and Jiao, 2020). This has resulted in a fragmented understanding of sediment transport pathways, depositional environments, and the evolutionary trajectory of black soils, especially within the northwestern sector of the plain, where geomorphic complexity and sedimentary dynamics are pronounced.
To bridge these knowledge gaps, the present study implements a paired-sample strategy, systematically collecting and comparing surface (0.2 m) and subsurface (1.0 m) sediment samples from four representative geomorphic units within the Jalaid Banner sector of the Songnen Plain. By integrating high-resolution grain size end-member modeling with comprehensive major and trace element geochemical fingerprinting, this research aims to (1) delineate granulometric patterns, (2) elucidate sediment transport and depositional mechanisms, and (3) resolve sediment provenance across contrasting geomorphic settings. The outcomes are expected to enhance our understanding of sediment transport pathways and the formation mechanisms of black soils in the Songnen Plain, providing a scientific basis for future research on regional sedimentary processes and supporting sustainable soil management strategies within this ecologically and agriculturally significant area.
2 Study area
The study area is located within Jalaid Banner (121°19′00″∼ 123°37′57″E, 46°04′11″∼ 47°20′54″N), one of Inner Mongolia’s “Eastern Four League” regions, covering approximately 11,116 km2. Geographically, Jalaid Banner occupies a transitional corridor between the Greater Hinggan Mountains and the Songnen Plain (Figure 1A). Structurally, it is positioned along the eastern flank of the Great Hinggan Uplift, forming part of the third uplift belt within the Northeast China Xinhua Xia tectonic framework, and bordered to the east by the Songliao Depression. The regional geological architecture was initially shaped by the Hercynian orogeny and subsequently influenced by later tectonic events and multiple Cenozoic sedimentary processes, which collectively contributed to episodic uplift, subsidence, and erosion. Bedrock exposures predominantly consist of Paleozoic to Mesozoic terrestrial volcanic assemblages, encompassing a spectrum from mafic to felsic compositions, interspersed with metamorphic lithofacies. Holocene surficial deposits are mainly represented by fluvial sands, gravels, clays, and aeolian fine sands, reflecting dynamic sedimentary regimes in the recent geological past.
Climatically, the area experiences a temperate continental monsoon climate, with a mean annual temperature near 3.1 °C and average yearly precipitation around 400 mm. Both seasonal and interannual variations in rainfall are significant, with precipitation in wet years reaching up to 3.3 times that of dry years. Flooding events are temporally concentrated, underscoring pronounced hydrological variability. Geomorphologically, Jalaid Banner spans the transition from the eastern foothills of the Greater Hinggan Mountains to the Songnen Plain, with elevations decreasing progressively from northwest to southeast. The landscape comprises four principal landform categories: low hills, gullies, platforms, and plains (Figures 1B). Dominant soil types—dark brown, meadow, and chestnut soils—collectively account for nearly 80% of the land surface, each indicative of distinct pedogenic and environmental settings. Quaternary sedimentary cover is comparatively thin, varying between 2 and 50 m in thickness (Hu, 1993). Hydrologically, the Chuo River, a major tributary of the Nenjiang River, traverses the region for approximately 205 km, functioning as a primary conduit for sediment transport. Long-term monitoring reveals an average annual suspended sediment yield of 411,400 t, an erosion modulus of 26.47 t/(km2·a), and a mean sediment concentration of 0.183 kg/m3 (Huang and Cao, 2015).
Figure 1. The geographical location of the study area (A) (Liu et al., 2015) and sampling locations in different geographical units (B).
3 Methods
3.1 Sample collection
The study area (Jalaid Banner) was divided into four typical geomorphic units: low mountain and hilly areas, valley areas, platform areas, and plain areas. These units were mainly delineated based on the regional digital elevation model (DEM) and Jalaid Banner Chronicle (Hu, 1993). This classification comprehensively considered key geomorphic indicators such as altitude, slope, surface morphology, and sedimentary processes, and was verified and locally adjusted during field investigations to ensure that each unit has clear sedimentary environment representativeness.
Within each geomorphic unit, the sampling points are distributed in accordance with the principle of seeking spatial representativeness within feasible limits. Due to the limitations imposed by natural conditions (such as steep terrain, vegetation coverage, and poor accessibility) and actual operational capabilities, the sampling points were not arranged in a completely regular grid pattern (as shown in Figures 1B). During the actual operation, we mainly selected typical locations along the terrain gradient, river valley direction, and accessible routes to conduct sampling, aiming to reflect the sedimentary characteristics of each unit. Considering the varying distribution areas of different geomorphic units, we appropriately increased the sampling point density in plains and platform areas, and focused on representative sections in low mountains and valleys. All sampling points are marked on the geographical base map and overlaid with geological and topographic maps for verification to ensure that the main sedimentary types of each topographic unit are covered.
Furthermore, during the sampling process, we collected samples from two different depth layers, surface (0.2 m) and subsurface (1.0 m). The surface layer (0.2 m) represents modern sedimentation, while the subsurface layer (1.0 m) is considered to reflect Holocene or Late Pleistocene deposition (Cui et al., 2021), allowing for comparison of depositional stability over time.
During July and August 2023, systematic field campaigns were undertaken across representative geomorphic units (see Figures 1B). We used backpack drills (TETHYS-XY10) to collect core samples, and collected 942 grain size samples and 353 geochemical samples from 0.2 m to 1.0 m underground layers respectively. Detailed methodologies and sampling site distributions are presented in Table 1.
Table 1. Sampling situation of each geomorphic unit.
3.2 Grain size analyses
The grain size samples were sent to the Key Laboratory of Quaternary Chronology and Hydrological Environment Evolution of China Geological Survey for determination by the Mastersizer 2000 laser grain size analyzer (Malvern Instruments, United Kingdom) with a measurement range of 0.02∼2000 μm. The experimental steps followed the pretreatment steps for sediment grain size testing (Konert and Vandenberghe, 1997): (1) Sample weighing: the required amount of untreated sample was weighed. (2) Removal of organic matter: 1.5∼2.0 g of sample was placed in a glass beaker, treated with 10% H2O2, and heated on a hot plate. Additional H2O2 was added as needed until bubbling ceased and the solution became pale yellow or colorless. (3) Remove carbonate: An appropriate amount of 10% HCl was added, and the mixture was heated (T < 80 °C) until bubbling stopped. (4) Neutralization: The sample was rinsed repeatedly with distilled water, allowed to stand for 24 h each time, and the supernatant was decanted. This process was repeated until the solution reached neutral pH. (5) Dispersion: 10 mL of 0.05 mol/L sodium hexametaphosphate ((NaPO3) 6) was added as a dispersant, and the mixture was ultrasonicated for approximately 10 min. During measurement, the obscuration was maintained at 10%–20%. Each sample was measured in triplicate, and the average value was used for data analysis. The granularity components were obtained using Mastersizer Software (version 5.61). To ensure comparability across samples of different origins and to comply with the instrument’s measurement range, samples were sieved prior to analysis, and only fractions smaller than 2000 μm were used for measurement.
3.3 Granularity statistics and end-member analysis methods
The Udden-Wentworth grade scale (Udden, 1914; Wentworth, 1922) was adopted for grain size classification. Grain size statistical parameters, including mean, median, mode, standard deviation (σ), skewness (Sk), kurtosis (KG) were calculated using the graphical method proposed by Folk and Ward (1957).
According to previous research (Hateren et al., 2018), the AnalySize model provides high accuracy in end-member extraction. Therefore, after calculating grain size parameters, the AnalySize package was employed for end-member analysis. End-member analysis of sediments primarily involves nonparametric and parametric methods. The nonparametric approach selects a characteristic grain-size combination based on the driving forces and transport processes inferred from individual datasets. However, it does not account for the sediment’s geological and geomorphic context. This limitation often results in a poor fit between the decomposed grain-size frequency distribution curves and the modeled data, thereby failing to adequately represent the intrinsic sedimentary dynamics (Cheng et al., 2018). In contrast, parametric methods apply nonnegative matrix factorization to grain-size data, building upon the nonparametric foundation. This approach addresses the aforementioned shortcomings by producing grain-size components that more accurately reflect sedimentary dynamics and transport mechanisms. Consequently, the resulting frequency curves achieve a better fit, and the separated endmember components more reliably indicate sedimentary sources, transport media, and processes (Bai et al., 2020; Zhang et al., 2020). We run the Analysize master’s analysis package (version 1.2.2) in MATLAB 2022b and select Gen. Weibull method for the parameterized endmember analysis.
Modeling procedure and parameterization are as follow (Hateren et al., 2018): (1) Input the granularity data of samples. (2) Determination of the number of end members (q): Models were run sequentially for q values ranging from 2 to a maximum of 10. The optimal q was determined by integrating the following criteria: Overall fitting goodness (requiring R2 > 0.95), end member independence (angle deviation <5°, low correlation of main end members), and geological rationality (the extracted endmember number should be no less than the number of main peaks in the sample’s particle size frequency curve). After comprehensive judgment based on these criteria, the final model was run using the parametric (Generalized Weibull) end-member fitting mode. Through iterative optimization, the algorithm simultaneously solved for the set of end-member grain-size distribution curves and their proportional contributions to each sample.
3.4 Geochemical samples tests
Major elements concentrations were determined by X-ray fluorescence spectrometry (XRF) using the powder pellet method at the Experimental Center, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. (1) Preparation of powder pellets: 4.0000 g (±0.0001 g) of the sieved sample was thoroughly mixed with 0.9000 g of microcrystalline cellulose binder. The mixture was pressed into a 32 mm diameter pellet at 30 MPa for 60 s to obtain a smooth, crack-free surface. (2) Instrument parameter settings: A wavelength dispersive X-ray fluorescence spectrometer (XRF) was employed. The diffraction crystal and detector were selected according to the characteristic spectral lines of the elements analyzed. The tube voltage was set at 40 kV, the tube current at 50 mA, and the scanning time was 20 s per element. The instrument was calibrated using certified reference substances (such as the GBW series of soil standard samples) to establish the working curve, with an experimental error of less than 3%.
Trace elements were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). (1) Acid Digestion Pretreatment: A 0.1000 g sample was weighed into a polytetrafluoroethylene (PTFE) digestion vessel. Then, 3 mL of HNO3, 1 mL of HF, and 1 mL of HClO3 were added sequentially. The mixture was digested in a microwave digestion system (power: 1200 W) with programmed heating to 180 °C, which was maintained for 30 min. After digestion, the solution underwent acid driving-off, was diluted to 50 mL (in 1% HNO3 medium), and filtered through a 0.45 μm membrane before analysis. (2) ICP-MS Analytical Conditions: Analysis was performed using an ICP-MS instrument equipped with a collision/reaction cell. Key parameters were optimized, including a carrier gas flow rate of 1.05 L/min, RF power of 1550 W, and sampling depth of 8 mm. Online internal standards (e.g., 115In, 103Rh) were used to correct for matrix effects and signal drift. Dynamic Reaction Cell (DRC) mode was applied to eliminate polyatomic ion interferences. The geochemical element testing was conducted by the Experimental Center of the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences.
4 Results
4.1 Grain size composition and parameters
Grain size classification effectively characterizes sediment composition and is closely associated with sediment provenance and depositional dynamics (Qian et al., 2011). As shown in Table 2, excluding gravel fractions (>2 mm), the grain size distribution within each geomorphic unit at both depths (0.2 m and1.0 m) can be classified into seven grain size categories. Among these, silt constitutes the largest proportion (up to 70%), while clay and sand contents are both low, each accounting for less than 30%. And the grain size composition exhibits a high degree of similarity between surface (0.2 m) and subsurface (1.0 m).
Table 2. The grain size average content and parameters of sediments in different geomorphic units.
Among the geomorphic units, the plain and low mountain and hilly areas exhibit the coarsest mean grain size (MZ), at 34.15 μm and 33.73 μm, respectively, while the platform area shows the finest mean size (27.13 μm). The gully area falls in between (26.99 μm). More importantly, the mean grain sizes of the deep and surface samples within each geomorphic unit are remarkably similar, with differences all less than 3 μm, indicating no significant vertical change in the overall grain size.
The sorting coefficient (σ) for all units is greater than 2.8, categorizing the sediments as poorly sorted. The low mountain and hilly area shows the poorest sorting (σ = 3.10), while the gully area is relatively better sorted (σ = 2.90). Vertically, the sorting characteristics remain nearly identical between deep and surface samples in all units, except for a slight deterioration in sorting in the deep sample of the platform area.
All samples show near-symmetrical or very fine-skewed (Sk) distributions (−0.09∼0.02), indicating a dominance of fine components and an underdeveloped coarse tail. The kurtosis (KG) values are all greater than 1.15, indicating a leptokurtic (sharply peaked) distribution and suggesting concentrated grain size populations. These two parameters also show minimal vertical variation.
In summary, the grain size characteristics of the sediments in the study area demonstrate remarkable vertical stability from the deep to the surface layers. The differences between various geomorphic units are far greater than the vertical differences within any single unit. This may imply that the dynamic regime controlling sediment transport and deposition has not undergone fundamental changes since the deposition of the deep sediments.
4.2 Characteristics of grain end members
4.2.1 End-member separation result
Previous research results have shown that the final number of end members can be determined through a comprehensive analysis of correlation (>0.95), angular deviation (<5°), and correlation degree (Hateren et al., 2018; Paterson and Heslop, 2015). In our research, regardless of the analytical method, when the number of end members exceeds four, the linear correlation (R2) between the simulated and measured values surpasses 0.95, indicating a high degree of overall fit. However, as the number of end members increases, the R2 gradually also increases progressively. In general, the final number of end members should not be less than the number of peaks observed in the grain size distribution curve (Figure 2) (Hateren et al., 2018). Meanwhile, taking into account that the angular deviation should be less than 5° and the correlation degree of the dominant end member should be low, the number of end members in each geomorphic unit was determined, and their fitting characteristics are summarized (Hateren et al., 2018; Zhang et al., 2020).
Figure 2. End member granularity frequency distribution curve at two depths.
Figure 2 presents the fitting results of end members for each geomorphic unit. At 0.2m, six end members were identified in low mountain and hilly area, five in the gully area, four in the platform area, and five in the plain area; while at 1.0 m, six end members were separated in all four geomorphic areas.
The distribution characteristics of end members are shown in Figure 3. The proportional abundance of each end member exhibits distinct patterns across the four geomorphic units, with notable consistency observed between the surface (0.2 m) and deep (1.0 m) samples within each unit. A key finding is the remarkable consistency between surface (0.2 m) and deep (1.0 m) samples within each geomorphic unit, indicating stable sedimentary dynamics and source materials over time.
Figure 3. Proportion of EMs in each area.
In each geomorphic unit, EM1 and EM2 correspond to extremely fine silt, EM3 and EM4 to fine silt, and EM5 and EM6 to coarse silt and coarse sand, respectively. In the low mountain and hilly area, six end members are identified. EM3 presents the dominant component proportion (31.37% at 0.2m, 26.17% at 1.0 m), followed by EM2 (extremely fine silt). In contrast, the proportions of coarser end-members (EM5 and EM6) remain low (<4%) at both depths. The area exhibits the highest internal variability among end-members (CV = 0.73), reflecting its complex sedimentary environment.
Although only five end members are identified in the gully area, their proportional distribution closely resembles that observed in the low mountain and hilly region. EM3 constitutes the highest proportion at both the surface (29.94%) and subsurface (33.57%). The overall structure of the end-member assemblage is very similar to that of the low mountain and hilly area, though the absence of EM6 suggests a slight simplification in sediment sources or transport mechanisms. The internal variability is substantial (CV = 0.68).
The platform area contains the fewest end members, suggesting relatively simple sedimentary dynamics and provenance conditions on surface, with a notable absence of coarse-grained components. EM1 is the dominant component at the surface (35.01%), while at depth, EM3 becomes slightly more prominent (29.52%). The consistent lack of coarse-grained end-members (EM5, EM6) is notable. The internal variability is moderate (CV = 0.37&0.48) and lies between that of the plain and the more rugged areas.
The plain area features five end members and displays the most uniform distribution, evidenced by the lowest internal variability (CV = 0.31&0.53). EM2 and EM1 are the most abundant at the surface, while at subsurface, the proportions of EM1, EM2, and EM3 are nearly equal. This suggests a well-mixed sediment load under relatively stable hydrodynamic conditions.
In summary, the vertical consistency within each unit underscores the stability of sediment sources and depositional processes. Spatially, the complexity of end-member assemblages and their internal variability decrease from the low mountain and hilly area to the gully, platform, and plain areas, correlating with the transition from erosional to depositional landscapes. It is worth noting that EM1-3 dominates both the surface and subsurface layers, and its content proportion remains relatively stable.
4.2.2 Probability accumulation curves of grain size for end members
Sediment grain size analysis curves are typically classified into three categories: grain size frequency curves, cumulative frequency curves, and probability accumulation curves (Visher, 1969). Compared with the other types of curves, probability accumulation curves provide a more effective means of observing and comparing changes in slope, degree of mixing, cut-off points, and other key parameters, thereby playing a crucial role in the identification of depositional processes (Xu et al., 2019). The cumulative probability curves of grain size distributions for the four geomorphic zones are illustrated in Figures 4A,B. Based on the number of segments and their respective slopes, the probability accumulation curves of the end members can be categorized into three distinct types: the steeply-inclined upward arch mode, the steeply-inclined two-stage mode, and the three-stage mode (Figures 4C).
Figure 4. The cumulative probability curves of end members for each geomorphic unit at 0.2 m (A) and 1.0 m (B), as well as their curve patterns (C).
The distribution of cumulative probability for the end member grain size across different geomorphic units are summarized in Figure 4. A three-stage pattern is evident in EM1 across all topographic regions for both depths. Generally, all three sediment transport modes are present, with the traction and suspension components comprising are lower than saltation component which is constituting over 80% of the grains. Meanwhile, its sorting efficiency is slightly lower than that of the traction and suspension components. With 70% of its grains finer than 6Φ within a wide size range (2Φ to 12Φ), EM1 is designated as the finest end member due to its comprehensive mixture of grain sizes. A steeply inclined, two-stage mode predominates in EM2, EM3 and EM4. These curves consist of two linear segments with slopes exceeding 50°, delineating the suspension and saltation components, and notably lacking traction components. The pronounced slope suggests efficient sorting, indicative of relatively stable sediment transport dynamics. The high-inclined upward-arched curve is typified by a continuous arch without distinct inflection points for individual grain sizes, reflecting a transitional grain size distribution. EM5 exhibits distinct characteristics across different units. In low mountain and hilly, and gully areas, it displays a high-angle arched pattern similar to that of EM6, with no obvious grain boundaries observed. This reflects a transitional grain distribution, indicative of rapid accumulation of coarse particles. Despite the heterogeneity in grain sizes within the steep upward-arched type, sorting is markedly superior to that observed in the broad, gently arched curves characteristic of mudflow deposits (Wang et al., 2017). Meanwhile, the EM6 at the surface and deep layers varies significantly in different regions. The surface features such as gully, platform and plain lack the coarsest EM6, while the four deep-layer terrain areas all have high-sloped and upward-arching curves for EM6.
4.3 Characteristics of geochemical elements
4.3.1 Major elements
The principal oxides in the study area primarily consist of SiO2, Al2O3, Fe2O3, CaO, Na2O, K2O, and MgO (Table 3). SiO2 is the most abundant component, with an average concentration of 60.57% (52.08%∼63.15%), followed by Al2O3, at 12.75% (11.17%∼14.12%). The content of Fe2O3 averages 3.84% (2.99%∼4.54%). Na2O and K2O are both approximately 2%, while MgO is present at the lowest concentration, around 1%.
Table 3. Content of constant elements (%).
The spatial variability of these six oxides is limited (CV < 0.5). In contrast, although the average CaO content is moderate (4.48%), its spatial distribution is considerably more variable (CV = 0.73). Specifically, CaO concentrations in the platform region are significantly higher than in the plain, gully, and low hill regions, following the order: platform > plain > gully > low hill.
Compared to the Upper Continental Crust (UCC; Figure 5), the study area exhibits relative depletion in Al, Fe, Na, K, and Mg. In contrast, Si content remains stable and closely matches UCC values. CaO is enriched in the platform region and approaches UCC levels in the plain, but is markedly depleted in the gully and low hill regions. Relative to Post-Archean Australian Shale (PAAS), the study area is enriched in Ca and Na, while Al, Fe, and K are depleted. SiO2 and MgO concentrations are similar to those in PAAS.
Figure 5. Standardization pattern diagram of major elements.
4.3.2 Trace elements
The distribution pattens of trace element show close alignment between surface and deep sediments across Jalaid region (Figure 6), with only minor vertical variation.
Figure 6. Standardization pattern diagram of trace elements.
Relative to Upper Continental Crust (UCC) values, high-field-strength elements (HFSEs) exhibit distinct trends: Th and Y remain stable and cluster near UCC values across all geomorphic units; U is slightly enriched in gully and plain areas but depleted in platform and low mountain areas; Zr shows significant enrichment with minimal spatial variation, whereas Nb is consistently depleted.
Among the large ion lithophile elements (LILEs), Rb and Sr are generally depleted. Rb is notably lower in the platform area, while Sr is relatively elevated in the plain area and is particularly enriched in the deep layer (1.0 m) of the platform. Ba is uniformly slightly enriched across all units.
Transition metal elements display systematic depletion of Sc, Cu, and Zn, whereas V, Cr, Co, and Ni are mildly enriched. Especially, concentrations of transition metals are significantly higher in low mountain and gully areas than in platform and plain.
5 Discussion
5.1 Discrimination of end-member dynamic types
5.1.1 C-M diagram
To refine the identification of sedimentary dynamic regimes, the C-M diagram was utilized as a supplementary analytical method alongside grain size end-member analysis. Originally proposed by Passega (1964), the C-M diagram synthesizes sediment transport dynamics with grain size distributions, offering critical insights into depositional mechanisms and environmental settings.
As shown in Figure 7, the sediment end members in Jalaid Banner are notably dispersed, reflecting heterogeneous sedimentation processes across the study area. Except for EM5 in the platform region, all other end members display similar distribution patterns between surface (0.2 m) and subsurface (1.0 m) horizons, suggesting stable depositional dynamics over depth.
Figure 7. C-M diagram of end-member granularity in various terrain areas (Passega, 1964). Among them, the data of Huangshan loess is from (Wei et al., 2015), the modern sediment data of Yangtze River is from (Li et al., 2010), and the data of Xifeng Loess and Harbin Formation is from (Zhan et al., 2018).
Although EM1, EM2, and EM3 are situated within a turbidity current model domain, their sedimentary features do not correspond to classical turbidity current patterns, as indicated by their probability accumulation curves (Zhang et al., 2018). The distribution of these end-members in the C-M diagram is similar to that observed for Huangshan loess, Harbin Formation, and Xifeng loess, which is consistent with aeolian sedimentary characteristics.
EM4, EM5, and EM6 are located near the range of traction flow models and display similarities to modern fluvial facies sediments (Li et al., 2010; Wang et al., 2021). EM4 and EM5 are more dispersed than the other end-members. The surface distribution of EM4 is scattered, with occurrences in mountain, gully, and platform regions corresponding to the turbidity model domain, while its presence in the plain region falls within the SR segment of the traction flow and uniform suspension domains. In deeper layers, the distribution of EM4 across geomorphic units is similar to that observed in the surface layers of the low mountain and plain regions. The surface distribution of EM5 is also relatively dispersed. Specifically, EM5 in low mountain areas is situated within the turbidity model domain; in the plain region, it is found in the PQ segment of the traction flow domain, where suspension is the primary transport mode with a minor rolling component. EM5 in the gully region and EM6 in the mountain region are present in the NO segment of the traction flow domain, which is associated with the rolling transport of coarse particles. Distinct differences are observed between the distribution patterns of EM5 in deep and surface layers. In the gully region, deep EM5 is found in the turbidity model domain, in contrast to its surface distribution near EM6. In the platform region, deep EM5 corresponds to Huangshan loess deposits, which have been identified as having a fluvial origin (Zhan et al., 2018).
5.1.2 Chemical weathering indices
Chemical weathering indices offer quantitative assessment of the extent of weathering affecting sediments, thereby facilitating sedimentary environment discrimination. Widely adopted indicators include the Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW), and Plagioclase Index of Alteration (PIA) (Li et al., 2022). To minimize the bias inherent in single-index approaches, this study applies all three indices concurrently, enabling a robust evaluation of chemical weathering intensity across geomorphic units (see Table 4).
Table 4. Index of weathering degree for each geomorphic unit.
The CIA is calculated as CIA = Al2O3/(Al2O3 + K2O+ Na2O+ CaO*), where CaO* represents the calcium exclusively from silicate minerals, excluding carbonates and phosphates. The CIA value is proportional to chemical weathering degree of sediment, with strong weathering degree of 80∼100, moderate weathering degree of 60∼80 and low chemical weathering degree of 50∼60 (Fedo et al., 1995). Elevated CIA values indicate enhanced chemical weathering. The data reveal that low mountain and hilly regions (mean CIA = 59.86 and 60.89) and gully areas (59.83 and 59.89) exhibit marginally higher weathering degrees compared to plains (54.79 and 55.77) and platforms (56.75 and 58.87). All values correspond to low to moderate weathering intensities. The similarity between surface (0.2 m) and subsurface (1.0 m) samples across all units suggests minimal temporal variation in weathering degree. All the samples showed that study area had experienced low degree of chemical weathering.
To account for the influence of potassium metasomatism, the CIW is calculated as CIW = Al2O3/(Al2O3 + CaO* + Na2O) × 100 (Harnois, 1988). Its value of 50∼60 is lower chemical weathering degree, CIW>70 is stronger chemical weathering degree (Han et al., 2024). The CIW values display a similar spatial trend: low mountain and hilly regions (68.23 and 69.17) > gullies (68.21 and 68.03) > platforms (64.90 and 66.65) > plains (63.32 and 64.30). The PIA, which specifically addresses plagioclase weathering, is computed as PIA = [(Al2O3 - K2O)/(Al2O3 + CaO* + Na2O- K2O)] × 100 (Fedo et al., 1995), and yields consistent results. The consistently low values of CIA, CIW and PIA (all less than 70, as shown in Table 4) indicate that regional chemical weathering is relatively weak and is currently in the leaching stage of Ca, Na and Mg (Figure 5). This may be consistent with the cool and semi-arid regional climate (Hu, 1993) as well as the relatively high sedimentation rate (Cui et al., 2021), all of which limit the long-term chemical changes of the sediments. The lower degree of weathering supports the interpretation that the post-depositional modification of mainly aeolian sediments is limited.
5.1.3 Analysis of each EM
Based on the preceding analyses, above results, each grain size end member is analyzed individually as follows:
EM1, characterized by mode grain sizes of 11.63 μm (0.2 m) and 12.84 μm (1.0 m), is an extremely fine silty sand component. Past windblown sand experiments have demonstrated that particles with sizes between 10 and 50 μm are readily suspended in the air, and those smaller than 20 μm can persist as floating dust for extended periods once windblown (Tsoar and Pye, 1987). The grain size distribution within 6∼20 μm has been identified as high-altitude, long-distance suspended dust (Vriend et al., 2011). Notably, most of EM1’s grain size is below 15μm, making it susceptible to prolonged suspension in the wind. While weathering processes can result in the formation of fine-grained clastic material from surface bedrock or weathering crust (Sun et al., 2010), the pedogenic grain size is less than 2 μm. The ultra-fine end-member components of Xiashu loess are primarily formed through pedogenesis, with grain sizes under 2 μm, and their variation trends are strongly correlated with weathering-induced pedogenesis (Liu M. et al., 2021). However, the chemical weathering indicators show that all geomorphic units are in the primary weathering stage and the proportion of clay <2 μm is very low (about 3%). That means the contribution of pedogenesis to the fine-grained matter is relatively limited. The fine-grained end members of Harbin loess, situated on the eastern margin of the Songnen Plain, have been described as “background dust” with a modal size of 7.81 μm (Song et al., 2023). Although the mode grain size of EM1 is slightly larger, its sedimentary characteristics are comparable. Thus, EM1 is interpreted as being primarily transported by long-distance wind suspension, with only a minor contribution from pedogenic processes.
EM2, characterized by mode grain sizes of 23.26 μm (0.2 m) and 23.45 μm (1.0 m), primarily consists of a fine silt component. According to the dust dynamics model, particles larger than 20 μm are typically confined to low heights, not exceeding 100 m even under strong winds. In terms of transport distance, dust particles larger than 20 μm generally have a migration radius of less than 30 km. However, in cases of intense convection, this distance can be extended to 500–1500 km, which belongs to the middle- and long-distance source transport model (Pye and Zhou, 1989; Tsoar and Pye, 1987). Similarly, Vriend et al. have pointed out that dust particles with sizes between 22 and 31 μm can be transported via middle-distance suspension (Vriend et al., 2011). Furthermore, research conducted by Yuan et al. has shown that grain size components with a median value of 13–30 μm constitute a significant portion of dust storms in Harbin (Yuan et al., 2018). Based on this information, this paper concludes that EM2 is primarily transported through short-term suspension over middle and long distances.
EM3, characterized by mode grain sizes of 48.83 μm (0.2 m) and 47.51 μm (1.0 m), is classified as a fine silt component. It is generally acknowledged that particles exceeding 50 μm gradually lose their ability to float as they become larger and heavier (Yuan et al., 2018). In loess studies, particles larger than 63 μm are typically indicative of near-source transport and are carried by short-distance suspension or saltation near the ground (Vriend et al., 2011). Particles smaller than 30 μm, akin to EM2, are associated with middle and long-distance suspension transport. However, for grain sized between 30 and 50μm, there is a lack of consensus. Tsoar suggests that this size range is generally transported by strong winds over short to medium distances (Tsoar and Pye, 1987). Sun et al. interpret this size component as a short-distance intermittent suspension near the surface influenced by regional winter winds (Sun et al., 2004), whereas Gao et al. determine it to be a short-term suspension transport from middle and far sources (Gao et al., 2022). Considering the specific differences in sedimentary environments, the grain size of EM3 components in this study is coarser than EM2 and exhibits sedimentary characteristics closer to Harbin loess (Figure 7). Therefore, it is postulated that EM3 is more likely transported by wind over short to medium distances, primarily through temporary suspension and saltation.
EM4, characterized by mode grain sizes of 105.95 μm (0.2 m) and 105.22 μm (1.0 m), is classified as a medium to coarse silt component. Its distribution across various geomorphic units is significantly lower than that of EM1∼3, typically accounting for less than 20% of the total. Additionally, it was observed that the content in the deep platform area is higher than that in the deep plain area. This may indicate that the current platform area was most likely a plain area in the past with alluvial deposits. The morphological characteristics of EM4 probability accumulation curves are similar, with approximately 40% of the components being jumping particles and about 60% being suspended particles. And the inclination angles of the line segments are generally large, indicating good sorting. Previous research has mentioned that such sedimentary features are indicative of braided river flow (Zhang et al., 2018). Furthermore, EM4 of most units are concentrated in the SR section of the traction flow model, suggesting that these are water-generated suspended sediments (Figure 7). While the EM4 curves in both surface gully and surface platform areas exhibit a high-inclined two-stage morphology, the grain size is coarser in these regions. The saltated components account for more than 90%, with only a few suspended components present. The overall classification is poor, reflecting a high-energy hydrodynamic environment and the instability of the riverbed, which retains coarse sediment (Zhang et al., 2018).
EM5, with mode grain sizes of 594.38 μm (0.2 m) and 315.60 μm (1.0 m), represents a medium to fine sand fraction. Sun et al. reported that the peak grain sizes of coarse components in river sediments typically fall within the 200∼400 μm range, or even coarser (Sun et al., 2002). Similarly, Dietze found that end members with grain sizes between 250 and 600 μm predominantly occur in fluvial sediments (Dietze et al., 2014). Apart from the deep platform area, the EM5 in other regions all near PQ and P-NO(Figure 7). Accordingly, EM5 is interpreted as a fluvial sediment component. However, the occurrence of EM5 varies across different geomorphic regions; its abundance is generally lower than that of EM4 and it is entirely absent in the surface platform region. In low mountains area, EM5 accounts for only about 3%, likely representing turbiditic or graded suspended sediments. In the gully area, EM5 constitutes the coarsest end member of surface with just 1.93% and 3.69% of deep. It plots within the NO section (rolling transport) of the C-M diagram (Figure 7), suggesting a possible origin as gully river overbank or plain facies deposits. In contrast, the deep platform area exhibits a relatively higher proportion of EM5 (9.32%) and plain area (10.23% and11.78%), indicative of graded suspended sediment deposition. The distribution of EM5 displays an increasing trend from the Greater Hinggan Mountains towards the Songnen Plain, consistent with the regional fluvial transport direction.
EM6, with mode grain sizes of 1218.93 μm (0.2 m) and 1250.65 μm (1.0 m), represents a coarse sand fraction and is the coarsest end member identified in the Jalaid area. It is exclusively present in the all-deep regions and only surface low mountains. EM6 is located within the NO section (rolling transport stage) of the C-M diagram (Figure 7), indicative of bedload transport under the combined influence of hydrodynamic forces and gravity. This is most likely related to the undulating topography and high erosion (Tsoar and Pye, 1987). The mountainous and hilly regions are characterized by elevated topography, pronounced relief, enhanced chemical weathering, and intensified physical erosion. The region experiences a temperate continental monsoon climate, with most of the precipitation occurring in the summer (Hu, 1993). Orographic uplift enhances precipitation and precipitation intensity with increasing elevation from the plains to the mountains, thereby increasing the erosive capacity of runoff. The presence of the coarser-grained end member in deeper layers likely indicates stronger hydrodynamic conditions during past depositional periods, more pronounced erosional forces or the source supply changed during the sedimentation process. This has led to the widespread presence of traction load deposits across all topographic zones.
5.2 Analysis of sediment sources
5.2.1 Geochemical and sedimentological evidence
In provenance studies of sediments, geochemical characteristics serve as crucial indicators. Although undergo complex geological processes including weathering, erosion, transportation, deposition, and even consolidated diagenesis that can partially modify their original chemical signatures, previous research has shown that sediment geochemistry is primarily controlled by the properties of source region (Absar et al., 2009; Ahmad et al., 2016). While major elements can be used for provenance discrimination (Bhatia, 1983; Bhatia and Crook, 1986), their susceptibility to weathering processes can complicate interpretations in surface environments. By contrast, trace element analysis provides more definitive evidence for provenance identification. The ratios of some stable elements (e.g., La vs. Th, Cr/Th vs. Co/Th, Figure 8) clearly confirm that.
Figure 8. Provenance identification based on trace element ratios: including La/Th (A), Y/Ni vs Co/Th (B), and Y/Ni vs Co/Th (C), comparing the study area with potential source regions. Data sources: the data of Chaihe City is from (Peng et al., 2013), the data of Sandy Lands are from (Zhao W. et al., 2024).
In the La vs. Th diagram, our samples are closer to local sediments and the Horqin Sandy Land, while relatively farther from other sandy lands. Notably, samples from the platform and plain areas exhibit greater proximity to the Horqin Sandy Land. This is likely due to their lower elevation and southeastern position within the study area. Such a geographical setting is more conducive to receiving high-altitude dust inputs from the Songnen or Horqin Sandy Lands, compared to the western mountainous regions. In the Y/Ni vs. Co/Th diagram, our samples are relatively clustered and show generally weak affinity toward potential source regions; however, a slight tendency toward the Horqin Sandy Land is still observable. Furthermore, in the Cr/Th vs. Co/Th diagram, although samples display approaching trends to multiple sandy lands, the Horqin Sandy Land is unambiguously the closest among them. This exclusion aligns with the known obstruction effect of the Greater Hinggan Mountains, which effectively impedes dust transport from the Hulun Buir Sandy Land and Onqin Daga Sandy Land to the study area (Xie et al., 2020). In conclusion, trace element ratios (e.g., La/Th, Cr/Th, Co/Th) clearly cluster our samples with the Horqin Sandy Land and distinctly separate them from other sandy lands (Figure 8).
5.2.2 The regional wind field
Identifying the possible source transmission paths is equally important with analyzing the sediment characteristics in provenance analysis. The study area is located at the northwest edge of the Songnen Plain, while the disputed potential source area-the Songnen sandy land is in the southeast direction, and the Horqin sandy land is in the south (Figure 1A). However, there are essential differences in the contribution efficiency of the two to the study area. Research on modern dust storms and loess in Harbin region indicate that the dominant wind direction in the Northeast Plain of China is the southwest wind during the spring and summer (Liu J. et al., 2023; Song et al., 2023; Xie et al., 2013; Zhang et al., 2023). Since the last ice age, compared to the Songnen sandy land, the Horqin sandy land has been in a stronger activation stage and possesses a large-scale dust emission capacity (Yang et al., 2017). Therefore, the Horqin sandy land is quite reasonable as a source area for medium and long-range sand. Under the influence of the southeast wind, the Songnen sandy land is certain to make a contribution of coarse particles from the near source to the study area, just as the analysis of the loess in Harbin (Song et al., 2023). However, it is obvious that the contribution proportion is still relatively lower than Horqin sandy land.
5.2.3 Comprehensive analysis
In conclusion, although the Songnen Sandy Land is geographically closer, this study—based on paleo-wind dynamics, geochemical fingerprints, and sediment composition—identifies the Horqin Sandy Land as the dominant aeolian source of the black-soil sediments in the Jalaid region. The principal evidence is as follows: (1) Transmission feasibility: The prevailing paleo-southwesterly wind field enabled long-distance transport of dust from the Horqin Sandy Land. The Songnen sandy only makes a relatively weak contribution under the influence of southerly winds. (2) Geochemical evidences: Trace element ratios show significant similarity between the study area sediments and the Horqin Sandy Land, while demonstrating clear differentiation from the Songnen Sandy Land. (3) Sedimentary consistency: The dominant aeolian components and their provenance implications are fully aligned with the geochemical evidence. These findings highlight the long-range, high-altitude transport characteristics of the black-soil materials along the northwestern margin of the Songnen Plain. Under the combined influence of winter winds and westerly circulation, dust from the Horqin Sandy Land was transported over >100 km to settle in the study area, forming the foundational parent material of the local black soils. In contrast, local and proximal fluvial inputs (EM4–6, largely derived from the Greater Hinggan Mountains) represent secondary modifying processes.
5.3 Environmental implications of EMs
End member analysis reveals that variations in end-member characteristics among geomorphic units are linked to the interplay between topography and climate. In low mountain and hilly areas, steep slopes and high, concentrated precipitation (Huang and Cao, 2015) promote slope erosion and mudflow deposition (EM6). In contrast, the flat terrain and weaker hydrodynamics of the plains favor siltation, facilitating the accumulation of wind-blown sediments (EM1∼3) and suspended river deposits (EM4∼6). On the platform, wind sorting and local water flow result in a more uniform end-member distribution. These findings underscore the significant role of topography in controlling sediment dynamics, as noted in previous studies (He et al., 2023; Peng et al., 2013).
Research on black soils in Keshan County (Liu K. et al., 2023) and Hailun area (Song et al., 2022) indicates that widespread Late Pleistocene to Mid-Holocene loess—like sub-sandstones in the eastern Songliao Plain foothills serve as key parent materials, with geochemical evidence confirming their aeolian origin. A similar formation process appears to have occurred in Jalaid, a mountain-plain transition zone shaped by climate-geology interactions. Surface sediments (0–1 m) here are also dominated by aeolian components (EM1∼3 > 80%), analogous to the eastern black soils. During the interglacial period, the prevailing winds in the Songnen Plain were from the south (Liu K. et al., 2023). Located on the plain’s northwestern margin, Jalaid was susceptible to these winds, which transported sand and dust. However, as Jalaid was not directly downwind, wind energy was limited. This resulted in the deposition of relatively fine EM3 particles (medium silt) within the aeolian end member (medium silt). Despite partial constraints from monsoons—such as the Greater Hinggan Mountains blocking northwesterly winds and the brief summer restricting monsoonal sediment input—a strong linkage between local sediments and the monsoon system persists. However, we acknowledge the inherent uncertainty in reconstructing past wind patterns. Additional quantitative data and sedimentological evidence are required to further elucidate wind and dust transport pathways. Future studies integrating paleoclimate modeling and independent wind proxies are needed to further constrain dust transport pathways during key depositional periods.
6 Conclusion
In summary, this study sheds light on the provenance and formation processes of black soils in the Jalaid region of the northwestern Songnen Plain.
1. Grain-size and geochemical analyses reveal a notable vertical consistency in sediment sources and transport mechanisms, with 4-6 grain-size end members persistently identified in both surface (0.2 m) and deep (1.0 m) layers across four geomorphic units.
2. Aeolian inputs (EM1-3) are shown to dominate the sediment composition, accounting for more than 71% of the assemblage, while hydrodynamic contributions remain comparatively minor less than 29%. Geological conditions have a significant impact on sedimentation: steep slopes in low mountain and gully areas promote slope erosion and mudflow deposition (EM6), while flat plains and platforms favor the accumulation of aeolian fines (EM1-3) and fluvial suspended loads (EM4-5).
3. Trace element ratios, such as La/Th and Cr/Th vs. Co/Th, further point to the Horqin Sandy Land as the primary source of windblown material, and the observed patterns suggest that southerly winds are responsible for dust transport and deposition.
This study provides a quantitative provenance framework that revises our understanding of sediment sources for the Songnen Plain black soils, with implications for assessing soil sustainability and modeling past dust dynamics in East Asia. Nevertheless, local-scale dust transport and deposition processes are not yet fully understood, highlighting the need for future research employing high-resolution geomorphological and meteorological data.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
PC: Conceptualization, Funding acquisition, Methodology, Resources, Validation, Visualization, Writing – original draft, Writing – review and editing. JQ: Conceptualization, Data curation, Methodology, Validation, Writing – original draft, Writing – review and editing. JM: Formal Analysis, Supervision, Writing – review and editing. YJ: Formal Analysis, Supervision, Writing – original draft. HS: Investigation, Project administration, Writing – original draft. CW: Investigation, Writing – review and editing. JH: Data curation, Validation, Writing – original draft.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by Major geological survey projects: Surface matrix survey of black land in Zhalantun-Jalaid area on the northwest margin of Songnen Plain (NO. DD20220856), Surface matrix survey of 1:250,000 in Haihe Plain (NO. DD20242042) and the APC was funded by DD20242042.
Acknowledgements
Thanks to Professor Huaming Guo of China University of Geosciences (Beijing), Professor Jin-song Yang of Institute of Hydrogeology and Environmental Geology of Chinese Academy of Geological Sciences and Yanru Wang of Harbin Normal University for their help and guidance.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Keywords: end member analysis, grain size characteristics, provenance analysis, sedimentological information, songnen plain
Citation: Chen P, Qi J, Ma J, Jing Y, Sun H, Wang C and Huang J (2026) Identification and provenance analysis of sediments’ end-members in the typical black soil area of northeast China. Front. Earth Sci. 13:1749718. doi: 10.3389/feart.2025.1749718
Received: 19 November 2025; Accepted: 15 December 2025;
Published: 07 January 2026.
Edited by:
Shiyong Yu, Jiangsu Normal University, ChinaReviewed by:
Guoxiang Chen, Gansu Agricultural University, ChinaRaul Miranda-Aviles, University of Guanajuato, Mexico
Copyright © 2026 Chen, Qi, Ma, Jing, Sun, Wang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jiahao Qi, cWlqaDI0NEAxNjMuY29t