A perspective on heavy metal(loid)s traceability in soil surrounding coal gangue areas: combined application of multiple source analysis methods

In China, coal remains the dominant energy source, leading to substantial production of coal gangue. This study established comparative zones—a study area and a control area—and applied the Geo-Detector model (GDM), Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model, and cadmium isotope fingerprinting to delineate the origins of heavy metal(loid)s (HMs) in soil proximate to accumulations of coal gangue. The contents of cadmium (Cd), mercury (Hg), arsenic (As), and lead (Pb) were significantly elevated in the study area relative to the control, whereas the levels of chromium (Cr), copper (Cu), nickel (Ni), and zinc (Zn) displayed no marked differences between the zones, underscoring an intensified pollution level in the study region. The integration of GDM, APCS-MLR, and Cd isotope fingerprinting methods significantly enhanced the precision and dependability in identifying pollution sources. Soil contamination in the study area was predominantly due to persistent coal gangue accumulation, vehicular emissions, and agricultural chemicals, contributing 40.1%, 33.7%, and 26.2% respectively, while in the control area, vehicular emissions and agricultural activities were the main contributors. Surface runoff from coal gangue accumulation emerged as the dominant pathway for soil contamination in the study area. The study underscores the urgency of implementing strategic coal gangue management and mitigation of ecological hazards, particularly those associated with elevated Cd levels.

1 Introduction

Coal has long been a crucial energy source, extensively utilized across multiple industries (Wang et al., 2024). China is the world’s largest coal producer and consumer, contributing ∼50% of global coal production (Liu and Song, 2024), and coal consumption escalated from 60 million tons of oil equivalent (Mtoe) in 1971 to 1907 Mtoe in 2018, this trajectory, projected to reach 2,410 Mtoe by 2030 (Shahbaz et al., 2015; Tiwari et al., 2023). This significant increase in consumption has prompted widespread mining operations. Generated primarily through these processes, coal gangue constitutes 10%–15% of overall coal output and has amassed to over a billion tons, with a yearly rise of 150–200 million tons. This has become a significant byproduct of industrial processes (Zhang et al., 2015; Gao et al., 2021). A multitude of studies has identified the accumulation of coal gangue as a significant contributor to heavy metal(loid)s (HMs) pollution, which disperses to surrounding soils through surface runoff and atmospheric deposition (Song et al., 2023; Zhang et al., 2024; Sun et al., 2021; Wang and Wang, 2018; Tokatlı et al., 2025). Importantly, HMs such as Cd, As, and Pb are designated as priority control pollutants by the United States Environmental Protection Agency (USEPA) because of their persistent toxicity (Arora and Chauhan, 2021; Ma et al., 2024a; Din et al., 2024). The presence of these HMs can adversely affect a wide range of plant species at increased contents, while also presenting significant health hazards to humans (Yüksel and Ustaoğlu, 2025; Din et al., 2023; Ali and Muhammad, 2023).

The origins of soil HM pollution are varied and intricate. Beyond coal gangue, significant sources include soil parent material weathering, agricultural irrigation, fertilizer and pesticide usage, and emissions from industrial and road traffic (Ma et al., 2024a; Wang et al., 2019; Xiao et al., 2019; Muhammad et al., 2024). Thus, precise identification and quantification of HMs sources in soils near coal gangue heap are crucial for effectively managing and reducing regional soil HM pollution. Presently employed receptor models such as the APCS-MLR and Positive Matrix Factorization (PMF) are preferred for their ease, speed, and efficiency (Shi et al., 2024; Ma J. et al., 2023; Yao et al., 2024). The former identifies sources via PCA, quantifying their contributions through MLR between HMs content and APCS. The latter uses uncertainty-based weighting, decomposing the data matrix into factor contributions and profiles through iterative least-squares refinement (Ma et al., 2024b). Nevertheless, these models present limitations: outliers may skew the distribution of HM source apportionment results (Huang et al., 2018; Lv, 2019), and these models frequently overlook additional environmental factors (Ma J. et al., 2023; Yang et al., 2021), resulting in source identifications that rely predominantly on subjective assumptions by the user, which are challenging to substantiate (Ma J. et al., 2023; Shi et al., 2023). Recent advancements have demonstrated that combining receptor models with the Geo-Detector Model (GDM) can decrease user subjectivity and enhance the precision and dependability of source apportionment (Ma et al., 2024b; Guo et al., 2022). This enhancement benefits from the GDM’s capability to discern principal factors influencing soil HMs and to assess the nonlinear relationships between these factors and soil HMs, making it especially suited for analyzing spatial variations (Guo et al., 2022; Shi et al., 2018; Zeng et al., 2023).

However, robust evidence directly connecting receptors to specific contaminants is still limited. Isotopic ratios serve as distinctive markers in environmental studies, allowing researchers to identify possible origins of HMs and track their movement (Yao et al., 2023; Zhong et al., 2022; Wang et al., 2021). Such as elevated-temperature operations, including smelting, coal combustion, and the production of glass, induce notable isotopic fractionation in cadmium. In these processes, lighter isotopes preferentially ascend into the gaseous state, whereas heavier isotopes accumulate in residual materials (Xia et al., 2025; Zhong et al., 2020). Moreover, isotopic ratios of Cd in terrestrial minerals and rocks generally show scant variance (Liang et al., 2024). Extensive research has demonstrated that the cadmium isotope ratio (δ¹¹⁴/¹¹⁰Cd) is a robust tracer for pinpointing the origins of cadmium within environmental settings, elucidating isotopic discrepancies across varying contents (Cloquet et al., 2006). We believe that the combined application of multiple source analysis methods can make the analytical results more reliable, accurate and scientific.

This study are designed to achieve primary objectives: (i) ascertain the levels, spatial arrangement, and stratigraphic profiles of HMs adjacent to a coal gangue heap by designating investigatory and control zones; (ii) combined application of multiple source analysis methods, discern the sources of these HMs and quantitative assess their effects on the soil near the coal gangue heap.

2 Materials and methods

2.1 Study area

The southern Qijiang District of Chongqing Municipality in southwest China is where the specified study and control regions are located (Figure 1a). The area experiences four separate seasons of pleasant, humid weather with an annual rainfall of around 1,100 mm, and it is characterized by a subtropical monsoon climate.

Figure 1

Figure 1. Location and sampling sites of the study area and control area.

The study area encompasses approximately 30 hm², positioned at elevations between 700 and 800 m (Figure 1b), nestled among valleys and encircled by steep mountainous terrain. It includes two residential clusters spanning about 2 hm², interconnected by primary roads. An abandoned gangue heap, occupying about 7 hm² and standing 30 m tall with a 40-year storage history, lies to the east. Currently inactive, the site no longer accepts coal gangue.

Conversely, the control area is situated 2 km south of the study zone, covering an area of about 30 hm² with elevations ranging from 430 to 700 m (Figure 1b). It abuts a major road on the east, bordered by dispersed settlements. A mountain barrier distinctly separates the study and control zones, shielding the latter from any surface runoff from the coal gangue heap and devoid of any nearby industrial or mining activities.

In both the study and control areas, the land use type was predominantly cultivated land, primarily planted with corn and vegetables. Nitrogen and organic fertilizers were the main inputs applied. Furthermore, there were no industrial or mining enterprises operating within a 3 km radius of either area.

2.2 Samples and analysis

Since both the study and control areas are situated in hilly regions, the cultivated land plots are small and fragmented, with most field surfaces measuring approximately 150 m in length. Therefore, a 150 m × 150 m grid system was established, with 38 agricultural soil sampling sites (19 in each zone) distributed across the study and control areas (Figure 1b). Surface soil (SS) samples were collected following the standardized five-point sampling protocol, ensuring representative coverage of each grid: one central subsample and four peripheral subsamples taken 10 m from the center in the cardinal directions (Yao et al., 2024). Additionally, to enhance the representativeness of sampling points, the selection of soil profile locations primarily considered the distance from the coal gangue heap. In each area, eight distinct soil profiles (T1, T6, T8, T10, T30, T32, T36, T38) were excavated and segmented into three layers: surface soil (SS, 0–20 cm), subsoil (20–60 cm), and substratum (60–100 cm) (Ma et al., 2024b).

Post a 48-hour air-drying period, all samples were cleared of roots and organic residues using tweezers. The soil’s pH level was measured after grinding and sieving through a 2 mm nylon mesh polyethylene sieve, using a pH conductivity meter (SevenExcellence, CH) with a 1:2.5 soil-to-water ratio (MEE, 2018). For Cd and Hg analysis, the ground samples were further sieved through a 0.150 mm nylon mesh. Cd digestion followed the protocol of HCl-HF-HNO₃-HClO₄ as specified in GB/T 17141 (MEE, 1997), with detection by graphite furnace atomic absorption spectroscopy (ZEEnit700P, GER). Hg was treated with HNO₃-HCl as per GB/T 22105.1 (SAMR and NSA, 2008), analyzed using atomic fluorescence spectrophotometry (AFS-9750, CHN). For other HMs like As and Zn, after sieving through a 0.075 mm sieve, the samples were examined using X-ray fluorescence spectrometry (S8 TIGER, GER) in accordance with HJ 780 (MEE, 2015). The method detection limits for Cd, As, Pb, Cr, Cu, Ni, and Zn were 0.01, 0.002, 0.01, 2.0, 3.0, 1.2, 1.5, and 2.0 mg/kg, respectively. To ensure the accuracy and reliability of the data, Geochemical Soil Standard Reference Samples (GSS-5a and GSS-8) were employed for quality control. The standard deviation for each sample remained below 10%, confirming the high precision and reliability of the experimental results.

In addition, five coal gangue samples (CG1, CG2 at 0–50 cm; CG3, CG4, CG5 at 50–100 cm) along with 24 soil samples from the mentioned profiles were prepared for Cd isotope analysis following the protocols established by Yao et al. (2023) (Yao et al., 2023). Adopting the robust alkaline anion exchange resin AGMP-1M, Cd contents between 50 and 100 ng were reduced. Beginning with 2 mol/L HCl, moving on to 1 mol/L HCl, 0.3 mol/L HCl, 0.06 mol/L HCl, and finally 0.0012 mol/L HCl, the matrix elements were eluted in a graduated fashion after the samples were introduced. Further, an HCl solution with a content of 0.0012 mol/L was used to extract Cd. The separated Cd solutions were dried out by evaporating them on a hotplate set at 130°C for the entire night. For maximal Cd refinement, the air-dried samples were reconstituted in 2 mol/L HCl and were purified cyclically repeated using the same column arrangement. Afterwards, the stock solution for isotope analysis was prepared by resolving the Cd samples in 2% HNO₃. Surprisingly, all samples and reference materials consistently achieved recovery rates over 90% during the digestion and purification processes. A ulticollector inductively coupled plasma mass spectrometer (MC-ICP-MS, Neptune II, Thermo Scientific, United States) was applied to assess Cd isotope patterns. The assay was calibrated and quality-controlled using the widely-recognized Cd isotopic standard (NIST SRM 3108). The State Key Laboratory of Environmental Geochemistry conducted all related procedures.

2.3 Research methods

2.3.1 GDM

The GDM, introduced by Wang and Hu (2012), is effective in recognizing inherent correlations and addressing the spatial variability of factors. This tool is instrumental in the systematic determination of significant influencers (Wang and Hu, 2012). and has been broadly applied in uncovering driving elements in soil studies (Dong et al., 2021).

The interaction between the independent variables (X) and the dependent variable (Y) can be quantified by factor analysis (Zhao et al., 2021). The descriptive strength of this relationship is commonly indicated by the q-value, whose calculation is outlined as below:

$q=1-\frac{{\displaystyle \sum _{h=1}^L}{N}_h{\sigma }_h^2}{{N\sigma }^2}$

In the formula, q quantifies the explanatory power of X over Y, on a scale of 0–1, with a higher value indicating a stronger influence; h indexes the specific strata related to Y and X; N_h and N stand for the respective zones of each stratum h and the total area; and ${\sigma }_h^2$ (Nh) and ${\sigma }^2$ (N) are the variances within these strata.

Reflecting on the relevance of environmental factors around the coal gangue heap and the availability of data, six key variables were chosen: the vertical height difference between the sampling point and the coal gangue heap (X₁), the slope at the sampling point (X₂), the distance from the sampling site to the coal gangue heap (X₃), the proximity of the sampling site to the main road (X₄), the distance from the sampling site to the nearest residential area (X₅), and the Soil pH (X₆). Coordinates for X₁, X₃, X_4, and X₅ were sourced from 91 Map Assistant 19.4.0, while X₂ was derived from ArcGIS 10.2, and X₆ was directly measured in this investigation. The natural breakpoint method was applied to segment X₁∼X₆ into five distinct categories each.

2.3.2 APCS-MLR model

The APCS-MLR model, introduced by Thurston and Spengler (1985), employs APCS from PCA. These scores are combined with MLR to detect potential pollution sources, characterize their nature, and estimate their contributions (Thurston ang Spengler, 1985). The application of this model is prevalent in soil pollution source apportionment studies (Ma J. et al., 2023; Lv, 2019; Gong et al., 2024). The formula for the model is structured as follows:

$C_i=b_{i0}+{\displaystyle \sum _{p=1}^P}\left(b_{pi}\times {APCS}_p\right)$

where $b_{i0}$ stands for a constant of MLR for HM_i; $b_{pi}$ stands for the coefficient of MLR of source p specific to HM_i; APCS_p stands for the adjusted value of rotated source p; and $b_{pi}\times {APCS}_p$ means the average contribution of source p to $C_i$.

2.3.3 Cd isotope

The isotopic signature of cadmium is determined by measuring the ^114Cd/^110Cd ratios, presented in the delta (δ) notation. This process standardizes the sample’s isotope ratio to that of the NIST standard (specifically, NIST SRM 3108) (Yao et al., 2024; Imseng et al., 2018), according to the following formula:

${\mathrm{\delta }}^{114/110}Cd=\left[\frac{{\left({}^{114}Cd/{}^{110}Cd\right)}_{\text{sample}}}{{\left({}^{114}Cd/{}^{110}Cd\right)}_{\text{NIST}3108}}-1\right]\times 1000$

2.4 Statistical analysis

Descriptive statistics was conducted with Microsoft Excel 2016, involving calculations of average, peak, and minimal values, along with the coefficient of variation (CV). GeoDetector 2015 software was utilized for GDM analysis. Spatial distribution diagrams were generated using ArcMap 10.2. Statistical analyses, including the APCS-MLR model, correlation tests (Significant p ≤ 0.05, Extremely Significant p ≤ 0.01), principal component analysis (PCA), and independent-samples t-tests (Significant p ≤ 0.05,Extremely Significant p ≤ 0.01), were conducted using SPSS Statistics 24.0.

3 Results and discussions

3.1 Characteristics of HMs contents in SS

Figure 2 and Supplementary Table S1 document the SS metrics. Within the study area, the mean contents of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn were 1.33, 0.29, 13.8, 32.9, 142, 68.8, 54.6, and 118 mg/kg, respectively. Cd contents surpassed the risk screening values at 16 sites (84.2%), and Cu at 8 sites (42.1%) (MEE and SAMR, 2018). Conversely, the control area recorded average contents of 0.42, 0.17, 10.2, 26.1, 141, 76.1, 58.7, and 125 mg/kg, respectively, with Cd exceeding risk thresholds at 12 sites and Cu at 10 sites (MEE and SAMR, 2018). Thus, the analysis confirmed Cd as the primary pollutant. The contents of Cd, Hg, As, and Pb were significantly elevated in the study area relative to the control, whereas the levels of Cr, Cu, Zn, and Ni displayed no marked differences between the zones, underscoring an intensified pollution level in the study region.

Figure 2

Figure 2. HMs contents, standard deviation, and significant difference in SS.

In the study area, the CV values were sorted as follows: Cd (111.4%) > Hg (58.6%) > Cu (38.0%) > Pb (34.5%) > Zn (26.5%) > As (26.2%) > Ni (21.2%) > Cr (9.4%). In contrast, the control area displayed CV values of: Cd (34.4%) > As (29.7%) > Cu (29.2%) > Hg (24.1%) > Ni (22.4%) > Pb (20.5%) > Zn (17.6%) > Cr (16.5%). According to the CV classification proposed by Wilding (1985), Cd, Hg, and Cu in the study area fall into the high variation category (CV > 36%), indicating substantial anthropogenic influence. In contrast, other HMs demonstrated moderate or low variation (CV ≤ 36%). Notably, Cr showed the lowest CV in both zones, suggesting minimal anthropogenic impact (Yang et al., 2021; Delbari et al., 2019).

3.2 Spatial distribution of HMs in SS

Figure 3 illustrates the HM distribution patterns in soil. Sub-Figures 3a–d reveal that Cd, Hg, As, and Pb share similar dispersal trends within the study zone, with contents escalating as proximity to the coal gangue heap decreases. Correlation analysis indicated a strong linkage among these HMs (P ≤ 0.01, r = 0.59–0.77) (Figure 4a), signifying a common pollution source: the prolonged accumulation at the coal gangue heap. Conversely, in the control zone, these elements displayed a more uniform and stable distribution. Prior research has shown that coal gangue heaps are the main culprits when it comes to HM enrichment in nearby SS (Song et al., 2023; Li et al., 2016; Chen et al., 2024; Ge et al., 2016). This is because HMs are transferred from coal gangue heaps to nearby SS by surface runoff, dust, and leachate (Yao et al., 2023; Dong et al., 2024; Ma et al., 2023b). The coal gangue heap’s location in mountainous terrain limits dust diffusion into the atmosphere. Additionally, a mountain barrier that separates the study and control zones effectively protects the soil in the control area from significant impacts. However, due to its higher elevation, the coal gangue heap’s leachate infiltrates the study area’s soil through rainwater runoff. It is thus preliminarily concluded that Cd, Hg, As, and Pb contents near the coal gangue heap in the study area are primarily influenced by surface runoff, while in the control area, these elements are not significantly affected by the coal gangue. The spatial distribution of Cu, Zn, and Ni in the study area was similar (Figures 3f–h); as the proximity to the coal gangue heap decreased, their contents decreased, indicating a lesser influence from surface runoff. Correlation analysis indicated a significant positive correlation among Cu, Zn, and Ni (P ≤ 0.01, r = 0.60–0.73 in the study area; P ≤ 0.01, r = 0.77–0.86 in the control area) (Figures 4a,b), suggesting these metals may share similar sources of pollution, excluding the coal heap. The spatial distribution of Cr was notably dissimilar to other HMs (Figure 3e), leading to the preliminary conclusion that Cr’s pollution sources differ from those of other HMs.

Figure 3

Figure 3. Spatial distribution of HMs in SS.

Figure 4

Figure 4. Correlation among HMs of SS in the study area (a) and control area (b).

3.3 Vertical distribution of HMs contents in soil profiles

Figure 5 provides data on soil profiles, showing that in the study area, the contents of Cd, Hg, As, Pb, Cu, Zn, and Ni are higher in SS (0–20 cm) than in subsoil (20–60 cm) and substratum (60–100 cm). This pattern indicates that HM pollution in SSs is largely due to anthropogenic sources (Huang et al., 2017; Liang et al., 2021). Notably, in the study area, contents of Cd, Hg, As, and Pb were consistently higher across all soil layers than in the control area. Particularly near coal heaps (T1, T6, T8), elevated levels suggest migration from surface to deeper layers due to coal gangue accumulation, increasing their presence in subsurface soils (Luo et al., 2024). In contrast, the contents of Cu, Zn, and Ni were similar across soil layers in both the study and control zones, indicating that their contamination might be linked to anthropogenic sources other than coal heaps. Additionally, the uniform chemical composition of Cr across various depths suggests it predominantly originates from parent materials (Bini et al., 2011).

Figure 5

Figure 5. Vertical distributions of HMs in soil profiles.

3.4 Driving factor analysis based on GDM

Figure 6 illustrates the results of factor test. In the study area, Cd, Hg, As, and Pb demonstrated the strongest explanatory power for variable X₃ (distance from the coal gangue heap), with q-values of 0.524, 0.313, 0.712, and 0.442, respectively. This supports the spatial distribution findings, offering quantitative evidence that the coal gangue heap significantly influences these HMs. Conversely, Cr, Cu, Zn, and Ni had the strongest explanatory power for variable X₅ (distance from residential zones), with q-values of 0.387, 0.422, 0.514, and 0.338, respectively. In the control area, Cd, As, Pb, Zn, and Ni showed the most significant influence for variable X₄ (distance from the main road), with respective q-values of 0.363, 0.391, 0.387, 0.670, and 0.714; Hg, Cr, and Cu were most influenced by variable X₅, with q-values of 0.311, 0.392, and 0.456. Prior research has shown that proximity to roads and residential zones often leads to increased human activities, such as road traffic emissions and agricultural practices, which in turn accumulate HMs in soil (Atafar et al., 2010; Cho and Newman, 2005; Zhang et al., 2012; Han et al., 2019). Therefore, it can be inferred that the contents of Cr, Cu, Zn, and Ni in the study area, as well as all HMs in the control area, were influenced by rural residents’ production and lifestyle.

Figure 6

Figure 6. Explanatory power of X₁∼X₆ on influence of HMs in the study area (a) and control area (b).

3.5 Source apportionment of HMs in soil

The Kaiser-Meyer-Olkin (KMO) values in the study and control zones were 0.568 and 0.677, respectively, both above the threshold of 0.5, validating the appropriateness of conducting principal component analysis (PCA) (Supplementary Table S2) (Wang et al., 2020). The PCA, performed using the APCS-MLR receptor model, indicated strong predictive capabilities for HMs, with R² values of 0.966, 0.903, 0.669, 0.779, 0.907, 0.830, 0.938, and 0.862 for Cd, Hg, As, Pb, Cr, Cu, Zn, and Ni in the study area; and 0.745, 0.672, 0.744, 0.731, 0.988, 0.623, 0.815, and 0.882 for the same metals in the control area (Duan et al., 2020).

Source apportionment outcomes illustrated in Figure 7 reveal that in the study area (Figures 7a,b), PC1, PC2, and PC3 accounted for 40.1%, 33.7%, and 26.2% of the variance, respectively. Predominantly, Cd, Hg, As, and Pb were influenced by PC1, with contributions of 78.8%, 62.9%, 60.8%, and 60.7%, respectively. The analysis of both surface and profile soil data suggests these elements are majorly affected by anthropogenic factors. Spatial analysis via GIS demonstrated an increase in the contents of these HMs near the coal gangue heap. Moreover, the quantitative analysis employing the GDM revealed that X₃ (the distance from the sampling site to the coal gangue heap) provided the most significant explanation for the variation in these HMs. Research indicates that the redox reactions of pyrite in coal gangue lead to the release of HMs as sulfide compounds, which persistently migrate to the adjacent soil, propelled by surface runoff (Sun et al., 2021; Dong et al., 2024; Zhao et al., 2024). Thus, it is posited that PC1 is primarily derived from mining activities, significantly affecting the soil’s content of Cd, Hg, As, and Pb through the accumulation of coal gangue. Conversely, Cu, Zn, and Ni were mostly impacted by PC2, with respective contributions of 79.5%, 78.5%, and 50.6%. GDM analysis showed that X₄ (distance to the main road) was most explanatory for these metals. The proximity to residential zones and thoroughfares elevates human activity, enhancing land use and potentially elevating soil contamination levels (Atafar et al., 2010; Cho and Newman, 2005; Zhang et al., 2012; Han et al., 2019). It is hypothesized that PC2 stems predominantly from vehicular emissions and the usage of fertilizers and pesticides. Cr, mainly affected by PC3 with a 69.4% contribution, exhibited the lowest CV in SSs, suggesting uniform levels across different soil depths (Yang et al., 2021; Delbari et al., 2019; Bini et al., 2011). These findings are corroborated by other studies, which attribute natural sources as the principal contributors to the Cr content in Chongqing’s SS (Ma et al., 2024a; Yao et al., 2024; Dong et al., 2018).

Figure 7

Figure 7. Contributions of sources calculated by APCS-MLR receptor model in the study area (a,b) and control area (c,d).

In the control area (Figures 7c,d), contributions from PC1, PC2, and PC3 were 37.1%, 36.2%, and 26.7%, respectively. Predominantly, Pb, Cu, Ni, and Zn were influenced by PC1, with contribution percentages of 54.1%, 68.7%, 84.3%, and 91.5%, respectively. Numerous studies have shown that elevated levels of these metals primarily arise from vehicular sources. For instance, significant Cu content in brake pads and their subsequent wear contribute to Cu release (Jeong et al., 2022); the inclusion of ZnO and ZnS in tires is a recognized source of Zn from tire wear (Jeong, 2022; Adamiec et al., 2016); historical use of leaded gasoline has notably led to Pb accumulation (Ye et al., 2022; Eichler et al., 2015); Ni, frequently included in tire and brake pad compositions, serves to bolster the resistance of these materials against plastic deformation (Jeong, 2022; Zhang P. et al., 2020). Given the strategic location near roads in the control area, it is theorized that PC1 mainly arises from road traffic emissions. Cd, Hg, and As, influenced by PC2 with contributions of 63.7%, 95.3%, and 68.4%, respectively, alongside Pb, significantly affected at 42.2%, underscore the impact of agricultural practices. Research shows Cd and Pb may precipitate as cadmium and lead phosphates in soils due to their presence in phosphate fertilizers (Jastrzębska et al., 2018; Dharma-wardana, 2018); similarly, phosphorus fertilizers contribute substantially to Hg presence in soils (Tang et al., 2018). Oyedele et al. (2006) noted significant rises in Cd, Pb, and Hg levels following superphosphate fertilizer application. Moreover, arsenic contents in soils are known to increase with the usage of arsenic-containing herbicides and pesticides (Atafar et al., 2010; Defarge et al., 2018). Field studies confirm the control area’s predominant agricultural land use, mainly employing nitrogen and phosphorus fertilizers, leading to the supposition that PC2 originates largely from fertilizer and pesticide application. Finally, Cr, chiefly influenced by PC3 with a 78.5% contribution, showed minimal variability across soil depths, reinforcing the notion that natural sources predominantly dictate Cr distribution, mirroring findings from the study area.

In summary, the soil in the study area was predominantly influenced by the ongoing accumulation of coal gangue (PC1), which accounted for 40.1% of the impact. Additionally, road traffic emissions, together with the use of fertilizers and pesticides (PC2), contributed 33.7%. Natural sources (PC3) also played a role, accounting for 26.2%. Conversely, in the control area, the primary influences were road traffic emissions and the use of fertilizers and pesticides (PC1 and PC2), contributing a combined 73.3%, followed by natural sources (PC3), which accounted for 26.7%.

3.6 Cd isotope

Figure 8a depicts the Cd isotopic composition relative to the inverse Cd content in soils and coal gangues. Previous research has demonstrated that weathering and surface runoff enhance the release of HMs from coal gangue, particularly those that are surface-weathered compared to unweathered samples (Guo et al., 2024; Li et al., 2021). Leaching from mine tailings into creeks causes heavier Cd isotopes to be preferentially transported into aqueous solutions, while lighter isotopes tend to remain in the solid phase (Imseng et al., 2018; Wang et al., 2023; Zhang et al., 2016). This study observed a similar trend in coal gangue samples, where δ¹¹⁴/¹¹⁰Cd values ranged from −0.208‰ to 0.024‰, with samples CG1 and CG2 (0–50 cm in depth) exhibiting higher values than CG3, CG4, and CG5. Furthermore, Cd isotopic ratios in soil samples from the study area varied from −0.038‰–0.316‰, indicating higher ratios than those in the control area. Notably, the content of heavy Cd isotopes in the SS decreases as the distance from the coal heap increases, as shown by the sequence: T1 (0.316‰) > T6 (0.117‰) > T8 (0.092‰) > T10 (-0.038‰). This suggests that heavy Cd isotopes from coal gangue migrate to the surrounding soil via surface runoff, enriching nearby soil with these isotopes. This aligns with findings by Yao et al. (2023). In contrast, Cd isotopic ratios in the control area’s soil samples were consistently lower, spanning −0.441‰ to −0.187‰. Research by Yan et al. (2021) has shown that both compound and phosphate fertilizers, as well as road traffic emissions, contain light Cd isotopes, with values of −0.43 ± 0.03‰, −0.15 ± 0.04‰, and −0.21 ± 0.03‰, respectively. Zhang X. W. et al. (2020) also indicated similar isotopic values in fertilizers and emissions, with −0.51 ± 0.06‰ and −0.40 ± 0.01‰, respectively, closely matching the range found in the control area soils. By integrating results from the APSC-MLR model and GDM, it is speculated that soil Cd in the control area is primarily affected by road traffic emissions and the application of fertilizers and pesticides.

Figure 8

Figure 8. Cd isotopic composition versus the inverse Cd content (mg/kg) of soils (a) and coal gangues, Cd isotopic composition versus the depth of soils (b).

Figure 8b illustrates the isotopic composition of cadmium in relation to soil depth. In the research site, the mean δ¹¹⁴Cd/¹¹⁰Cd ratios for the surface, subsurface, and bottom soils were recorded at 0.122‰, −0.039‰, and −0.051‰, respectively. In the control area, the observed values were −0.241‰, −0.293‰, and −0.319‰, respectively, suggesting a decline in the δ¹¹⁴Cd/¹¹⁰Cd ratio as soil depth increased. This indicates that topsoil is more profoundly affected by human activities (Yao et al., 2023). The elevated ratios of Cd isotopes observed in each soil layer of the study area, in contrast to the control area, indicate that heavy Cd isotopes are likely migrating downward from the surface, possibly facilitated by rainwater infiltration (Gao et al., 2021). Consequently, the longstanding accumulation of coal gangue has significantly impacted the deep soil surrounding it.

4 Conclusion

In this study, we designated specific study and control zones, utilizing the APCS-MLR model, GDM, and Cd isotope fingerprints to ascertain the origins of HMs in the soil surrounding coal gangue heaps. The findings revealed that pollution levels were elevated in the study area compared to the control area, with Cd emerging as the primary contaminant. The integration of the GDM, APCS-MLR model, and Cd isotope fingerprints significantly improved the precision and dependability of our source apportionment efforts. In the study area, soil contamination was predominantly due to the persistent accumulation of coal gangue (PC1: Cd, Hg, As, and Pb), emissions from road traffic, and the usage of fertilizers and pesticides (PC2: Cu, Zn, and Ni), along with natural sources (PC3: Cr), contributing 40.1%, 33.7%, and 26.2% respectively. In contrast, the control area’s soil was mainly influenced by road traffic emissions and the application of fertilizers and pesticides (PC1 and PC2: Cd, Hg, As, Pb, Cu, Zn, and Ni), and by natural sources (PC3: Cr), with respective contribution rates of 73.3% and 26.7%. Surface runoff from coal gangue accumulation emerged as the dominant pathway for soil contamination in the study area. Thus, forthcoming research should prioritize effective coal gangue management strategies and address the ecological threats posed by elevated Cd levels in the study area.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

JM: Conceptualization, Formal Analysis, Investigation, Visualization, Writing – original draft, Writing – review and editing. YJ: Data curation, Investigation, Visualization, Writing – original draft. ZS: Data curation, Investigation, Visualization, Writing – original draft. BZ: Data curation, Investigation, Visualization, Writing – original draft. HZ: Conceptualization, Formal Analysis, Investigation, Writing – review and editing. XY: Conceptualization, Formal Analysis, Investigation, Writing – review and editing. XF: Investigation, Writing – original draft. DS: Investigation, Writing – original draft.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by Chongqing Science and Technology Bureau Project (CSTB2024JXJL-YFX0061) (CSTB2024JXJL-YFX0017), and National Natural Science Foundation of China Project (42077001).

Conflict of interest

Author ZS was employed by China Merchants Ecological Environmental Protection Technology Co., Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1582799/full#supplementary-material

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