1. Introduction
Con With the rapid economic development around the world, the development of mineral resources has brought huge economic and social benefits to mankind. However, according to a rough estimate by researchers, for every 1 ton of metal extracted, 2–12 tons of waste tailings are produced [1]. The remaining tailings from mining, as solid waste, are mostly piled up in the open environment, which not only occupies a certain amount of land area, but also most of the open tailings piles are untreated in the continuous leaching of rainwater and weathering, PTEs may pose long-term impacts on the ecological environment and human health through pathways such as the food chain and bio-accumulation [2]. The mobility of PTEs varies in different environments. Studies have shown that fine particles have a larger specific surface area and stronger adsorption capacity compared to coarse particles, which can reduce PTEs mobility [3]. Therefore, investigating the physicochemical parameters that influence PTEs migration is of significant importance. The most important source of Sb in natural conditions is pyroxene, which in epigenetic environments is weathered to produce antimony-bearing minerals such as Sb2O3, Sb3O6OH, and other oxidized minerals [4]. Research has shown that Sb pollution is a global problem [5–7]. Not only that, antimony tailings contain As, Pb and Cd, which are also highly toxic in low concentrations [8–9]. Therefore, analyzing PTEs in antimony tailings is of great significance, and also provides a scientific basis for evaluating its pollution risk to the surrounding environment.
This study is based on the antimony tailings in DaChang Town, Qinglong County, Qianxinan Prefecture, Guizhou Province, which has the highest antimony resources in China. The mine was closed at an earlier stage due to environmental damage caused by over-exploitation, but there is always a risk of ecological contamination from piles of antimony tailings. In order to solve the current situation of environmental pollution of pyroxene mine, most scholars have carried out a series of studies on the mine, for example: Hu et al carried out a study on the mineralization of primary deposits [10], Chen et al conducted a statistical survey on the impact of local mining activities on the hydrogeochemical evolution of groundwater and mineralization, and some of the researchers evaluated the potential resources of tailings, as well as studied the distribution of contaminating elements of the environmental media in the mining area and the screening of heavy-metal-enriched plants in such area [11–12]. Due to the accumulation of tailings in open environments, PTEs are prone to migrate into the surrounding environment, which has led more scholars to focus on evaluating the surrounding soil and vegetation [13]. As a result, there is limited research on the physicochemical parameters affecting the migration rate of PTEs in antimony tailings, as well as on the assessment of pollution and health risks associated with this migration.
Traditional pollution assessments typically employ indices (For example, Geoaccumulation Index, Potential Ecological Risk Index, etc) to evaluate PTEs in tailings, with subsequent qualitative pollution analysis [14–16]. The geoaccumulation index (Igeo), which was first proposed by Muller at the University of Heidelberg [17–18], is nowadays used for the evaluation of PTEs contamination in modern sediments, soils, and dustfall. And the Potential Ecological Risk Index (RI) was proposed by Hakanson (1980). This method is proposed from the sedimentological point of view to evaluate PTEs contamination in soil or sediment [19]. Although useful for preliminary risk screening, these methods lack quantitative accuracy. Our study introduces weighted multi-index integration to derive comprehensive environmental risk scores. Diverging from conventional approaches, we implement the USEPA-recommended model enhanced with Monte Carlo simulation [20–22]. This probabilistic framework establishes exposure parameter distributions, performs computational iterations, and generates risk probabilities-effectively addressing traditional models’ limitations while pinpointing critical risk factors through sensitivity analysis [23–25]. Notably, while extensively applied to mining-area soil heavy metals, this methodology remains underutilized for antimony tailings. Our work specifically addresses this research gap through systematic risk characterization.
The process of occurrence, release and migration of PTEs in tailings cannot be ignored. Therefore, the objectives of this study are: i) To select the tailings from an antimony tailing pile as the study subject, and determine the mineral composition of the tailings as well as the concentrations of PTEs (Sb, As, Ni, Cr, Zn, Pb, and Cd); ii) To evaluate the environmental pollution risk caused by PTEs in the antimony tailings using the Igeo, RI, and single-factor index evaluation, and to weight the four results to obtain a comprehensive environmental pollution risk score; iii) To design and conduct static leaching experiments on the tailings to simulate the pollution conditions of PTEs in the tailings following rainfall in a natural environment; iv) To understand the leaching behavior of PTEs in antimony tailings through static leaching tests, and use principal component analysis to evaluate the PTEs pollution in water, identifying the main contributors to PTEs pollution, and compare these results with the comprehensive pollution risk score to obtain a more comprehensive evaluation of the PTEs pollution risk in antimony tailings. This will help provide theoretical support for leaching risk assessment of antimony tailings in aquatic environments; v) Finally, to conduct a probabilistic evaluation of PTEs in the tailings using Monte Carlo simulation.
Additionally, the Chinese government has introduced a series of laws and policy measures aimed at strengthening the prevention and control of pollutants of PTEs in tailings, as well as risk prevention and mitigation. The findings of this study provide a theoretical foundation for subsequent pollution and risk prevention efforts, and they also have significant implications for safeguarding the health of residents living near tailings storage sites.
2. Materials and Methods
2.1. Sampling and Analysis
The antimony tailings samples are located in Dachang Town, Qinglong County, Qianxinan District, Guizhou Province, an overview map of the sampling sites is shown in Fig. 1. Antimony ore is the main mineral resource in this area, and most of the ore-bearing rock bodies are from the Dachang layer (Pd). The main exposed strata in the study area are Permian (P) and Quaternary (Q). The oldest exposed stratum is the Middle Permian Maokou Fm (P2m), which consists of a set of shallow marine tuffs, flint-striped tuffs, dolomitic tuffs, and dolomites, followed by the Dachang layer (Pd), which is the main ore-controlling mineral-bearing stratum, and is characterized by strong silicification and claying, and is followed by the Emeishan Basalt Fm (P3β), Upper Permian Longtan Fm (P3l), and the Quaternary (Q). The mineralogical composition of the ores in this district is relatively simple, and the metallic minerals are dominated by pyroxene. The vein minerals are quartz, kaolinite, pyrite, and clay minerals. We have taken a detailed look at the study area, making it clear that there are large quantities of tailings and mining waste from open pit mining spread around the hills of the site.
The central area of the tailings site was selected as the sampling point in the study region (E105°00′, N25°30′–25°50′), with sampling conducted at its central zone where artificial stacking and land-filling processes had created heterogeneous material distributions. Adhering to China’s DZ/T 0429-2023 standard for solid mineral exploration [26], we implemented stratified sampling across vertical profiles of the tailings heap. Specifically, drilling techniques and splitters were employed to collect tailing samples from the surface, middle, and bottom layers of the tailings heap. A total of five groups of tailings samples (labeled TK1-TK5) were collected. All tailings samples were stored in sterile, sealed bags and transported to the laboratory, kept away from light. After natural air drying, each sample was repeatedly ground using an agate mortar to ensure all particles could pass through a 200-mesh sieve. The mineral composition of the tailings samples was characterized using X-ray diffraction (XRD, Catalysis), and elemental composition was determined using a handheld X-ray fluorescence (XRF) spectrometer (Skyray). To simulate real-world conditions in tailings storage areas, a static leaching test was conducted. Particle size analysis of a typical tailings repository revealed that particles ≤1.18 mm accounted for approximately 30%–60% of the total mass, while particles in the 1.18 mm-4.75 mm range formed the primary structural framework of the tailings pile. This classification corresponds to the standard sieve specifications used in engineering screening equipment [27]. Therefore, for experimental purposes, the samples were classified into two groups: tailings with particle sizes ≤1.18 mm and those in the 1.18 mm-4.75 mm range.
As shown in Fig. 2, columns I to III were used to study the release behavior of PTEs from antimony tailings after infiltration leaching with deionized water. Details of the conditions are given in Table 1. Columns I and II were filled with 1kg of air-dried antimony tailings with particle sizes <1.18 mm and 4.75 mm, respectively, and column III was filled with air-dried antimony tailings with mixed particle sizes (500 g of each of the above particle sizes were mixed homogeneously). The maximum particle size of the filled antimony tailings (4.75 mm) was 1/20 of the column diameter (9 cm) to minimize the preferential flow of the solution within the column. To prevent sample particles from falling out of the column. Fill the upper and lower ends of the column with fine-grained quartz sand and clean it with deionized water and filter paper before sealing. According to the United States Environmental Protection Agency (USEPA) method [27], the flow rate was set to 5.63 L/h when filling the column with a solution to increase the possibility of local equilibrium in the solid-liquid phase, and 5 L of deionized water (at a solid-liquid ratio of 5:1) was added with this flow rate. Afterward, 50 ml of each of the filtrate at the bottom of the column and the immersion solution in the column were collected at the beginning of the experiment at the time intervals of 0 h, 1 h, 3 h, 4 h, 5 h, 7 h, 10 h, 15 h, 24 h, 72 h, 120 h, 168 h, 216 h, and 576 h, and the pH, electrical conductivity (EC) and Oxidation-Reduction Potentia (ORP) of the water samples were measured immediately on-site with a portable water quality analyzer (MULT2620WTW, Germany). Afterward, the water samples were filtered through a 0.45 mm membrane and acidified by adding 68% HNO3 (the purpose is to dissolve the heavy metal ions, mainly to prevent hydrolysis), then stored in the environment of 4°C. Finally, the concentrations of the PTEs in the water samples were analyzed by ICP-MS.
2.1.1. Quality assurance and quality control
To ensure the reliability of detecting PTEs using ICP-MS, the following quality control measures are implemented in accordance with the HJ 700–2014 standard [28]:
Standard Curve Calibration: Before analyzing each batch of samples, a standard calibration curve is constructed by preparing standard solutions for the target PTEs. The instrument measures these standards to generate the curve, ensuring that the correlation coefficient (R2) of the standard curve exceeds 0.999.
Internal Standard Monitoring: The internal standard intensity is monitored during each analysis, maintaining a response within 70%–130% of the standard curve.
Blank Sample Analysis: A laboratory blank is analyzed for every batch of samples to verify the absence of contamination or interference in the reagents and equipment.
Spiked Recovery Testing: Known amounts of analytes are added to blank reagents, with recovery rates required to be within 80%–120%.
Duplicate and Matrix Spikes: A purified matrix spike and a matrix duplicate spike are included per batch, with recovery rates between 70%–130% and duplicate deviations ≤20%.
Calibration Verification: Every 10 samples, an intermediate concentration point is analyzed, with relative deviations ≤ 10%. After completing each batch, the lowest concentration point of the standard curve is verified, with relative deviations ≤30%.
These rigorous quality control measures ensure the accuracy, precision, and reliability of PTEs determination using ICP-MS.
2.2. Evaluation Methods for PTEs Pollution and Risk Assessment
Due to the long-term accumulation of antimony tailings, the PTEs contained therein can be released significantly after rain. After release, they enter plants through water and soil, affecting ecological security. In the long run, they can also seep into the human body through the hands, mouth, and skin, which is hazardous to human health.
2.2.1. Environmental pollution risk assessment
The formula of Iego is:
where Ci is the measured PTEs content; k is a constant set to take into account the fluctuation of the background value that may be triggered by the rock-forming movement, and is generally taken as k=1.5; Bi is the geological background value of the element. Table S4 lists the background value.
The expression of the potential ecological hazard index method is as follows:
where
is the individual potential ecological risk factor for PTEs i;
is the toxicity response factor for PTEs i, the toxicity response coefficients for the elements Sb, Pb, Zn, Ni, Cr, Cd, As, and Cu were 7, 5, 1, 5, 2, 30, 10, and 5, respectively [29].
is the single contamination factor for PTEs i;
is the average of the concentrations of PTEs i in tailings from at least five sampling sites; and
is the background value of soil contaminants in the study area.
The background value of PTEs and the classification of pollution index are shown in Table S4 and Table S1 respectively.
The categorization of the degree of PTEs contamination was consistent for all three evaluation methods (none, low, medium, considerable, high, very high, and extremely severe) (Table S1). The focus of the three evaluation methods, although different, centers on the two most important factors: the amount of stockpiles and the pollution that has been caused. In summary, this study assumes that PTEs concentration and PTEs pollution level contribute equally to ecological risk. The three evaluation results were scored based on their ranks, and each score was weighted to obtain a comprehensive result. The synthesized results serve as the comprehensive evaluation results. The weighting method assigns a value of 0 to a “none” pollution level and values from 1 to 6 to low, medium, medium-high, high, very high, and very severe categories, respectively. A composite PTEs pollution risk score (PERs) is then derived.
where Sx(Iego) is the degree score of geological accumulation of PTEs, Sx(Cf) is the degree score of PTEs in terms of mono-metallic pollution content, and Sx(Er) is the degree score of potential ecological risk of PTEs.
Principal component analysis (PCA) screens out the main PTEs from many variables, with fewer new composite variables instead of the original variables, and these new composite variables can maintain as much original information as possible. In addition, this method can be more objective in determining the weight of each PTEs indicator to avoid subjective arbitrariness. The use of this method objectively reflects the quality status of the water body.
The equation for the cumulative release of PTEs from solution is:
where q is the cumulative release of PTEs from antimony tailing in the static leaching experiment, mg /kg; Ci is the concentration of PTEs in the solution collected for the ith time, mg /L; Vi is the volume of the solution sample collected on the i day, L.
The specific steps are summarized below after references : Standardization of selected data to eliminate the effects of scale and order of magnitude among different indicators.
The KMO (Kaiser-Meyer-Olkin) test statistic and Bartlett’s test of sphericity was used to determine the correlation between the indicators and determine whether the original variables were suitable for factor analysis. Factor analysis can be performed when the KMO value is >0.5. Bartlett’s sphericity test is obtained based on the determinant of the correlation coefficient matrix. When the approximate chi-square value is significant and its corresponding significance <0.05, the null hypothesis should be rejected, i.e., the correlation coefficient matrix is not likely to be a unit array, and a correlation exists between the original variables, which allows the principal component analysis to be performed.
Determine the number of principal components. Determine the principle: i. eigenvalue λ>1 corresponding to the principal components; ii. accumulated percentage of λ value of more than 80% of the corresponding principal components; iii. According to the gravel plot, changes in the mutation point are used to determine the number of principal components.
Creating principal component score expressions,
where ωij denotes the weight of each variable in the principal components. The score coefficient matrix can be built based on Eq. (8). Ti is the ith principal component and aij is the value of the jth water quality indicator of the ith principal component in the component matrix. xij represents the jth indicator of the ith evaluation object, while χ̄j and vj refer to the sample mean and sample standard deviation of the ith indicator.
Establish a composite score formula and calculate the principal component scores; the higher the composite score, the more serious the pollution,
in the equation: Ti is the composite score; Ci is the variance contribution ratio of the ith principal component.
The above calculation steps were performed in SPSS 25.0.
2.2.2. Monte Carlo method for health risk assessment
The Monte Carlo method employs probabilistic simulations to quantify uncertainty and health risks through randomized sampling [33]. This study adopts the U.S. Environmental Protection Agency (USEPA) health risk assessment model [27], stratifying populations into adults and children for evaluating PTEs exposure risks in tailings. Three exposure pathways are examined: oral ingestion, dermal contact, and inhalation. The corresponding daily soil intake formulas are:
In the formula, ADDing, ADDdrem, and ADDinh represent the average daily intake of PTES through oral ingestion, dermal contact, and inhalation, respectively. Ci indicates the ith heavy metal concentration. Statistical distributions of all parameters are systematically detailed in Table S2.
Health risks are categorized as carcinogenic (CR) and non-carcinogenic (NCR). The NCR evaluation determines potential adverse health effects from PTES exposure in tailings, establishing whether exposure levels exceed safety thresholds. CR quantifies lifetime cancer probability under defined exposure scenarios. The risk assessment employs the following formulations:
where Hazard Index (HI) and Hazard Quotient (HQ) represent the combined and individual non-carcinogenic risk indices, respectively, and Total Carcinogenic Risk (TCR) and Carcinogenic Risk (CR) represent the combined and individual carcinogenic risk indices, respectively. ADDij refers to the average daily intake of element i via pathway j, while RfDij is reference dose and SFij is slope factor. Specific reference values are shown in Table S3. The acceptable level for non-carcinogenic risk is < 1 and the value of CR should not exceed 1×10−6. For CR, 1×10−4 to 1×10−6 is the acceptable or tolerable risk range, and there is a potential carcinogenic risk if CR > 1×10−4 [24]. The study utilized Oracle Crystal Ball 11.1.2.4 to perform Monte Carlo simulation-based human health risk assessment, with 10000 iterations and a 95% confidence level. Data processing and visualization were conducted using Origin 2024.
3. Results and Discussion
3.1. PTEs Pollution Assessment
3.1.1. Mineral composition and PTEs content
After X-ray detection (XRD) analysis and statistics, the different mineral compositions of the collected antimony tailings (TK1–5) revealed a significant trend in their percentages. As shown in Fig. S1, the primary minerals present in each sample are quartz (SiO2) and calcite (CaCO3). The remaining minerals include gypsum (CaSO4–2H2O), jarosite (KFe3(SO4)2(OH)6), fluorite (CaF2), pyrite (FeS2), dickite (Al4[Si4O10](OH)8), dolomite (CaMg(CO3)2), destinezite (FeSO4·7H2O). Small amounts of stibnite (Sb2S3), hydrotalcite (Ca3Si3O9(OH)4), anatase (TiO2) were also detected. The complexity of the mineral composition of the samples is attributed to the use of a natural karst funnel as the tailings storage at Qinglong Antimony Mine. Three types of tailings—smelting waste tailings, hand-selected tailings, and flotation tailings—are piled up in the tailings storage, resulting in their mixture due to human activities. Table S4 summarizes the PTEs content in antimony tailings (TK1–5) detected by XRF, showing that the average PTEs content in the tailing samples is in the order of Sb>As>Ni>Cr>Zn>Pb>Cd. The antimony tailings contain high PTEs contents, particularly Sb and As. This is because the cumulative amount of antimony metal proven at the Dachang antimony mine in Qinglong amounted to 300000 tons [34]. The high As content results from the development of a large amount of ring belt pyrite in the Qinglong antimony deposit [35–37], which is enriched in As (up to 5.02%) at its margins. Additionally, during the evolution of the metallogenic fluids, the quartz-galena ± pyrite assemblage at the late stage of mineralization was characterized by a high As content compared to the quartz-galena ± fluorite assemblage at the early stage, accompanied by a certain degree of the pro-S element Cu [38]. The low relative content of elemental Cu is attributed to the pyroxene generated in the late stages of mineralization being weakly S-lossy, leading to a low associated pro-S elemental Cu content. The average values of the remaining PTEs differ slightly, likely due to the migration patterns of each PTEs contaminant.
As shown in Table S4, the geologic enrichment of PTEs can be seen using Igeo. It can be seen that Sb, As and Zn are extremely enriched, indicating that these three elements in antimony tailings are the main source of pollution. Meanwhile, Cd belongs to partial heavy enrichment, Ni belongs to medium enrichment, Cr and Pb belong to light enrichment, and only Cu belongs to no enrichment. The contamination level and potential biological risk of PTEs in antimony tailings were further analyzed using Hakanson’s potential ecological risk assessment method. As shown in the Table S4, the highest total monometallic contaminant content index Cf of Sb was 6741.07, which led to the highest Er of Sb at 6781.07, reaching a very high risk. Elemental As is the next highest, Cf of 65.5 shows extremely high contamination, and its Er of 85.5 is also high risk. Ni and Cd have a Cf greater than 6, which is highly contaminated, and Cu, Zn, Pb, and Cr have a Cf between 1 and 3, which is moderately contaminated, but the Er of all six elements is less than 40, which is low risk. The RI for the entire group of antimony tailings was 6948.28, indicating a very high potential ecological risk.
3.1.2. Potential ecological risk assessment
As shown in Fig. 3, the combined scores are, in descending order: PERs(Sb)>PERs(As)>PERs(Zn)=PERs(Cd)>PERs(Ni)>PERs(Pb)> PERs(Cr)>(Cu). It can be seen that antimony tailing is very harmful to the study area’s environment. The remaining PTEs content poses a considerable hazard to the surrounding ecosystems and humans, making this pollution risk evaluation extremely meaningful. The study evaluated the contamination levels of Pb, Zn, Cu, Ni, Cr, Cd, and Sb in antimony tailings using three common methods: the geologic cumulative index evaluation method, the single-metal contamination evaluation method, and the potential ecological risk evaluation method. From Table S4, it is evident that there are differences in the results of the three evaluation methods. For example, in the case of Zn and As, Zn had very severe, medium and low levels of contamination under the Iego, Cf and Er evaluation methods, respectively; and As had very severe, high and moderately high levels of contamination, respectively. The element Cu, in particular, is non-polluting in the Iego evaluation method, but presents medium and low levels of pollution under Cf the Er and evaluation methods, respectively. These differences arise from the varying emphasizes of the three evaluation methods. The Iego evaluation method focuses on the degree of enrichment of PTEs in antimony tailings, the Cf evaluation method focuses on the degree of contamination of PTEs compared to background values in the study area, and the Er evaluation method focuses on the individual potential ecological risk coefficients of the PTEs based on a specific toxicity coefficient of the PTEs [38]. Most of the PTEs in the surrounding environment migrate from the tailings. To obtain a comprehensive result that considers all factors, the three results were weighted to derive a comprehensive pollution risk score. To verify the accuracy of the comprehensive evaluation results, a static leaching test of antimony tailings with different particle sizes was conducted using a simulated field environment.
3.2. Characterization of PTEs in Static Leaching Experiments
The interaction among solution pH, EC, and ORP serves as a critical determinant in the migration and transformation of PTEs. These three parameters jointly govern the environmental behavior of heavy metals in solution through a integrated dynamic system [39–41].
pH exerts principal control over aquatic metal speciation through dissolution-precipitation equilibria, as evidenced in Fig. 4(a), (b) documenting biphasic evolution: an initial rapid acidification phase (pH 2.93–6.81) exhibiting marked intergroup disparities, followed by stabilization with attenuated oscillations (pH 4–6). This trajectory reflects progressive neutralization of acidic leachates during extended leaching duration. Fig. 4(c), (d) show EC, which is related to the ionic concentration in the solution and is measured in μs/cm. Bottom-column effluent consistently exceeded 2000 μs/cm across all particle fractions within 24 h, contrasting with top-column measurements remaining below 500 μs/cm demonstrating enhanced PTEs mobilization through denser tailings matrices. ORP describes the ability of water to donate or accept electrons, reflecting the tendency for oxidation or reduction reactions. It is typically measured in millivolts (mV). Fig. 4(e), (f) show the variation in ORP values during the experiment. In water samples collected from the bottom of the column, when the particle size is within the 1.18 mm-4.74 mm range, ORP values exceed 200 mV, favoring metal oxidation states. While ≤1.18 mm materials maintained reductive microenvironments (ORP <100 mV).
The changes in the cumulative release of PTEs with increasing drenching time are shown in Fig. 5. The maximum value of the cumulative release of PTEs in water is Zn>Cu>Sb>Ni>As> Pb>Cr>Cd in descending order. Considering the measured pH, EC, and ORP results, the geochemical data revealed MT 2-1’s unique tripartite regime of elevated acidity, oxidative intensity and hypersalinity, which synergistically amplified the mobility of redox-sensitive metalloids (As) and transition metals (Cd, Pb, Cu). In contrast, Ni and Sb were more soluble under acidic, high-salinity conditions with relatively lower ORP. Conversely, Ni and Sb exhibited anomalous solubility peaks under acidic, high-salinity conditions with relatively lower ORP conditions—a phenomenon conflicting with classical oxidation-mobility paradigms. This divergence stems from Ni2+ sequestration via Fe/Mn oxide coprecipitation at ORP >200 mV [42], and the ORP may not be sufficient to fully oxidize Sb(III), leading to the formation of more stable soluble complexes, such as SbCl3 [43]. Zinc demonstrated inverse behavior, exhibited higher release under relatively higher pH, lower ORP, and lower EC conditions. This may be due to a significant increase in the proportion of Zn2+ in its free ionic form under such conditions [41]. Collectively, these findings underscore a critical geochemical transition: near-neutral conditions (pH 5–7, ORP <100 mV, EC <400 μs/cm ) suppressed all PTEs releases.
As shown in Fig. 5, the cumulative release of PTEs varies with different tailings particle sizes. The cumulative release of As, Cd, Pb, and Cr was greater when large particle sizes predominated (d=1.18 mm-4.75 mm). The cumulative release of Zn was greater when small particle sizes predominated (d<1.18 mm). The cumulative release of Sb and Ni was greater when both sizes were equally predominant. In addition, as shown in the Fig. 5, there are obvious differences in the PTEs content of water samples taken from different locations in the column. Most of the accumulated release of PTEs in the filtrate collected at the bottom of the column is greater than that in the leaching solution collected at the top. Obviously, when the solution passes through the tailing, it removes the PTEs adsorbed on the surface of the tailing. This result shows that PTEs contamination precipitated by rainwater on tailing over a long period is much less than that released by rainwater through leaching. From Fig. 5(a), it can be seen that the cumulative release of Sb under MT 2-1 is smaller than that under MT 1–2 and MT 3-2. This indicates that particle size variation has a greater influence on the release of Sb element, with smaller particle sizes being more unfavorable for the release of Sb.
Cross-validation of static leaching data with composite risk indices reveals a critical duality in antimony tailings management: while solid-phase PTEs concentrations provide baseline contamination metrics, their hydrogeochemical transformation potential under evolving field conditions demands equivalent scrutiny. Over time, environmental conditions change due to prolonged weathering, potentially leading to acidic, highly oxidizing, and high-salinity conditions. Therefore, on-site monitoring should not only focus on the presence of PTEs in solid tailings but also on their concentrations in surrounding water bodies. The study results indicate that when assessing PTEs pollution in antimony tailings, it is essential to consider the actual environmental conditions, including the occurrence of PTEs in tailings, their release into the aquatic environment, and the physicochemical parameters influencing their mobility.
3.3. Water Quality Evaluation based on Principal Component Analysis
To clarify the main impact indicators, the study used principal component analysis to evaluate the results of static leaching experiments. This is of great significance for the prevention and control of water pollution in the antimony tailing accumulation area.
Table S5 shows that the KMO test metric values for the six groups of experimental data are all >0.5, and the significance of Bartlett’s sphericity test is <0.05, indicating a correlation between the original variables suitable for principal component analysis. After that, the number of principal components is determined, and the number of principal components can be judged according to the PCA chart (Fig. S2). According to the principal component formula, the principal component scores are calculated, and the higher the composite score, the more serious the pollution.
Table S6 shows that Ni is the main control index of the first principal component in six groups of static leaching experiments, while Zn is the main control index in other five groups, except for MT 3-1. Cr, Cd, and Sb appear in the second principal component. Therefore, based on the importance of water quality indexes, the comprehensive pollution control indices characterizing the water quality of static leaching experiments can be summarized as Ni, Cu, Zn, As, and Pb, with special attention to Ni and Zn.
The box plots of the composite principal component scores for the six groups of data from the static leaching experiments are shown in Fig. 6. Analysis of Fig. 6 shows that for the water samples taken at the bottom, the maximum value of the principal component composite score is obtained when the tailing particle size is ≥4.75 mm, followed by 1.18 mm-4.75 mm, with <1.18 mm being the smallest. For the water samples taken at the top, the maximum value of the principal component composite score is obtained when the tailing particle size is 1.18 mm to 4.75 mm, followed by <1.18 mm, with ≥4.75 mm being the smallest. Therefore, the PTEs pollution in the MT2-1 group was relatively high. Additionally, water sample MT2-1 had the widest distribution of composite scores, while water sample MT3-2 had the smallest. This indicates that water quality pollution fluctuated more in MT2-1 and less in MT3-2 throughout the experiment.
Based on this section, it is worth noting that the transport of PTEs from antimony tailing to the nearby environment is controlled by several factors: particle size of the tailings, duration of leaching, direction of solution flow, physicochemical properties of the tailings, and transport distance. It can be reasonably predicted that the input flux of PTEs to the local environment will increase dramatically once the sulfide minerals in the tailing undergo extensive weathering and oxidation. Moreover, combining the composite score of PTEs pollution risk in antimony tailing samples with the composite score of principal components from water samples in static leaching experiments highlights the need to monitor Sb, Zn, Ni, and Cd pollution. Additionally, particle sizes of 1.18 mm-4.75 mm have a greater negative impact on the environment. Therefore, it is necessary to minimize the contact of tailing with water and air, and reduce the production of these particle sizes to effectively reduce PTEs leaching.
3.4. Health Risk Assessment of PTEs in antimony tailings
Health risk assessment is a powerful tool for quantifying the potential human health risks posed by toxic substances in tailings. The primary exposure pathways for human contact with PTEs in tailings are ingestion, dermal contact, and inhalation [44].
The results of the non-carcinogenic health risk assessment for the collected antimony tailings are presented in Table S7 and Fig. 7(a), (c), (d). The mean HI values for adults and children were 3.29E+00 and 4.37E+01, respectively. In children, all HI values exceeded 1, indicating a high non-carcinogenic health risk from antimony tailings for both children and adults. This elevated risk is primarily due to ingestion, which is the dominant exposure pathway, as children are more likely to ingest tailings particles through hand-to-mouth behavior near the deposition site [45]. Among the eight analyzed PTEs, only As and Sb had maximum HQ values exceeding 1(Table S8). Found that the mean non-carcinogenic health risks for As and Sb in adults were 1.72 and 1.49, respectively. 15% of As HQ values and 20% of Sb HQ values were below 1, suggesting that in some cases, the risk was within the acceptable range. However, in children, the mean non-carcinogenic risks for As and Sb were 2.3E+01 and 1.97E+01, respectively, with all HQ values exceeding 1, confirming an extremely high non-carcinogenic risk, particularly for children.
The carcinogenic health risk assessment results are shown in Fig. 7(b), (d), (f). Among all PTEs, only As and Ni had CR values exceeding 1E-04, while the cancer risk from other PTEs was negligible. In adults, the mean CR value for As and 95% of CR values were greater than 1E-04, indicating a significant carcinogenic risk. Although the mean CR value for Ni was below 1E-04, 25% of its CR values exceeded 1E-04, suggesting a localized carcinogenic risk. In children, the mean CR values for both As and Ni exceeded 1E-04, and nearly all CR values were above this threshold, indicating a notable carcinogenic health risk, with children facing a higher risk than adults. The TCR results showed that the mean TCR values for adults and children were 3.27E-04 and 1.23E-03, respectively, both exceeding the 1E-04 threshold. This suggests that overall, PTEs in the antimony tailings pose a certain level of carcinogenic health risk for both adults and children, with As and Ni identified as the primary carcinogenic factors. Notably, Arsenic emerged as dual-threat contaminant, demonstrating both carcinogenic and non-carcinogenic risks through all exposure pathways.
4. Conclusions
A simple analysis of Sb, Cr, Pb, Cd, As, Ni, Cu, and Zn concentrations in antimony tailings revealed that the average concentrations of all elements exceeded the background values in the study area. Furthermore, Igeo, Cf, Er, and RI were used to evaluate the risk of contamination of these PTEs. Although different in focus, the three evaluation methods concentrate on the two most important factors: the amount of accumulation and the contamination already caused. Therefore, the three evaluation results were unified and simplified into a comprehensive evaluation that reflects the degree of PTEs pollution. In summary, this study assumes that PTEs concentration and the degree of PTEs pollution contribute equally to ecological risk. The three evaluation results were scored by level, and each score was weighted to obtain a comprehensive evaluation result: PERS(Sb)>PERS(As)>PERS (Zn)=PERS(Cd)>PERS(Ni)>PERS(Pb)>PERS(Cr)>PERS(Cu).
To validate the reasonableness of the comprehensive risk assessment results, a static leaching experiment was conducted on high-risk potentially toxic elements (PTEs). The experimental results demonstrated that the cumulative release of PTEs in water followed the sequence: Zn > Cu > Sb > Ni > As > Pb > Cr > Cd. Among these elements, As, Cd, Pb, and Cu displayed higher mobility under acidic conditions characterized by high ORP and high salinity. In contrast, Ni and Sb were more prone to dissolution under acidic, high-salinity conditions with relatively lower ORP. Zn, however, was more readily released in environments with higher pH levels, lower ORP, and lower EC. Notably, particle size variation exhibited a more pronounced effect on the release of Sb. To further identify the key factors influencing pollution, this study employed PCA to evaluate the water quality of leachate samples obtained from the static leaching experiment. The PCA results indicated that nickel and zinc require prioritized monitoring due to their potential environmental impact. Furthermore, when the particle size was ≥4.75 mm, the negative environmental impact was found to be more significant. Therefore, to effectively reduce PTEs leaching, it is crucial to minimize the exposure of tailings to water and air while also limiting the generation of particles ≥4.75 mm in size within tailings storage areas.
Finally, the Monte Carlo simulation results revealed that PTEs in tailings pose both non-carcinogenic and carcinogenic health risks to both adults and children, with children being particularly vulnerable and facing a significantly higher risk compared to adults. Specifically, As may exhibit direct toxicity to the skin, while Sb could cause adverse effects on the respiratory system. Moreover, As and Ni are identified as potential carcinogens. Notably, in this study, As was found to pose the highest non-carcinogenic and carcinogenic risk to children, underscoring the need for heightened concern regarding its exposure. This study provides a comprehensive assessment of antimony tailings in a karst region. Moving forward, future research efforts should focus on expanding the applicability of the methodology to PTE-contaminated non-metallic tailings, soils, or sediments.
In conclusion, this study provides a novel contribution to current understanding of PTEs contamination risks, offering a robust theoretical foundation for designing effective source control strategies and remediation interventions to safeguard environmental quality and mitigate associated risks. The findings underscore the importance of addressing PTEs contamination in health risk evaluations, making it a critical consideration for future research and practical applications. it has been clarified that the tailings still contains certain amounts of heavy metal resources (such as antimony), and our research can provide scientific basis for the resource utilization of tailings.
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