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Article

Assessment of Ecological Recovery Potential of Various Plants in Soil Contaminated by Multiple Metal(loid)s at Various Sites near XiKuangShan Mine

by
Yanming Zhu
1,†,
Jigang Yang
2,†,
Jiajia Zhang
3,
Yiran Tong
3,
Hailan Su
1,*,
Christopher Rensing
3,
Renwei Feng
3 and
Shunan Zheng
4,*
1
Institute of Crop Sciences, Fujian Academy of Agricultural Sciences (Fujian Germplasm Resources Center), Fuzhou 350003, China
2
Institute of Agricultural Resources and Environment, Sichuan Academy of Agricultural Sciences, Chengdu 610055, China
3
Institute of Environmental Microbiology, College of Resources and Environment, Fujian Agriculture & Forestry University, Fuzhou 350002, China
4
Rural Energy and Environment Agency, Ministry of Agriculture and Rural Affairs, Beijing 100125, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to the work.
Land 2025, 14(2), 223; https://doi.org/10.3390/land14020223
Submission received: 3 October 2024 / Revised: 15 January 2025 / Accepted: 16 January 2025 / Published: 22 January 2025
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Abstract

:
Soil metal(loid) pollution is a threat to ecological and environmental safety. The vegetation recovery in mining areas is of great significance for protecting soil resources. In this study, (1) we first gathered four types of soils to analyse their contamination degree, including tailings mud (TM), wasteland soil (TS) very near TM, as well as non-rhizosphere soils of pepper (PF) and maize (MF) in a farmland downstream from the TM (about 5 km). Geo-accumulation and potential ecological risk indices indicated that the soil samples were mainly polluted by antimony (Sb), arsenic (As), cadmium (Cd), chromium (Cr), lead (Pb), and copper (Cu) to different degrees. Leachates of TM resulted in increased Sb, As, and Cd accumulation in TS. (2) Then, we sampled six local plants growing in the TS to assess the possibilities of using these plants as recovery vegetation in TS, of which Persicaria maackiana (Regel) Nakai ex T. Mori absorbed relatively high Sb concentrations in the leaves and roots. (3) After that, we collected rhizosphere soil and tissue samples from eight crops on the above farmland to assess their capacities as recovering vegetation of contaminated farmland soil, of which the fruits of maize accumulated the lowest concentrations of most monitored metal(loid)s (except for Pb). Further, we compared the differences in the bacterial community structure of MF, PF, TM, and TS to assess capacities of cultivating pepper and maize to improve soil microbial community structure. The MF displayed the best characteristics regarding the following attributes: (1) the highest concentrations of OMs and total P; (2) the highest OTU numbers and diversity of bacteria; and (3) the lowest abundance of bacteria with potentially pathogenic and stress-tolerant phenotypes.

1. Introduction

Antimony (Sb) is a toxic element for all living organisms, and its pollution of the environment due to mining is a serious worldwide problem, particularly in some provinces of China [1]. The XiKuangShan mine (XKS) in the Hunan Province of China harbours the largest Sb reserves in the world. Mining activity causes severe Sb pollution in soils, water systems, and plants around XKS [2,3,4]. Moreover, ores in XKS contain other accompanying metal(loid)s, such as arsenic (As) and cadmium (Cd), which lead to co-contamination with multiple metal(loid)s in the surrounding environment [5,6,7]. Microorganisms influence the migration and transformation of heavy metals in soil. Therefore, studies on the diversity and community structure of microbes in polluted soils near mines can contribute to a better understanding of soil ecology.
In soils, bacteria play important roles in the migration and speciation transformation of metal(loid)s, mainly via redox reactions, methylation, precipitation, and adsorption/absorption [8,9]. Currently, most investigations on the bacterial community structures of soils around XKS are aimed at wasteland soils [10,11,12,13,14]. The target bacteria were dominated by phyla of Proteobacteria, Acidobacteria, Chloroflexi, Bacteroidetes, Actinobacteria, Planctomycetes, Gemmatimonadetes, and Cyanobacteria [10,12,13,14]. However, changes in the microbial community structure of plant rhizosphere soils near XKS have not been investigated. Few studies have investigated the microbial community structure in the rhizosphere soils of herbs and trees [15,16,17]. There is still insufficient research investigating the alteration of soil bacterial communities in soils around XKS after growing different plants.
The selective cultivation of plants has been shown to improve soil quality and bacterial diversity in polluted soils [18], which are able to loosen soil structure and supply increasing amounts of organic materials (OMs) [9]. Some OMs stabilise metal(loid)s, thereby lowering their toxicity to plants and soil organisms while simultaneously supplying nutrients for soil microbial growth [19]. However, different plants may display different efficiencies in improving the soil physicochemical properties (such as pH, soil texture, and OMs) and microbial communities. Therefore, the identification of suitable plants is a prerequisite for the ecological recovery of polluted soils.
Therefore, in this study, we aimed to (1) screen plants to explore their potential to restore the vegetation cover of wasteland soil (TS) in the vicinity of a tailings reservoir, and (2) screen crops to restore the ecology of multiple-metal(loid)-contaminated farmland soils downstream of TS (about 5 km). To achieve these goals, (1) we sampled tailings residues (TM) in a tailings reservoir and some TS to measure metal(loid) concentrations and basic physicochemical properties. Simultaneously, we collected six plant species inhabiting the TS to determine the concentrations of metal(loid)s in different plant tissues. (2) We collected tissue samples from eight crops and their rhizosphere soils in a farmland (approximately half of 667 m2, 5 km away from the tailings reservoir) to determine elemental concentrations. We found that the growth of maize was relatively good, whereas that of pepper was not. Therefore, we chose soil samples very close to the roots of maize (MF) and pepper (PF) to determine the respective bacterial communities. (3) We assessed the ecological risk of different polluted soils (TM, TS, MF, and PF) using the geo-accumulation index (Igeo) and potential ecological risk index (RI). (4) We analysed the differences in soil bacterial community diversity and structure of the four types of soil, including TM, TS, MF, and PF.

2. Materials and Methods

2.1. Sampling of Soils and Plants

2.1.1. Descriptions for Sampling Sites of TM and TS

Samples were collected from the well-studied XKS mine area located in LengShuiJiang City, Hunan Province, China. XKS is located in a subtropical humid climate zone (mountainous terrain feature) and has an average annual temperature of 16.7 °C, an average annual evaporation of 1237.9 mm, and an average annual rainfall of 1446.2 mm (mainly from March to August). XKS has two mining areas: a northern mining area and a southern mining area (the main ore in the latter is stibnite (Sb2S3)) [20]. We collected TM and TS samples from the southern mining area (111°47′ E, 27°74′ N), where the tailings residues were open-stacked, resulting in the release of pollutants into the environments including soils, water systems, and plants (Figure 1, sampling site 2).

2.1.2. Sample Collections of TM, TS, and Plant Species Inhabited at TS

Collections of TM and TS. We selected four sampling sites in TM and four sampling sites in a wasteland close to the tailings reservoir (~50 m apart) (TS). The sampling sites were located 20 m apart. Three samples (0–20 cm) were randomly collected from each sampling site within 20 m2 (four sampling sites × three times at each sampling site). After removing fallen leaves, plant roots, and crushed stones, 3 samples from each site were evenly mixed to obtain 1 sample. Therefore, there were 4 samples both for the TM and TS. Each of the eight samples was divided into two parts using the quartering method. One part was put in a 50 mL sterile centrifuge tube, stored in incubators containing a lot of dry ice, transported to our laboratory, and then stored in a –80 °C freezer for microbial community analysis (n = 4). The other portion was naturally air-dried, mixed evenly through a 0.149 mm sieve, and used to determine soil physicochemical properties and elemental concentrations (n = 4).
Collection of six plant species inhabiting TS. We found only six plant species growing in the wasteland: Persicaria maackiana (Regel) Nakai ex T. Mori (PM), Erigeron canadensis L. (ECL), Broussonetia papyrifera (L.) L’Hér. ex Vent. (BP), Artemisia caruifolia Buch.-Ham. ex Roxb. (AC), Imperata cylindrica (L.) P. Beauv. (IC), and Crepis tectorum L. (CT). Whole plants were removed from the soil, cleaned with tap water and then with deionised water, separated into different tissues (roots, stems, and leaves), oven-dried, and digested to determine the concentrations of metal(loid)s in the different tissues. As these plants did not reproduce in large numbers and we were only able to gather one to five seedlings, there were no replications for the testing indices from these six plants (n = 1).

2.1.3. Sample Collections of Eight Crops and Associated Rhizosphere Soils

Descriptions of sampling sites. The samples were gathered from a farmland soil, ~5 km away from the tailings reservoir (111°50′ E, 27°80′ N) (Figure 1, sampling site 1). The river flows adjacent to farmland. The percolated water from the tailings reservoir converges into this river, resulting in the co-contamination of multiple metal(loid)s.
Descriptions for eight crop species. Farmers used water from the river to irrigate farmland, but this is now forbidden. The official government prohibited the reuse of farmlands in the vicinity of XKS, but we still found some crops (eight types of crops) cultured in several small-area farmlands (approximately half of 667 m2). This led us to assess the possibility of using these crops to restore the soil ecology of damaged farmlands in XKS. These crops include Capsicum annuum L. (CA), Cucurbita moschata (Duchesne ex Lam.) Duchesne ex Poir. (CMD), Luffa aegyptiaca Miller (LAM), Cucumis sativus L. (CS), Allium fistulosum L. (AF), Zea mays L. (ZM), Solanum melongena L. (SM), and Vigna Unguiculata Subsp. Sesquipedalis (L.) Verdc. (VU).
Gathering of eight crops and their associated rhizosphere soils. When we gathered samples of rhizosphere soils and crops, three plants from each type of crop were sampled. Whole plants with rhizosphere soils were removed from the soils, and the soils adhering to the root surface (~2 mm) were shaken off and considered rhizosphere soils. The partial rhizosphere soils of each plant were naturally air-dried and sieved through a 0.149 mm sieve to determine the soil physicochemical properties (n = 3).
Soil samples were collected from soils very close to the roots of maize (MF) and pepper (PF) plants for soil microbial community structure analysis. We observed that pepper plants did not grow well, but the growth status of maize was relatively good. Therefore, we wanted to determine the relationship between soil bacterial communities and plant growth status. Therefore, we gathered soils (non-rhizosphere soils) as close as possible to the roots of pepper and maize plants to analyse the soil bacterial community structure. During the sampling process, one soil sample was collected from each plant, and there were four soil samples each for pepper and maize. The above soils were placed in sterile centrifuge tubes, stored in incubators containing dry ice, transported to our laboratory, and then stored in a –80 °C refrigerator. Fresh rhizosphere soil samples were sent for analysis of the microbial community structure.

2.2. Analysis of Soil Physicochemical Properties

The physicochemical properties of the soil samples, including soil pH, electrical conductivity (EC), OMs, total nitrogen concentration (TN), total phosphorus concentration (TP), available phosphorus concentration (AP), and available potassium concentration (AK), were analysed. Soil pH (soil-to-water = 1:5) and EC were determined using acidity (Fisher Scientific, Waltham, MA, USA) and conductivity meters (Mettler Toledo, Zurich, Switzerland), respectively [21]. The soil OMs were determined using the potassium dichromate heating method [22]. The soil TN was measured using the Kjeldahl method [23]. The NaHCO3 extraction method was used to extract the available P [24], and the concentrations of P (available and total P) were determined using the molybdenum antimony colourimetric method [25]. Soil available K was extracted using ammonium acetate, and its concentration was determined using flame photometry [26].

2.3. Element Concentration Determination of Soil and Plant Samples

2.3.1. Digestion of Soil and Plant Samples

An ED54 DigiBlock digestion system (Lab Tech, Inc., Hopkinton, MA, USA) was used to digest plant and soil samples. The digestion method has been described in a previous study [21]. For the digestion of soil samples, 10 mL HNO3, 4 mL HF, and 0.25 g soil samples were mixed in a digestion tube and incubated overnight. Samples were heated first at 120 °C for 1 h, and then at 150 °C for 2 h. Next, the liquid in the tube was evaporated at 180 °C until the liquid volume was ~1 mL. After that, the liquid volume was topped up to 25 mL using deionised water, filtered through a 0.22 μm filter, and then used to determine the elemental concentrations. The digestion method for plant samples was as follows: 15 mL of HNO3 and 0.2 g of plant samples were mixed in a digestion tube and incubated overnight. The samples were first heated to 80 °C for 1.5 h, at 120 °C for 1.5 h, and then at 150 °C for 3 h. Finally, the liquid in the tube was evaporated to be approximately 1 mL under a temperature of 180 °C. After that, the volume of the above liquid was topped up to 50 mL in a volumetric flask and filtered through a 0.22 μm filter for the determination of elemental concentrations.

2.3.2. Determination of Elemental Concentrations in Soil and Plant Samples

Elemental concentrations in soil and plant samples were determined using Inductively Coupled Plasma Mass Spectrometry (ICP–MS) (PE, Norwalk, CT, USA). Blank and standard materials (rice grains, GBW100350, purchased from the Institute of Analysis and Testing, General Iron and Steel Research Institute, Beijing, China) were used to ensure determination accuracy. The relative standard deviation (RSD) of each sample was less than 10%, and the recovery rate was 85–120%.

2.4. Quantitative Risk Analysis and Calculation

2.4.1. Geo-Accumulation Index (Igeo)

The geo-accumulation index (Igeo) was calculated to evaluate the potential environmental risks of toxic metal(loid)s in soils [27] according to the following equation:
I g e o = l o g 2 [ C i 1.5 ∗ B i ]
Ci is the mean concentration of the “i” element, and Bi is the geochemical background value of the “i” element in the soil of Hunan Province, China [28].

2.4.2. Potential Ecological Risk Index (RI)

The potential ecological risks of metal(loid)s were evaluated using the ecological risk index (RI) established by Hakanson [29]. RI was calculated according to the following equations:
E i = T i ∗ C i B i
R I = ∑ i = 1 n E i
E i is the potential ecological risk index of the “i” element; T i is the toxic-response index for the “i” element, and the associated values for Sb, As, Cd, Cr, Pb, and Cu are 13, 10, 30, 2, 5, and 5, respectively [30]. Ci is the mean concentration of the “i” element, and Bi is the geochemical background value of the “i” element in the soil of Hunan Province, China.

2.5. DNA Extraction and 16S rRNA Genes Amplification

Soil samples of TM, TS, MF, and PF were sent to Beijing Biomarker Technologies Co., Ltd. for analysis of the soil microbial community according to the method described by Zhang et al. [31]. Briefly, DNA was extracted using a TGuide S96 Magnetic Soil/Stool DNA Kit (Tiangen Biotech Co., Ltd., Beijing, China) according to the manufacturer’s instructions. The DNA concentration of the samples was measured using a Qubit dsDNA HS Assay Kit and a Qubit 4.0 Fluorometer (Invitrogen, Thermo Fisher Scientific, OR, Waltham, MA, USA). KOD One PCR Master Mix (Toyobo Life Science) was used to perform 25 cycles of PCR amplification. PCR amplicons were purified using Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA) and quantified using the Qubit dsDNA HS Assay Kit and Qubit 4.0 Fluorometer. Purified SMRTbell libraries from the pooled and barcoded samples were sequenced on a single PacBio Sequel II 8M cell using the Sequel II Sequencing Kit 2.0. The bioinformatics analysis of this study was performed with the aid of the BMK Cloud (Biomarker Technologies Co., Ltd., Beijing, China).

2.6. Data Processing and Statistical Analysis

One-way variance (ANOVA) and Tukey’s test at p < 0.05 were used to compare the significant differences between MF, PF, TM, and TS for the indices shown in Figure 2, Figure 3 and Figure 4, Figures S1 and S2. SPSS 22 (SPSS Inc., Chicago, IL, USA) software was used for data analysis. Origin 2018 and Microsoft Office Visio 2007 were used to draw the figures, except for those correlated with soil microbes, which were drawn using the data processing platform BMKCloud (www.biocloud.net (accessed on 15 January 2025)). For the analysis correlated to microbes, the following software (or platform) was applied, including python2 (scipy v0.17.1) for ANOVA plus Tukey−Kramer post hoc test (Figure 5 and Figure S5), Spearman’s correlation analysis (Figure 6), Bugbase (0.1.0) for phenotype prediction (Figure 7 and Figure 8), QIIME 1.9.1 (nmds.py) for Non-Metric Multi-Dimensional Scaling (NMDS) analysis (Figure S3c), QIIME 1.8.0 (principal_coordinates.py) for principal coordinate analysis (PCoA) (Figure S3d), and Software R v3.1.1 (picante, v1.8.2) for Alpha diversity analysis (Figure S4b,c). Data were processed as described by Zhang et al. [31].

3. Results and Discussion

3.1. Serious Multiple-Metal(loid) Pollution in the Environment Around XKS Mine

3.1.1. Concentrations of Metal(loid)s in TM, TS, MF, and PF

The Sb concentrations in TM, TS, MF, and PF were all higher than 655 mg kg−1 (on average, Figure 2a), being higher than its maximum permissible concentration in soils (36 mg kg−1, WHO) [32]. According to the risk-controlling concentration of metal(loid)s for agricultural lands (Table S1, pH > 7.5, GB15618−2018), the samples of TM, TS, MF, and PF were heavily contaminated by As (on average, ≥138.0 mg kg−1) and Cd (on average, ≥8.87 mg kg−1) (Figure 2b,c). The Cr concentrations in TM, TS, PF, and MF were lower than 250 mg kg−1 (Figure 2d). There were no significant differences in Pb and Cu concentrations among the TM, TS, PF, and MF treatments (Figure 2e,f). Furthermore, the Cu concentrations in TM, TS, PF, and MF were lower than 100 mg kg−1 (Figure 2f). The concentrations of Pb in one sample from a tailings mud sampling site (284.9 mg kg−1) and in one sample from a wasteland sampling site (184.1 mg kg−1) were higher than its risk-controlling concentration (≥ 170 mg kg−1) (Table S1). Therefore, based on the analysis of the metal(loid) concentrations, TM, TS, MF, and PF were mainly contaminated by Sb, As, and Cd.

3.1.2. Pollution Level of TM, TS, MF, and PF

According to the assessment standards for the degree of soil pollution based on the geo-accumulation index (Igeo) (Figure S1a–f) and potential ecological risk indices (Eir and RI), MF, PF, TM, and TS were polluted by Sb, As, and Cd (Table S2 and Figure 3a–c). These soils were also polluted with Cr (MF), Pb, and Cu (their Igeo values were greater than zero) (Table S2 and Figure S1d–f). However, the pollution levels of Cr, Pb, and Cu in TS, TM, MF, and PF were low because their E r i values were all < 40 (Table S2 and Figure 2d–f).
RI values of TM, TS, MF, and PF were as high as 5114–39,811 (Figure 3g and Table S2), indicating serious pollution levels for these four types of soils. The RI values of the soil around XKS are 165–36,501 [6,33]. The RI values of soils around the NanDan and XunYang Sb mining areas in HeChi City, Guangxi Autonomous Region, have been reported as 3240 and 175, respectively [20]. These results indicate that the overall pollution levels of the soil around the XKS mining area were higher than those around the NanDan and XunYang mining areas.
The smelting of Sb ores produces large amounts of alkali residues and discharges toxic metal(loid)s [34]. The emission of alkali residues may have resulted in the alkalisation of TM and TS (Figure 4a). The significantly higher EC value in TM (Figure 4b) than in TS, MF, and PF indicated (1) a high concentration of dissolved salts in TM, and (2) a higher pH (Figure 4a) and higher concentrations of Sb, As, and Cd in TS than in TM (Figure 2a–c), which resulted from the leachates from TM to TS.

3.2. Vegetation Recovery of TS Using Six Plants

Soils with EC < 150 μs cm−1 are suitable for the ecesis of plants and microbes [35]. Therefore, the relatively low EC values in TS, MF, and PF relative to those in TM (Figure 4b) are beneficial for plant ecesis. In TS, we sampled six plant species and determined the concentrations of metal(loid)s in different tissues, including PM, ECL, BP, AC, IC, and CT (Table S3). The roots of PM absorbed the highest concentrations of Cr (2.48 mg kg−1), Cu (12.10 mg kg−1), As (46.10 mg kg−1), and Pb (3.64 mg kg−1), as well as relatively high concentrations of Sb (548.6 mg kg−1) and Cd (0.57 mg kg−1) among six plant species. The stem of PM also absorbed the highest concentrations of Cu (9.03 mg kg−1) and Cd (1.85 mg kg−1), and its leaves absorbed the highest concentrations of Sb (275 mg kg−1) and Pb (4.96 mg kg−1) among six plant species (Table S3). PM is an annual plant widely distributed in Northeast Asia [36]. Therefore, among these six plant species, PM may be a tolerant plant suitable for vegetation recovery in TS. The stems of IC absorbed the lowest concentrations of Cu (1.60 mg kg−1), As (1.03 mg kg−1), Sb (2.32 mg kg−1), and Pb (0.12 mg kg−1); and the leaves of IC absorbed the lowest concentrations of Cu (2.18 mg kg−1), Cd (0.20 mg kg−1), Sb (14.43 mg kg−1), and Pb (1.66 mg kg−1), as well as a relatively low As concentration (11.79 mg kg−1) (Table S3).

3.3. Ecological Recovery Using Different Crops in the Farmland

3.3.1. Capacities of Eight Crops to Accumulate Metal(loid)s in Different Tissues

The roots of ZM accumulated the lowest concentrations of As (1.33 mg kg−1) and Cd (0.42 mg kg−1), and that of LAM accumulated the lowest Sb concentration (1.35 mg kg−1) (Table S3). The stems of SM accumulated the lowest concentrations of As (0.44 mg kg−1) and Sb (0.87 mg kg−1); that of CMD accumulated the lowest Cd concentrations (0.32 mg kg−1); and that of ZM accumulated moderate amounts of As (0.62 mg kg−1), Cd (0.42 mg kg−1), and Sb (1.46 mg kg−1). However, the CA stems accumulated the highest concentrations of Cd (1.30 mg kg−1). The leaves of CA accumulated the highest Sb concentration (18.17 mg kg−1). The leaves of AF accumulated the lowest concentrations of Cr (0.21 mg kg−1), As (0.42 mg kg−1) and Sb (1.23 mg kg−1). The leaves of ZM accumulated moderate amounts of As (3.23 mg kg−1), Cd (0.17 mg kg−1), and Sb (5.64 mg kg−1). The fruits of CA accumulated the highest concentrations of Cr (0.31 mg kg−1), Sb (2.38 mg kg−1), and Pb (0.13 mg kg−1), and relatively high concentrations of As (0.56 mg kg−1) and Cd (0.56 mg kg−1). The fruits of ZM accumulated the lowest concentrations of Cr (0.08 mg kg−1), Cu (3.35 mg kg−1), As (0.07 mg kg−1), Cd (0.25 mg kg−1), and Sb (0.22 mg kg−1), as well as a moderate concentration of Pb (0.04 mg kg−1).
The above results suggest that (1) maize has a limited ability to absorb metal(loid)s, especially in its fruits, and (2) planting pepper poses high risks because of the relatively high accumulation capacities for Cr, Sb, and Pb in its fruits. Previous studies have shown that maize has a weak ability to accumulate toxic metal(loid)s, such as Cd [37], As, and Sb [38].

3.3.2. Improvement of Soil Physicochemical Properties After Cultivation of Pepper or Maize

The pH value of non-rhizosphere soil of CA (indicated PF) and ZM (indicated MF) was 7.97 and 8.44 (Figure 4a), but their rhizosphere soil pH was 7.00 and 8.13, respectively (Figure S2a), suggesting that cultivation of CA and ZM may reduce soil pH in alkaline soils. In the non-rhizosphere soils of CA and ZM, the values of basic soil properties were as follows: 90.97 and 102.7 μs cm−1 (EC); 30.69 and 33.44 g kg−1 (OMs); 0.02 and 0.39 g kg−1 (total N); 0.26 and 0.36 g kg−1 (total P); 0.01 and 0.01 g kg−1 (AP); and 77.89 and 61.23 mg kg−1 (AK) (Figure 4b–g), respectively. In the rhizosphere soils of CA and ZM, the associated values of basic soil properties were as follows: 207.0 and 105.2 μs cm−1 (EC); 44.95 and 33.50 g kg−1 (OMs); 0.45 and 0.25 g kg−1 (total N); 0.50 and 0.34 g kg−1 (total P); 0.021 and 0.01 g kg−1 (AP); and 83.3 and 94.87 mg kg−1 (AK) (Figure S2b–g), respectively. We can conclude that (1) cultivation of CA and ZM increased rhizosphere soil EC values relative to their non-rhizosphere soils, especially for pepper; (2) cultivation of CA and ZM stimulated OM accumulation, especially for pepper; (3) cultivation of CA may lead to N accumulation in rhizosphere soil, but N uptake by ZM may lead to a lower N concentration in rhizosphere soil relative to the non-rhizosphere soil; (4) cultivation of CA may increase total P, AP, and AK concentrations in rhizosphere soil.

3.3.3. Changes in Soil Bacterial Community Structure of MF/PF Relative to TM/TS

OTU Numbers and Microbial Community Diversities

In this study, only 1238 OTUs were obtained from 16 soil samples (MF, PF, TM, and TS) (Figure S3a). Ref. [15] obtained 3285 OTUs in the rhizosphere soils of wild plants around XKS, which was speculated to be due to the degree of metal(loid) pollution between regions [39]. In the study of Ref. [15], the soil Sb and As concentrations ranged from 1.54 to 117.5 mg kg–1 and 53.78 to 130.4 mg kg–1, respectively, which were much lower than those in the present study (Figure 2a,b). However, the number of OTUs in MF (794) was higher than that in TS (777) and TM (652) (Figure S4a). The diversity indices (dilution curve, rank abundance curve, ACE, and Chao1) indicated better diversity of soil microbial communities in MF and PF than in TM and TS, especially in MF (Figures S3a,b and S4a–c), respectively.

Bacterial Phyla and Genera Associated with OM Degradation

The higher OM concentrations in the MF and PF than in the TM and TS (Figure 4c) may be an important reason for the improvement in bacterial OTU numbers and community diversity. OMs are one of the main sources of nutrients for the growth of soil bacteria [40], and can also alter the bacterial community structure by changing the bioavailability of metal(loid)s in soils [41].
The relative abundance of phyla correlated with the degradation of OMs was highest in MF among MF, PF, TM, and TS, and these phyla included Acidobacteria (Figure S5a), Chloroflexi (Figure S5c), and Verrucomicrobiota (Figure S5h). Many bacteria in the phylum Acidobacteria are involved in the degradation of lignocellulosic biomass [42], which carries many genes encoding cellulose and hemicelluloses [43]. Bacteria in the phylum Chloroflexi are found in activated sludge wastewater treatment plants and play an important role in degrading complex polymeric organic compounds to support bacterial growth [44].
The relative abundances of genera correlated with OM degradation were also the highest in MF, PF, TM, and TS, and these genera (Figure 5 and Table S5) included Vicinamibacter (belonging to Acidobacteriota), Bryobacter (Acidobacteriota), Chthoniobacter (Verrucomicrobia), Tepidisphaera (Planctomycetota), UTCFX1 (Chloroflexi), and Sphingomonas (Proteobacteria). Bacteria of the Bryobater genus can decompose lignin and cellulose [45]. Bacteria in the Tepidisphaera genus can degrade monosaccharides, disaccharides, and polysaccharides to produce extracellular polysaccharides [46]. Bacteria in the Chthoniobacter genus can degrade complex organic compounds [47], play an important role in plant root decay and decomposition, and benefit the colonisation and enrichment of rhizosphere-promoting bacteria [48]. Bacteria belonging to the UTCFX1 genus can degrade organic pollutants [49]. Bacteria of the Sphingomonas genus can degrade aromatic compounds [50].
In this study, the concentration of OMs in the MF was slightly higher than that in the PF (Figure 4c), which may have resulted in a higher relative abundance of phyla and genera associated with OM degradation and a higher OTU number in the MF than in the PF (Figure S4a). Furthermore, the concentration of OMs in the rhizosphere soil of ZM plants was significantly lower than that of CA plants (Figure S2c). The degradation of OMs by these bacteria in the MF persisted in the soil, showing generally equal concentrations of OMs in the MF and PF (Figure 4c). However, a greater loss of OM degradation was foreseeable in the rhizosphere soil of ZM than in that of CA (Figure S2c), which resulted from increased nutrient uptake by the larger aerial biomass of ZM relative to CA. In accordance with the above results, the relative abundances of Bryobacter, Chthoniobacter, Nitrospira, Terrimonas, Tepidisphaera, UTCFX1, and Vicinamibacter were all significantly and positively correlated with soil OMs (Figure 6). However, Sphingomonas was not significantly correlated with the soil OMs concentration. These results suggest that bacteria belonging to the genera Bryobacter, Chthoniobacter, Nitrospira, Terrimonas, Tepidisphaera, UTCFX1, and Vicinamibacter play important roles in soil OMs accumulation.

Bacterial Phyla and Genera Associated with the N Cycle

The significantly lower total N concentration in the PF treatment (Figure 4d) may also be because of bacteria in the soil N cycle and subsequent leaching. The relative abundances of the phylum Nitrospirae (Figure S5f) and Nitrospira genera (Figure 5h and Table S5) were the highest in PF, followed by MF, TS, and TM. The Nitrospira genus plays a potential role in biogeochemical cycle processes, such as soil methane, sulphur, and nitrogen cycles, and can oxidise nitrite to form nitrate [51]. Therefore, the highest relative abundance of Nitrospirae (Nitrospira), but the lowest total N concentration in the PF, may indicate increased formation of nitrate and subsequent loss of N due to its migration. Furthermore, the results of the Spearman correlation analysis showed that the relative abundance of Nitrospira did not significantly correlate with the soil total N concentration (Figure 6), which was only significantly correlated with the relative abundance of Annwoodia, Flavihumibacter, Gallionella, Sulfurovum, and Thiobacillus, but negatively correlated with the relative abundance of Arenimonas, Dongia, Pseudolabrys, and Reyranella. These results suggest that these genera may affect the soil N cycle.

Bacterial Phyla and Genera Associated with the P Cycle

We screened several genera belonging to different genera (Figure 5 and Table S5), including Gemmatimonas (belonging to Gemmatimonadetes), Pseudolabrys (Proteobacteria), Niatella (Bacteroidetes), and Vicinamibacter (Acidobacteriota). The relative abundance of Gemmatimonas was highest in TS, followed by MF, PF, and TM. The relative abundance of Pseudolabrys was the highest in PF, followed by MF, TS, and TM (Table S5). However, the relative abundance of Vicinamibacter was highest in MF, followed by PF, TS, and TM. Gemmatimonas, the main genus of Gemmatimonadetes, can dissolve low levels of bioavailable P to support plant growth [52]. Pseudolabrys has been shown to accumulate in P-deficient chilli pepper planting soils [53]. Niatella secretes acid phosphatase, which plays an important role in the conversion of organic P to inorganic P [54,55]. The Vicinamidacteria class can solubilise P.
In this study, the relative abundances of Sphingomonas, Tepidisphaera, and Vicinamibacter were significantly and positively correlated with soil total P (Figure 6), indicating their roles in P enrichment. The relative abundances of Tepidisphaera, Terrimonas, and Vicinamibacter genera were significantly and positively correlated with soil AP, indicating their roles in P availability in soils (Figure 6). However, the genera Gemmatimonas, Niatella, and Pseudolabrys were not significantly correlated with total P and AP, suggesting that the bacteria belonging to these genera played secondary roles in total P and AP in this study.

Bacterial Phyla and Genera Associated with the S Cycle

The most abundant genus of Proteobacteria in TM is Thiobacillus, and bacteria in this genus can directly or indirectly oxidise insoluble metal sulphides in sludge to form metal sulphides and leach them [56]. Other sulphur-oxidising bacteria in Proteobacteria, such as Sulfuritalea [57], Sulfurisoma, and Sulfurovum [58], were detected in TM (Table S5). Stibnite (Sb2S3) is the main ore in XKS and provides abundant sulphur to sulphur-oxidising bacteria [59]. Therefore, the presence of the aforementioned sulphur-oxidising bacteria may promote the leaching of metal(loid)s from Sb tailings [12]. MND1 can utilise the Sox multienzyme system to oxidise sulphides [13]. In this study, the relative abundance of MND1 was the highest in TS, followed by MF, PF, and TM (Figure 5b and Table S5). In addition, some Sb-oxidising bacteria contain genes encoding proteins that oxidise sulphide, which can promote the release of Sb from minerals to the environment [60], such as Bosea sp. AS-1.

Bacterial Phyla and Genera Associated with Plant-Promoting Bacteria

The relative abundances of Terrimonas and RB41 were significantly higher in MF and PF than in TM and TS (Figure 5d,g). The relative abundances of Candidatus Udaeobate and Gemmata in MF and PF were also significantly higher than those in TM (Table S5). These results suggest that bacteria belonging to these four genera may be conducive to plant growth. (1) Some bacteria belonging to the Terrimonas genera can not only adapt to the various environments [61], but also promote plant growth [62]. They can remediate the soils contaminated by heavy metals [63]; (2) some bacteria belonging to the RB41 genus often have a high tolerance to pollutants [64,65], and they can also help to maintain the biogeochemistry and metabolic basis of soils under long-term conditions with low soil nutrients [66]. (3) Some bacteria belonging to the Candidatus Udaeobater genus can inhabit poor soils [67] and have the potential to remediate polluted soils, which can survive in poor soils via obtaining amino acids and vitamins [68]. (4) Some strains in the Gemmata genus carry many resistance genes for Cd, zinc (Zn), As, Cu, and mercury (Hg) [69], or extracellular functions σ factors (ECFs) [70]. The latter are involved in the adaptation of strains to stressed environments and can benefit plant growth [70].

Phyla and Genera Correlated with the Resistances for Toxic Metal(loid)s

Resistance genes. The phylum Proteobacteria carries several metal(loid) resistance genes [71]. Therefore, the significantly higher relative abundance of Proteobacteria in the TM and TS than in the MF and PF (Figure S5g) may indicate the presence of more tolerant bacterial communities in the TM and TS.
Redox of toxic metal(loid)s. The bacteria in many genera were reported to be involved in metal(loid) redox reactions, including Gemmatimonas (oxidise arsenite to arsenate) [72], Thermomonas and Geobacter (converting antimonate to antimonite) [73,74], Gallionella (oxidising Fe(II) to Fe(III)) [75], Crocinitomix (oxidising nitrite to nitrate) [76], Rhodobacter (oxidising As(III) to As(V)) [77], and Flavihumibacter (oxidising Sb(III) to Sb (V)) [78]. In this study, the relative abundances of Gemmatimonas (Figure 5a and Table S5), Thermomonas (not in MF), Geobacter (TM), Gallionella (TM and TS), Crocinitomix (TM), Rhodobacter, and Flavihumibacter (Table S5) were highest in TM, followed by TS, PF, and MF. These results suggest that more Sb and As redox reactions may be driven by the correlated bacteria in TM and TS than in MF and PF.
Other resistant bacteria genus for toxic metal(loid)s. The relative abundance of Zavarzinella in the TM was also significantly higher than that in the MF, PF, and TS (Figure 5c). Some bacteria in the Planctomycota phylum, which the Zavarzinella belongs to, can resist heavy metal stress via degrading carbohydrates and thus forming extracellular polysaccharides [31]. Some bacteria belonging to the Sphingomonas genus carry metal oxidation genes and have high ALP activity, thus resisting the toxicity of Cd and Pb [79].

BUGbase Phenotypic Prediction

The ratios of the relative abundances of bacteria belonging to different phenotypes are shown in Figure 7a. There were no significant differences in the relative abundance of aerobic bacteria among the MF, PF, TM, and TS treatments (Figure 7b). TM had the lowest relative abundance of bacteria with the Anaerobic_phenotype (Figure 7c), Gram-positive phenotype (Figure 7e), and Forms_biofilm phenotypes (Figure 7j). However, TM had the highest relative abundance of bacteria with Facultative_anaerobic (Figure 7d), Gram-negative (Figure 7f), Contains_mobile_elements (Figure 7g), Stress_tolerant (Figure 7h), and Potentially_Pathogenic phenotypes (Figure 7i). MF had the highest relative abundance of bacteria with the Anaerobic_phenotype (Figure 7c) and the lowest relative abundance of bacteria with the Facultatively_Anaerobic phenotype (Figure 7d), Stress_tolerant phenotype (Figure 7h), and Potentially_pathogenic phenotype (Figure 7i). In this study, we selected only the phenotypes of Contains_mobile_elements, Stress_tolerance, and Potential_pathogenicity for further discussion. These phenotypes are closely correlated with substance migration [80], the detoxification of metal(loid)s [81], and the risk of potential pathogenic bacteria [82].
The high EC value in the TM (Figure 4b) may have been partially due to the high relative abundance of bacteria with the Contains_mobile_elements phenotype (Figure 7g). Bacteria with the Contains_mobile_elements phenotype were mainly found in the phyla Bacteroidetes (except in TM), Cyanobacteria (in TM), Gemmatimonadetes (TS), Planctomycetes (except in TM), Proteobacteria, and Verrucomicrobia (MF) (Figure 8a and Table S6). There were more bacteria with high resistance to oxidative stress in TM and TS than in MF and PF (Figure 7h), which may be due to the unique phyla and/or genera. Bacteria with a stress_tolerant phenotype mainly belonged to the phyla Bacteroidetes, Planctomycetes, Proteobacteria, Verrucomicrobia (MF), Cyanobacteria (TM), and Gemmatimonadetes (MF and TS) (Figure 8b and Table S6). Bacteria with the Potentially_pathogenic phenotypes mainly belonged to the phylum Proteobacteria (Figure 8c and Table S6).
Unique genera classified into the phenotype of Contains_Mobile_Elements in TM and/or TS mainly included Cytophagaceae (TM), Gaiellaceae (TS), Haliangiaceae (TS), Helicobacteraceae (TM), Saprospiraceae (TS), Flavihumibacter (TM), Hydrogenophaga (TM), Rhodobacter (TM), Rhodoferax (TM), Rubrivivax (TM), Sulfuritalea (TM), and Thiobacillus (TM and TS) (Figure 8d and Table S6). Unique genera in TM and/or TS included Cytophagaceae (TM), Gaiellaceae (TS), Haliangiaceae (TS), Saprospiraceae (TS), Flavihumibacter (TM), Hydrogenophaga (TM), Rhodobacter (TM), Rhodoferax (TM), Rubrivivax (TM), Sulfuritalea (TM), and Thiobacillus (TM and TS) (Figure 8e and Table S6). Unique genera with potentially pathogenic phenotypes in the TM and/or TS included Hydrogenophaga (TM), Rhodoferax (TM), Rubrivivax (TM), Sulfuritalea (TM), and Thiobacillus (TM and TS) (Figure 8f and Table S6). These results indicate that TM and TS had more potential pathogenic bacteria than MF and PF, which belonged to the Proteobacteria phylum.

4. Conclusions

The results showed that (1) MF, PF, TM, and TS were contaminated with Sb, As, Cd, Cr (MF), Pb, and Cu at different pollution levels; (2) TM had the highest EC value, which resulted in the accumulation of leachates in TS and alkalisation of TS; (3) MF and PF had higher concentrations of OMs, total P, and AP than TM and TS; (4) MF and PF showed higher OTU numbers and better bacterial diversity than TM and TS; (5) some unique genera were found in the TM and TS, which may be associated with the S cycle (MND1, Sulfuritalea, Sulfurisoma, and Sulfurovum) and the oxidation of metal(loid)s (Gemmatimonas, Thermomonas, Geobacter, Gallionella, Crocinitomix, Rhodobacter, and Flavihumibacter); and (6) TM and TS had plenty of bacteria belonging to the genera with the Contains_mobile_elements phenotype, Stress_tolerant phenotype, and Potentially_pathogenic phenotype, indicating that these bacteria may stimulate the migration of metal(loid)s, have a high tolerance for oxidative stresses resulting from metal(loid) exposure, and are partially pathogenic.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14020223/s1, Figure S1: Values of Igeo for Sb (a), As (b), Cd (c), Cr (d), Pb (e), and Cu (f) in TM, TS, MF, and PF. Figure S2: Basic physicochemical properties of rhizosphere soils of eight crops, including pH(a), EC(b), OM(c), total N concentration(d), total P concentration(e), available P concentration (f), and available K concentration (g). Figure S3: Alpha diversity indices including dilution curve (a) and rank abundance curve (b). Beta diversity indices including Non-Metric Multi-Dimensional Scaling analysis (NMDS, c) and Principal coordinates analysis (PCoA, d) of soil samples from different sites. Figure S4: OTU numbers for MF, PF, TM, and TS (a). Alpha diversity index values of 16S rRNA bacterial libraries, including ACE (b) and Chao1 (c). (d) Indicates the relative abundance of predominant bacterium at genus level. Figure S5: Relative abundances of the major phyla with significant differences among MF, PF, TM, and TS. Table S1: Limits for metal(loid) concentrations in agricultural soils and edible parts of crops. Table S2: Assessment standards for soil pollution degree based on the geo-accumulation index (Igeo) and the potential ecological risk indices (Eir and RI). Table S3: Concentrations of metal(loid)s in different tissues of plants and in rhizosphere soils of eight crops. Table S4: Numbers of species at different levels in MF, PF, TM, and TS. Table S5: Results of ANOVA analysis for the relative abundances of microbial communities (at genus levels). Table S6: Relative abundances of microbial communities (at phylum and genus levels) classified into different phenotypes based on BUGbase prediction analysis.

Author Contributions

Conceptualization, C.R.; investigation, Y.T.; data curation, J.Z.; writing—original draft preparation, J.Y.; writing—review and editing, Y.Z.; supervision, H.S.; project administration, R.F.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2023YFD1700104, 2021YFD2000205), China Agriculture Research System (CARS–24–B–05).

Data Availability Statement

The data that support the findings of this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Locations of sampling sites.
Figure 1. Locations of sampling sites.
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Figure 2. (a–f) Indicates the concentrations of Sb (a), As (b), Cd (c), Cr (d), Pb (e), and Cu (f) in TM, TS, MF, and PF, respectively. ** and * above the bars indicate significant differences between different sites at the p ≤ 0.01 and p ≤ 0.05 level, respectively. TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively.
Figure 2. (a–f) Indicates the concentrations of Sb (a), As (b), Cd (c), Cr (d), Pb (e), and Cu (f) in TM, TS, MF, and PF, respectively. ** and * above the bars indicate significant differences between different sites at the p ≤ 0.01 and p ≤ 0.05 level, respectively. TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively.
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Figure 3. (a–f) Indicates the E r i values for Sb (a), As (b), Cd (c), Cr (d), Pb (e), and Cu (f) in TM, TS, MF, and PF, respectively. * above the bars indicates significant differences between different sites at the p ≤ 0.05 level. TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively. (g) Indicates the values of RI for TM, TS, MF, and PF.
Figure 3. (a–f) Indicates the E r i values for Sb (a), As (b), Cd (c), Cr (d), Pb (e), and Cu (f) in TM, TS, MF, and PF, respectively. * above the bars indicates significant differences between different sites at the p ≤ 0.05 level. TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively. (g) Indicates the values of RI for TM, TS, MF, and PF.
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Figure 4. Basic physicochemical properties of TM, TS, MF, PF, including pH (a), EC (b), OM (c), total N concentration (Total N, (d)), total P concentration (Total P, (e)), available P concentration (AP, (f)), and available K concentration (AK, (g)). TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively. Lowercase letters on the bars indicate significant differences among TS, PF, MF, and TM (p ≤ 0.05).
Figure 4. Basic physicochemical properties of TM, TS, MF, PF, including pH (a), EC (b), OM (c), total N concentration (Total N, (d)), total P concentration (Total P, (e)), available P concentration (AP, (f)), and available K concentration (AK, (g)). TM, TS, MF, and PF indicate tailings residues, wasteland soils near the tailings reservoir, non-rhizosphere soils of maize, and non-rhizosphere soils of pepper, respectively. Lowercase letters on the bars indicate significant differences among TS, PF, MF, and TM (p ≤ 0.05).
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Figure 5. Relative abundance of the major genera (top 10) in MF, PF, TM, and TS, including Gemmatimonas (a), MND1 (b), Zavarzinella (c), Terrimonas (d), Tepidisphaera (e), Vicinamibacter (f), RB41 (g), Nitrospira (h), Thiobacillus (i), and Sphingomonas (j). * and ** indicate significant differences among TS, PF, MF, and TM at p ≤ 0.05 and p ≤ 0.01, respectively.
Figure 5. Relative abundance of the major genera (top 10) in MF, PF, TM, and TS, including Gemmatimonas (a), MND1 (b), Zavarzinella (c), Terrimonas (d), Tepidisphaera (e), Vicinamibacter (f), RB41 (g), Nitrospira (h), Thiobacillus (i), and Sphingomonas (j). * and ** indicate significant differences among TS, PF, MF, and TM at p ≤ 0.05 and p ≤ 0.01, respectively.
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Figure 6. Spearman correlation analysis between environmental factors and relative abundances of dominantly microbial communities at genus levels. The brown to pink colour indicates a negative correlation, and the light green to dark green colour indicates a positive correlation. *, **, and *** indicate significant relationships between different soil properties and microbial abundance of different genera at p ≤ 0.05, p ≤ 0.01, and, p ≤ 0.001 level, respectively.
Figure 6. Spearman correlation analysis between environmental factors and relative abundances of dominantly microbial communities at genus levels. The brown to pink colour indicates a negative correlation, and the light green to dark green colour indicates a positive correlation. *, **, and *** indicate significant relationships between different soil properties and microbial abundance of different genera at p ≤ 0.05, p ≤ 0.01, and, p ≤ 0.001 level, respectively.
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Figure 7. (a) Indicates bar chart of species distribution at genus level. Different coloured columns represent different species, and column length represents the proportion of species. (b–j) Indicates the prediction results based on BUGbase high-level phenotype classification, which classifies the microbe community into seven phenotypes, including oxygen utilising (aerobic (b), anaerobic (c), and facultatively anaerobic (d)), Gram-positive (e), Gram-negative (f), mobile element-containing (g), oxidative stress tolerant (h), pathogenic (i), and biofilm forming (j). * and ** indicate significant differences among TS, PF, MF, and TM at p ≤ 0.05 and p ≤ 0.01.
Figure 7. (a) Indicates bar chart of species distribution at genus level. Different coloured columns represent different species, and column length represents the proportion of species. (b–j) Indicates the prediction results based on BUGbase high-level phenotype classification, which classifies the microbe community into seven phenotypes, including oxygen utilising (aerobic (b), anaerobic (c), and facultatively anaerobic (d)), Gram-positive (e), Gram-negative (f), mobile element-containing (g), oxidative stress tolerant (h), pathogenic (i), and biofilm forming (j). * and ** indicate significant differences among TS, PF, MF, and TM at p ≤ 0.05 and p ≤ 0.01.
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Figure 8. Prediction results based on BUGbase high-level phenotype classification at phylum level (including Contains_Mobile_Elements (a), Stress_Tolerant (b) and Potentially_Pathogenic (c)) and genus level (including Contains_Mobile_Elements (d), Stress_Tolerant (e) and Potentially_Pathogenic (f)). Different coloured columns indicate different phyla or genera, which are classified into different phenotypes. The column length represents the relative abundances of phyla or genera.
Figure 8. Prediction results based on BUGbase high-level phenotype classification at phylum level (including Contains_Mobile_Elements (a), Stress_Tolerant (b) and Potentially_Pathogenic (c)) and genus level (including Contains_Mobile_Elements (d), Stress_Tolerant (e) and Potentially_Pathogenic (f)). Different coloured columns indicate different phyla or genera, which are classified into different phenotypes. The column length represents the relative abundances of phyla or genera.
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MDPI and ACS Style

Zhu, Y.; Yang, J.; Zhang, J.; Tong, Y.; Su, H.; Rensing, C.; Feng, R.; Zheng, S. Assessment of Ecological Recovery Potential of Various Plants in Soil Contaminated by Multiple Metal(loid)s at Various Sites near XiKuangShan Mine. Land 2025, 14, 223. https://doi.org/10.3390/land14020223

AMA Style

Zhu Y, Yang J, Zhang J, Tong Y, Su H, Rensing C, Feng R, Zheng S. Assessment of Ecological Recovery Potential of Various Plants in Soil Contaminated by Multiple Metal(loid)s at Various Sites near XiKuangShan Mine. Land. 2025; 14(2):223. https://doi.org/10.3390/land14020223

Chicago/Turabian Style

Zhu, Yanming, Jigang Yang, Jiajia Zhang, Yiran Tong, Hailan Su, Christopher Rensing, Renwei Feng, and Shunan Zheng. 2025. "Assessment of Ecological Recovery Potential of Various Plants in Soil Contaminated by Multiple Metal(loid)s at Various Sites near XiKuangShan Mine" Land 14, no. 2: 223. https://doi.org/10.3390/land14020223

APA Style

Zhu, Y., Yang, J., Zhang, J., Tong, Y., Su, H., Rensing, C., Feng, R., & Zheng, S. (2025). Assessment of Ecological Recovery Potential of Various Plants in Soil Contaminated by Multiple Metal(loid)s at Various Sites near XiKuangShan Mine. Land, 14(2), 223. https://doi.org/10.3390/land14020223

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