Assessment of Soil Heavy Metal Pollution and Phytoremediation Potential of Dominant Species in Water-Level-Fluctuation Zone of Large Reservoir

Abstract

The water-level-fluctuation zone of a reservoir is an important connection between land and water; due to counter-seasonal rhythmic water level changes, this area is more sensitive and vulnerable, whereas the assessment and management of heavy metal pollution are still elusive. This study selected the water-level-fluctuation zone in the hinterland of the Three Gorges Reservoir. Through a quadrats investigation, the soil pollution status of seven heavy metals and the composition of herbaceous plants were identified, and the potential of the dominant species for phytoremediation was assessed. The results showed that the overall pollution was at the alert level (P_N = 0.97) and a low ecological risk level (RI = 123.12). And the study area was primarily contaminated by a mixed source of traffic, agriculture, and industry. A total of 17 dominant species were calculated using importance values. Most dominant species had better phytoremediation potential for Cd and Cu. None of the 17 dominant species were hyperaccumulators, but they could still play an important role in phytoremediation due to their high adaptability to specific environments. The results have guiding significance for the management and remediation of soil heavy metal pollution in global reservoir areas, further promoting global ecological security and sustainable development.

1. Introduction

With the rapid development of global industrialization and urbanization, heavy metal pollution poses a significant threat to human health and the stability of ecosystems, emerging as a critical environmental issue worldwide [1,2,3]. Numerous studies indicate that the primary sources of heavy metal pollution in the environment are anthropogenic, with metals from activities such as mining, industrial emissions, solid waste disposal, and wastewater irrigation entering soils and water through atmospheric deposition and runoff [4,5]. Although heavy metals typically exist at low levels in the environment, their high toxicity leads to accumulation over time, eventually exceeding toxicity thresholds. Once they enter the food chain and food web, they can have severe toxic effects on living organisms, impacting the entire ecosystem [6,7,8]. Awareness of the environmental and health problems caused by heavy metals is increasing, particularly concerning pollution in ecologically sensitive areas [9,10].

The water-level-fluctuation zone (WLFZ) is a special area in rivers, lakes, and reservoirs that is periodically flooded or exposed due to fluctuating water levels. It serves as a dynamic transition zone between terrestrial and aquatic environments, playing a crucial role in the transport and transformation of materials and energy within ecosystems [11,12,13]. However, it is also strongly influenced by aquatic environments and human activities, making it a sensitive and fragile ecological region between rivers and upland vegetation. This area is recognized as a hotspot for global biogeochemical cycles and ecological restoration [14,15]. In recent years, the demand for services such as hydropower and flood control has surged globally, leading to the construction of over 2.8 million dams (with reservoir areas exceeding 10³ m²). And many original river systems have been fragmented by dam regulation, resulting in reduced hydrological connectivity [16,17,18]. The operation of these dams alters riverbank morphology, leading to vegetation destruction and significant ecosystem degradation [19]. Consequently, WLFZs are highly susceptible to heavy metal pollution, a growing concern that has garnered widespread attention [20,21,22]. The study of soil heavy metal pollution in WLFZs can not only provide a reference for the management of similar ecosystems globally, but also offer scientific support for regional pollution prevention and ecological security. This can help mitigate the risk of pollutant dispersion, thereby reducing the damage caused by heavy metal pollution to ecosystem services and its threats to human health.

The Three Gorges Dam (TGD) is currently the largest hydropower station in the world. Since its operation, a huge artificial reservoir (i.e., Three Gorges Reservoir, TGR) has been formed, and a WLFZ with a vertical drop of 30 m and an area of approximately 350 Km² has formed [23] (Figure 1). After the formation of this WLFZ, the vegetation diversity in the area sharply decreased, the ecological barrier was damaged, and the environmental problems became increasingly prominent, directly threatening the long-term stable operation of the Three Gorges Project and the sustainable development of the reservoir economy [11]. Due to the vulnerability and sensitivity of this region, there has been global concern regarding its ecological structure, functions, and potential environmental challenges, with heavy metal pollution becoming a research hotspot [6,24,25]. Large-scale and long-term studies have found that since the establishment of the TGD, the concentrations of As, Cr, Pb, and Cu in the soil of the WLFZ have continuously increased, with heavy metal levels in the upper Yangtze River often exceeding those in the lower reaches. In many cases, these levels surpass the acceptable pollution thresholds set by various indices [26]. Four main environmental factors—Fe/Mn oxides, pH, DOC values, and soil dynamics—significantly influence the selective remobilization of heavy metals after flooding [27]. The WLFZ is under increasing pressure.

Phytoremediation is a cost-effective and eco-friendly strategy that has gained increasing attention in recent years [28,29]. The idea of phytoremediation was first put forth by Chaney [30], who noted that excess soil heavy metals could be distinctively removed through the enrichment of pollutants by plants. Plants are able to remove pollutants from soil in several ways, such as phytostabilization, phytostimulation, phytoextraction, phytovolatilization, and phytodegredation [31]. Phytoextraction is a more researched and ideal way to treat heavy metal-contaminated soil, as it is more thorough in removing heavy metals from soil compared to several other methods [32]. Many scholars have studied the accumulation characteristics of heavy metals in plants and found that most plants, especially dominant species, have a strong accumulation capacity for one or more heavy metals [9,33,34]. The plants screened in this way, in addition to absorbing heavy metals, can quickly adapt to local living conditions and grow well, which is a more rapid, efficient, and practical way to remediate heavy metal-contaminated soil. In the WLFZ, periodic, off-season, and high-intensity flooding has a profound impact on vegetation. The plants in this area have evolved under conditions of periodic water–land alternation and possess high ecological adaptability [35]. Therefore, studying the potential for phytoremediation of dominant plants in the WLFZ can provide a scientific basis for pollution control in similar ecosystems and promote the global application of phytoremediation technology.

In this study, we posed the following questions: (1) What was the status of soil heavy metal pollution in the WLFZ of the TGR? (2) Which plants were dominant in this area? (3) What were the accumulation characteristics for heavy metals in the soil by the dominant species? In this way, we hoped to explore the distribution characteristics of soil heavy metal pollution in the WLFZ, identify dominant plant species in this particular ecosystem, and evaluate their ability to accumulate heavy metals. Ultimately, we expected to select advantageous species with potential for phytoremediation, providing practical support for the remediation of heavy metal pollution in the WLFZ of the reservoir.

2. Materials and Methods

2.1. Study Area

The study area was Zhongxian–Shizhu watershed (107°32′17″~108°34′43″ E, 29°38′48″~30°35′45″ N), located in the heart of the TGR (Figure 2), with an area of 38.90 Km², accounting for 11.21% of the total area of the WLFZ in the TGR. This area passes through 13 townships and streets, and human activities are very frequent. In addition, this area, whose geographical location is of great importance, involves the Huanghua Island National Wetland Park, Shibaozhai National Key Cultural Relics Protection Unit, and Ganjinggou Scenic Area. In this area, the lowest water level in summer is 145 m, and the highest in winter is 175 m. This area belongs to a humid subtropical monsoon climate, with an annual average temperature of 18.22 ± 0.56 °C and an annual average precipitation of 1110 ± 75.23 mm [36].

2.2. Quadrat Investigation

According to the literature records and the results of field investigation, herbaceous plants in the WLFZ of the TGR were absolutely dominant [12,14,36], and herbaceous plants are more sensitive to heavy metals and have a short growth cycle, which can efficiently screen out dominant species with the potential to remediate heavy metal-contaminated soil. Therefore, the objects of this study were the herbaceous plants in the WLFZ.

The duration of water inundation varies at different elevations, leading to differences in plant composition and soil contamination. To conduct a more systematic study of the WLFZ, the study area was divided into 3 elevations (i.e., low-elevation area I: 145–155 m; medium-elevation area II: 156–165 m; high-elevation area III: 166–175 m), with each elevation considered as a plot. A total of 28 sampling sites were selected for each plot (with the main stream and important tributaries selected for setting), and 3 quadrats were set for each sampling site, for a total of 252 quadrats with a size of 2 m × 2 m (Figure S1, Figure 2, Table S1). During the investigation, the species name, height, coverage, and frequency of herbaceous plants in the quadrat were recorded. The importance values of all the species in the three plots were calculated separately, and the species whose importance values ranked in the top 10% were taken as the dominant species in that plot. Since the object was herbaceous plants, the calculation formula of the IVs was as follows [37]:

IV (%) = (RH + RC + RF)/3 × 100%(1)

where IV is the importance value, RH is the relative height, RC is the relative coverage, and RF is the relative frequency.

2.3. Sample Collection and Processing

About 100 g of 0–20 cm topsoil was taken from each quadrat by the five-point sampling method. Three soil samples at the same sampling site were mixed and divided into three parts, then packed in ziplock bags. After natural air-drying and removal of debris, the soil samples were first crushed with a wooden stick and then ground into dry powder with a tissue crusher. After passing through a 100-mesh sieve, the samples were sealed and stored in ziplock bags.

According to the survey results of plant diversity in the study area, three plants of each dominant species were randomly selected in the moderately polluted area of the study area for heavy metal determination. Each plant was excavated with soil and brought back. The collected plants were soaked in 2 mM EDTA-Na₂ for 5 min to remove the heavy metal ions adsorbed on the surface, and washed with ultrapure water. After oven-drying at 105 °C for 30 min, the samples were dried at 80 °C to constant weight. Then, they were ground into a powder with a tissue crusher, passed through a 100-mesh sieve, and sealed in a ziplock bag for storage. The rhizosphere soil was obtained by brushing the soil from the roots after shaking the soil attached to the roots [38], and treated as above.

2.4. Determination of Heavy Metal Content

0.05 g samples were digested to a transparent solution and clarified by a microwave digester (Berghof SpeedWave SW-4, Eningen, BW, Germany) under different digestion systems; three replicates were performed for each sample. The plant digestion system was HNO₃:H₂O₂ = 6:2 (volume ratio), and the soil digestion system was HNO₃:H₂O₂:HF = 7:2:1 (volume ratio). The soil solution was heated afterward to remove the residual hydrofluoric acid. Then, all solutions were adjusted to 50 mL with ultrapure water. The contents of As, Cd, Cr, Cu, Ni, Pb, and Zn were determined by inductively coupled plasma-optical emission spectroscopy (ICP-OES, Thermo Fisher iCAP 6300, Waltham, MA, USA) [39]. The standard calibration solutions were prepared using multiple heavy metal standard solutions to calibrate the instruments, and the correlation coefficients of the calibration curves were all greater than 0.999. The method detection limits (MDLs) were 1.4, 0.1, 0.3, 0.3, 0.2, 0.8, and 0.1 μg/L, respectively. Quality control was carried out by setting blank samples, national standard samples (GSS-3, GSB-24), and parallel samples [40]. The recovery rates of the target elements ranged from 90% to 110%, and the relative errors of three parallel samples for each sample were all less than 5%, indicating reliable data.

2.5. Evaluation of Heavy Metal Pollution in Soil

The universally applied Nemerow Composite Pollution Index and Potential Ecological Risk Index were used to evaluate the level of contamination and its ecological impacts [41].

Nemerow Composite Pollution Index [42]:

(2)$P_i=C_i/S_i$

(3)$P_N=\sqrt{{\displaystyle \frac{{\left(P_i\right)}_{max}^2+{\left(P_i\right)}_{ave}^2}{2}}}$

where $P_i$ is the single-factor pollution index of element i, $C_i$ is the measured content of element i, and $S_i$ is the reference value of the evaluation standard of element i. The reference values were adopted from the “Soil Environmental Quality-Risk Control Standard for Soil Contamination of Agricultural Land” [43]. $P_N$ is the soil Nemerow comprehensive pollution index, ${\left(Pi\right)}_{max}$ is the maximum value of P_i for all elements, and ${\left(Pi\right)}_{ave}$ is the average value. The grading standards are shown in Table S2.

Potential Ecological Risk Index [44]:

(4)$E_r^i=T_r^i·{\displaystyle \frac{C_o^i}{C_n^i}}$

(5)$RI={\sum }_{i=1}^n{E}_r^i$

where $E_r^i$ is the single-factor potential ecological risk index of element i, $T_r^i$ is the toxicity response coefficient of element i, $C_o^i$ is the measured content of element i, $C_n^i$ is the soil background reference value of element i, and $RI$ is the sum of the $E_r^i$. The $T_r^i$ of As, Cd, Cu, Ni, Pb, Zn and Cr elements were 10, 30, 5, 5, 5, 1, and 2, respectively. The soil background reference values were adopted from the background values of the TGR [45]. The grading standards are shown in Table S3.

2.6. Source Analysis of Heavy Metals in Soil

The Positive Matrix Factorization (PMF) model, proposed by Finnish scholar Paatero and Tapper [46], is an improved factor analysis receptor model for source apportionment. This model employs an iterative least squares algorithm to decompose the concentration matrix (X) into a source contribution matrix (G), a source profile matrix (F), and a residual matrix (E). The calculation formula was as follows:

(6)$X_{ij}=\sum _{k=1}^p(G_{ik}\times F_{kj})+E_{ij}$

where $X_{ij}$ represents the sample concentration matrix, indicating the content (in mg·kg⁻¹) of the j-th heavy metal in the i-th sample; p is the number of source factors; $G_{ik}$ is the source contribution matrix, indicating the contribution rate of source factor k to the i-th sample; $F_{kj}$ is the source profile matrix, indicating the content (in mg·kg⁻¹) of the j-th heavy metal in source factor k; and $E_{ij}$ is the residual matrix, indicating the residual value of the j-th heavy metal in the i-th sample.

To minimize the value of the objective function Q, PMF selected the most appropriate number of factors by repeatedly calculating and decomposing the original matrix. The calculation formula was as follows:

(7)$Q=\sum _{i=1}^n\sum _{j=1}^m{\left[{\displaystyle \frac{X_{ij}-\sum _{k=1}^p\left(G_{ik}\times F_{kj}\right)}{U_{ij}}}\right]}^2=\sum _{i=1}^n\sum _{j=1}^m{\left({\displaystyle \frac{E_{ij}}{U_{ij}}}\right)}^2$

where Q is the objective function; n is the number of samples; m is the number of heavy metal species; and $U_{ij}$ represents the uncertainty, indicating the uncertainty of the j-th heavy metal in the i-th sample. The calculation formula for the uncertainty value was as follows:

(8)$U_{ij}=\sqrt{{(\delta \times C)}^2+{(0.5\times MDL)}^2}$

where δ represents the relative standard deviation, C is the concentration of the heavy metal (in mg·kg⁻¹), and MDL is the method detection limit for the heavy metal (in mg·kg⁻¹).

2.7. Heavy Metal Accumulating Capacities

The bio-concentration factor (BCF) can reflect the difficulty of the migration of heavy metals in the soil–plant system to a certain extent, while the transport factor (TF) evaluates the transfer ability of heavy metals from roots to shoots. These two indicators are commonly used to assess the remediation potential of plants for heavy metal-contaminated soil [29,30].

(9)$BCF=C_{shoot}/C_{soil}TF=C_{shoot}/C_{root}$

where $C_{shoot}$ denotes the average heavy metal content in the aboveground part of the plant, $C_{root}$ denotes that average content in the underground part of the plant, and $C_{soil}$ denotes the average content of the soil.

The combined enrichment capacity of plants for multiple heavy metals was evaluated by the comprehensive bio-concentration index (CBCI) combined with the affiliation function [47]. From this, the comprehensive bio-transport index (CBTI) was derived to evaluate the comprehensive transport capacity of the plants.

(10)$CBCI={\displaystyle \frac{1}{N}}\sum u\left(x_i\right)u\left(x_i\right)=(x_i-x_{min})/(x_{max}-x_{min})$

(11)$CBTI={\displaystyle \frac{1}{N}}\sum u\left(m_i\right)u\left(m_i\right)=(m_i-m_{min})/(m_{max}-m_{min})$

where $u\left(x_i\right)$ and $u\left(m_i\right)$ are the values of the BCF and TF affiliation function of the plant for heavy metal i, N is the total number of heavy metals investigated, $x_i$ and $m_i$ are the BCF and TF of plants for heavy metal i, and $x_{min}$/$m_{min}$ and $x_{max}$/$m_{max}$ are the minimum and maximum values of BCF/TF for heavy metal i for all plants.

2.8. Data Analysis and Mapping

The processing of raw data was conducted using Microsoft Excel 2019 (Microsoft Corp., Redmond, WA, USA). Using ArcGIS 10.3 (Esri Inc., Redlands, CA, USA) for spatial interpolation analysis and to plot the spatial distribution of heavy metal content, P_N, and RI [48]. To compare the soil heavy metal contamination at different elevations, a one-way analysis of variance (ANOVA) was performed using IBM SPSS Statistics 25.0 (IBM Corp., Armonk, NY, USA), and multiple comparisons were made using Duncan’s test, with the significance level set at 0.05. Before conducting the ANOVA, the normality of the data was checked using the Shapiro–Wilk test and the homogeneity of variances was tested using Levene’s test [49]. The sources of heavy metals in soil and their contribution rates were identified using EPA PMF 5.0 (U.S. EPA, Washington, DC, USA), and the relationship between heavy metal content and PMF model factor scores was visualized using Origin 2021 (OriginLab Corp., Northampton, MA, USA). The CBCI and CBTI of each plant were visualized using Origin 2021. Relevant graphics were created using ArcGIS 10.3, Adobe Illustrator 2020 (Adobe Inc., San Jose, CA, USA), and Adobe Photoshop (Adobe Inc., San Jose, CA, USA).

3. Results

3.1. Heavy Metal Contamination of Soil

The descriptive statistics of the soil heavy metal contents in 28 plots are shown in Table 1. Except for Cu, the average contents of the other six heavy metals exceeded the background values of soil heavy metals in the TGR. As, Cd, and Cr exceeded more significantly, and were 3.23, 2.32, and 2.17 times the background values, respectively.

The spatial distribution pattern of soil heavy metals was mapped using the ordinary Kriging interpolation method (Figure 3). As was distributed relatively uniformly, with relatively high levels at the mouths of tributaries. Cd was distributed discretely, with multiple points of contamination, and with higher levels in the SYX, XSX, and the middle reaches of the main streams. Cu was distributed relatively uniformly, with some localized contamination, and the contents of the SYX, XSX, and SBZ were relatively high. Ni had higher levels in the upstream. The distribution of Pb was obvious in patches, with higher contents in the downstream of the main stream and higher contents in tributaries such as the CXH, TJH, and QJH. Zn had a more uniform distribution, with higher content at the mouths of the SYX and TJH. Cr had a higher distribution in the middle reaches. Overall, As, Cu, and Zn were evenly distributed, while Ni, Pb, and Cr elements were patchily distributed, and the point pollution of Cd was higher.

Overall, the level of soil heavy metal pollution was at the alert level (P_N = 0.97) and low ecological risk level (RI = 123.12). The mean size of the single-factor pollution index (P_i) for the seven elements was as follows: Cd (1.04) > Cr (0.85) > As (0.63) > Zn (0.39) > Ni (0.33) > Pb (0.26) > Cu (0.17), and the mean size of the single-factor potential ecological risk index (E_i) was as follows: Cd (69.67) > As (32.31) > Pb (6.52) > Ni (5.54) > Cr (4.34) > Cu (3.34) > Zn (1.41) (Figure 4(A1,A2)). Taken together, As and Cd were the main pollutants with high ecological risks, especially Cd, which showed mild pollution and moderate ecological risk. Evaluating the soil heavy metal pollution at different elevations (Figure 4(B1,B3)), the overall single-factor and composite evaluation indices decreased gradually with increasing elevation. The low-elevation area (I) was relatively more contaminated with Cd, which was in the mild pollution (P_N = 1.52) and higher ecological risk class (RI = 102.03). Visualizing the integrated evaluation index spatially, P_N and RI were relatively high in the urban area of Zhong County (Figure 4(C1,C2)).

The correlation analysis of the seven heavy metal contents showed positive correlations between the contents of each element. Except for Cr, the correlation of the other elements was strong, and there might be similar sources. The PMF model was used to further quantify the sources of heavy metals (Figure 5). The residuals of seven elements were between −3 and 3, and the signal-to-noise ratios (S/N) were greater than two. The optimal results of the PMF model were obtained when the number of factors was set to three. Factor 1 accounted for 32.28% of the total contribution. Cd and Cu had the highest loads at 39.00% and 22.55%, respectively. Factor 2 carried a weight of 22.30% and was mainly driven by Ni and As, contributing 46.57% and 27.89%, respectively. Factor 3, with the biggest weight of 45.42%, was predominantly influenced by Pb (21.78%) and Zn (21.44%).

3.2. Analysis of the Composition of Herbaceous Plants

A total of 148 species of herbaceous plants in 49 families and 123 genera were recorded in the field survey. There were obvious differences in plant composition at different elevations, with higher species richness as the elevation increased (Figure S2). Based on the calculation of the importance values, Cynodon dactylon and Xanthium strumarium were the dominant species in all elevation areas. Combining the dominant species at different elevations, there were 17 dominant species, with Poaceae and Asteraceae predominating (Table S4).

3.3. Evaluation of the Remediation Potential of Dominant Species for Heavy Metal-Contaminated Soil

The analysis of the accumulation characteristics of the 17 dominant species found that the 17 dominant species had a certain accumulation capacity for all seven heavy metals except As, and that the accumulation characteristics of different plants for different heavy metals were different. The ranges of each heavy metal in the plants were as follows: As 0~0.92 mg·kg⁻¹; Cd 0.55~3.49 mg·kg⁻¹; Cu 2.47~61.73 mg·kg⁻¹; Ni 0~35.76 mg·kg⁻¹; Pb 3.19~8.22 mg·kg⁻¹; Zn 14.05~75.01 mg·kg⁻¹; and Cr 64.27~119.73 mg·kg⁻¹, with mean values of 0.05, 1.37, 24.95, 3.89, 4.88, 31.63, and 79.19 mg·kg⁻¹, respectively, and the contents of heavy metals in the plants were basically the same as those measured in the soil (Table 2). Overall, the dominant species had a good accumulation capacity for Cd and Cu, and a poor accumulation capacity for As, Ni, and Pb, and the contents of As and Ni in most dominant species were below the MDLs. Heavy metals were mainly accumulated in the underground part of the plant, and the Cu, Ni, Zn, and Cr in the underground part of Bidens frondosa were as high as 390.10 mg·kg⁻¹, 171.83 mg·kg⁻¹, 126.10 mg·kg⁻¹, and 537.23 mg·kg⁻¹, respectively, which were far more than other plants. Among all the plants, the aboveground parts of Cyperus rotundus contained the highest amount of Cd and Ni, at 4.20 mg·kg⁻¹ and 6.53 mg·kg⁻¹, respectively.

The BCF ranges of the dominant species for Cd, Cu, Ni, Pb, Zn, and Cr were 0.11–1.20, 0.09–3.82, 0.00–0.25, 0.08–0.28, 0.15–0.67, and 0.11–0.59. The TF ranges were 0.32–1.80, 0.05–5.00, 0.00–0.08, 0.18–0.74, 0.17–1.96, and 0.11–1.29, respectively. A total of 17 plants showed relatively good enrichment and translocation capacity for Cd and Cu, and weak enrichment and translocation for the remaining heavy metals, especially As and Ni (Table S5). C. rotundus showed better enrichment and translocation capacity (BCF&TF > 1). C. dactylon, Setaria viridis, Echinochloa crusgalli var. zelayensis, and Eriochloa villosa also showed BCF and TF greater than 1 for Cu. Analyzing the composite evaluation indices, C. rotundus (CBCI = 0.77, CBTI = 0.68) and E. crusgalli var. zelayensis (CBCI = 0.44, CBTI = 0.58) had the strongest combined enrichment and transport capacity for the seven heavy metals (Figure 6).

4. Discussion

Heavy metal pollution has become an important factor threatening the health of regional ecosystems and an important indicator for evaluating regional environmental quality. Taken together, the main heavy metals pollutants or with potential ecological risks in the study area were As and Cd, especially Cd. Cd is one of the most toxic and mobile elements in the environment, easily transferred from the soil to the food chain, may cause various types of cancers when ingested in excess, and is classified as a Class 1 human carcinogen [50,51]. We should pay great attention to Cd contamination and ensure successful pollution prevention and management. In the WLFZ, the pollution level in urban areas was significantly higher than that in rural areas. The content of heavy metals is mainly influenced by natural geological background and human activities, among which human activities are the main driving factors [52]. After investigation, the Zhongxian–Shizhu section has more towns and cities, and the area along the river is a populated area, with a greater influence from human activities. There are many industrial parks and port terminals along the main stream of the reservoir area, and the “three wastes” produced by industry, agriculture, and ship navigation have a greater impact on the heavy metal content of the soil. The difference in soil heavy metal content caused by different elevations might be due to the different flooding time at different elevations; the low-elevation areas are immersed in water for a long time, and heavy metals are re-deposited into the soil through water transport [12]. And with the increase in elevation, species richness and total biomass increase, while plants have a certain absorption capacity of heavy metals and different plants have different abilities and ways of accumulating different heavy metals, such as phytoextraction, phytostabilization, phytostimulation, etc. [31,53]. This also seems to lead to a decrease in soil heavy metal content with increasing elevation. As a transition zone between land and water bodies, the WLFZ is an important source and sink for carrying and accumulating heavy metals from land and water [12], and together with water and plants it forms a water–soil–plant cycling system. Therefore, the strict control of heavy metal pollution in the WLFZ is of great significance for maintaining the safety and stability of the entire ecosystem, ensuring the normal operation of the middle and lower reaches of the Yangtze River, and protecting human health.

In this study, the PMF model was used to quantify the source analysis of soil heavy metal content in the WLFZ. In Factor 1, Cd and Cu had the highest proportion. Cd is prevalent in fertilizers and pesticides, and is a marker element for agricultural activities. The excessive application of pesticides and phosphate fertilizers may lead to the accumulation of Cd in the environment [54]. Cu is mainly associated with high-Cu fungicides, insecticides, fertilizers, and feeds [55]. In terms of distribution, Cd and Cu have similar distributions, so Factor 1 can be considered as an agricultural source. In Factor 2, Ni and As were the main contributing elements, with relatively high upstream content and a large coefficient of variation for Ni. Some studies have shown that industrial activities have a large impact on the content of Ni and As in soil [56,57,58]. It is known that the upstream is densely populated with industrial parks and has the first 10,000-ton port in the upper reaches of the Yangtze River. Therefore, Factor 2 can be regarded as an industrial source. In Factor 3, the contribution rates of Pb, Zn, Cr, As, and Cu were similar. Except for Cr, the other four elements have a strong correlation and roughly the same distribution trend. Pb is usually considered a symbol of traffic in soil. Zn and Cu originate from transportation emissions and particulate matter emissions. The wear of car tires and corrosion of galvanized car parts lead to the accumulation of zinc in the soil [59,60]. The emissions of pollutants from ship parts and navigation processes may also increase the levels of these heavy metals. As and Cu are also important components of pesticides, fertilizers, and fungicides [54,61], and frequent agricultural activities can lead to the accumulation of As and Cu in the soil. The regions with relatively high Cr content were mainly concentrated in urban, agricultural, and industrial areas, which may be caused by the combined effects of urban waste, industrial emissions, agricultural activities, transportation, etc. Therefore, Factor 3 might be a mixed source of transportation, agriculture, and industry.

Phytoremediation is an effective method to improve the damaged ecosystems of contaminated soils. The key to phytoremediation is to find plants that are not only highly tolerant to complex heavy metal contaminations and have a better accumulation capacity, but are also adapted to the local climate and growing conditions of the contaminated area [62,63,64]. In the special habitat of the WLFZ, the remediation plants not only need to have a better ability to accumulate heavy metals, but also need to undergo the tests of the harsh environment such as drought, flooding, high temperature, and so on. The dominant species in the WLFZ have strong adaptability to the unique ecological environment and possess large biomass. Therefore, it is necessary to evaluate the phytoremediation potential of the dominant species. In this study, the 17 dominant species had good accumulation capacity for Cd and Cu. Cd is more mobile and easily absorbed by plants [65], and the content of Cd in all species was higher than the mean soil Cd content. As an essential element for plants, Cu was accumulated by all plants. The remediation potentials of C. dactylon, S. viridis, E. crusgalli var. zelayensis, E. villosa, and C. rotundus on Cu-contaminated soil were stronger, with both BCFs and TFs greater than one, while the invasive plants, B. frondosa and A. philoxeroides, mainly concentrated a large amount of Cu in their roots (with contents of 390.10 mg·kg⁻¹ and 74.23 mg·kg⁻¹, respectively), so as to keep the Cu from being transferred to the aboveground parts and to prevent the excessive Cu from affecting the plants. While the plants studied here were not hyperaccumulators of heavy metals (HHMs), they had high biomasses and high adaptability to the environment that hyperaccumulating plants do not have, and utilizing them for the remediation of heavy metal-contaminated soils is more practically significant. By using the CBCI and CBTI to evaluate the comprehensive remediation ability of plants, as soil is often contaminated by multiple heavy metals, C. rotundus showed good enrichment and transport capabilities for various heavy metals, which was similar to the results of other researchers [66,67], making it a suitable species for the remediation of heavy metal-contaminated soils. C. rotundus is a perennial clonal herb and also an important medicinal plant. It reproduces through various methods, including tubers, bulbs, and seeds, with tubers being the primary means of reproduction and exhibiting strong reproductive ability. Due to its extremely strong reproductive capacity, it is considered one of the most troublesome weed species in many areas, causing a significant impact on crops [68]. Therefore, when utilizing C. rotundus for the remediation of heavy metal-contaminated soils, it is crucial to strengthen its management and control. In addition, C. dactylon, as a dominant species in all three elevations, also showed great potential for phytoremediation, ranking highly in both CBCI and CBTI. C. dactylon is a perennial herb with a strong survival ability, it can quickly turn green after the water recedes and achieve extremely high coverage in a short period of time, playing an important role in soil and water conservation in the WLFZ [69]. Multiple researchers have conducted extensive mechanistic studies on the response of C. dactylon to Cd stress, and it has been identified as the main phytoremediation species for Cd-contaminated soil [70]. However, due to differences in plant habitats, research results may vary. It is noteworthy that among the 17 dominant species, three species—A. philoxeroides, A. conyzoides, and B. frondosa—are listed in the “List of Invasive Alien Species in China” (First and Fourth Batches) [71,72]. These invasive species demonstrated good accumulation capabilities for specific heavy metals. In particular, B. frondosa exhibited high concentrations of Cr, Cu, Ni, and Zn in its underground parts, reaching 537.23 mg·kg⁻¹, 390.10 mg·kg⁻¹, 171.83 mg·kg⁻¹, and 126.10 mg·kg⁻¹, respectively, making it an excellent candidate for phytostabilization in soils contaminated with multiple heavy metals. The high pollutant absorption rates of invasive species may be related to their extensive adaptability and competitive growth. However, the invasive behavior of these plants can alter the structure and function of ecosystems, potentially leading to the loss of local biocommunities. Currently, the reduction in biodiversity in various ecosystems worldwide poses one of the greatest ecological challenges to humanity. Therefore, the appropriate use of invasive species has become a topic of concern and research interest [73,74].

5. Conclusions

As a transitional zone between water and land, the WLFZ has a unique geographical location, severe environmental conditions, and fragile ecosystems, and its ecological and environmental issues should not be ignored. In the study area, Cd and As were the main pollutants, and the pollution in low-elevation areas and urban areas was relatively severe. Except for As and Ni, the 17 dominant species had a certain accumulation ability for the other heavy metals, especially C. rotundus, which had good comprehensive enrichment and transport abilities. Different plants had different response mechanisms to different heavy metals, and they had good potentials for application in the phytoremediation of the WLFZ. The results provided valuable guidance for the management of heavy metal pollution. Utilizing eco-friendly phytoremediation techniques to remediate heavy metal-contaminated soil in the WLFZ is conducive to maintaining the stability of the ecosystem, further strengthening the ecological barrier and the human health and safety barrier.

Author Contributions

Methodology, H.D., Y.Z. and W.L.; formal analysis, W.L.; investigation, W.L. and D.N.; data curation, X.Z.; writing—original draft preparation, W.L.; writing—review and editing, H.D. and Y.Z.; visualization, W.L. and Y.L.; supervision, C.X. and H.Z.; project administration, H.D. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. A schematic diagram of the water-level-fluctuation zone in the Three Gorges Reservoir. The dry period is from June to September every year, when the lowest water level is 145 m, and the rest is the storage period, when the highest water level reaches 175 m.

Figure 2. Location map and sampling sites of the study area.

Figure 3. Spatial distribution characteristics of heavy metals in soil.

Figure 4. Evaluation of soil heavy metal pollution. (A1,A2) are Nemerow Composite Pollution Index and Potential Ecological Risk Index, respectively. (B1) is Nemerow Composite Pollution Index at different elevations. (B2,B3) are Potential Ecological Risk Indexes at different elevations. I, II, and III represent different elevations. The lowercase letters indicate significant differences between elevations. Different letters denote significant differences between groups (p [less than] 0.05), while the same letters indicate no significant differences. (C1,C2) are spatial distributions of soil heavy metal pollution evaluation.

Figure 5. A correlation analysis of soil heavy metals and source analysis of PMF. A correlation analysis was conducted for the seven heavy metals in the soil (As, Cd, Cu, Ni, Pb, Zn, Cr), where * indicated p [less than] 0.05 and ** indicated p [less than] 0.01. The PMF model was employed to quantitatively analyze the sources of heavy metal content in the soil, with the final number of factors determined to be 3. The lines of different colors represented the contribution rates of different heavy metals within each factor.

Figure 6. Comprehensive bio-concentration index (CBCI, (A)) and comprehensive bio-concentration index (CBTI, (B)) of dominant species for each element. Cd: Cynodon dactylon; Vs: Set aria virilism; Hs: Hemarthria sibirica; Pa: Physalis angulata; Xs: Xanthium strumarium; Ap: Alternanthera philoxeroides; Aa: Artemisia annua; Ms: Mosla scabra; Ac: Ageratum conyzoides; Ds: Digitaria sanguinalis; Pl: Persicaria lapathifolia; Ai: Aeschynomene indica; Ecz: Echinochloa crusgalli var. zelayensis; Bf: Bidens frondosa; Ev: Eriochloa villosa; Pz: Pouzolzia zeylanica; Cr: Cyperus rotundus.

Table 1

Descriptive statistics of heavy metal contents in soil.

	Heavy Metal Content (mg·kg−1)
	As	Cd	Cu	Ni	Pb	Zn	Cr
Maximum	35.64	1.45	42.75	135.1	82.91	219.82	376.8
Minimum	3.73	0.10	3.60	0.76	13.19	39.15	52.80
Mean	18.85	0.31	16.71	32.63	31.12	98.58	169.32
Median	16.98	0.25	15.68	24.00	26.50	94.45	144.30
S.D.	7.66	0.26	7.35	28.30	12.48	31.54	73.26
C.V. (%)	0.41	0.84	0.44	0.87	0.40	0.32	0.43
Background value of TGR	5.84	0.13	25.00	29.47	23.88	69.88	78.03

Table 2

Contents of heavy metals in each part of the dominant species.

Dominant Plants	Position	Heavy Metal Content (mg·kg−1)
		As	Cd	Cu	Ni	Pb	Zn	Cr
C. dactylon	aboveground	/	1.13 ± 0.06	43.33 ± 0.47	2.33 ± 0.32	4.57 ± 0.23	25.57 ± 1.03	86.67 ± 3.86
	underground	/	1.30 ± 0.00	19.33 ± 1.45	41.23 ± 2.32	15.33 ± 1.50	33.57 ± 1.43	93.30 ± 2.00
	total	/	1.16 ± 0.04	39.61 ± 1.24	8.36 ± 1.53	6.23 ± 1.20	26.81 ± 1.21	87.69 ± 3.64
S. viridis	aboveground	/	1.13 ± 0.15	38.67 ± 1.52	/	3.53 ± 0.45	29.63 ± 0.95	67.97 ± 6.82
	underground	/	1.40 ± 0.10	28.50 ± 1.05	46.47 ± 2.89	19.30 ± 0.96	40.20 ± 1.05	272.17 ± 7.30
	total	/	1.15 ± 0.11	37.86 ± 1.46	3.67 ± 2.34	4.78 ± 0.64	30.47 ± 0.88	84.10 ± 6.32
H. sibirica	aboveground	/	0.90 ± 0.10	9.53 ± 0.76	/	3.67 ± 0.40	13.87 ± 0.49	86.67 ± 5.40
	underground	/	1.23 ± 0.21	16.43 ± 1.19	/	12.30 ± 0.70	15.20 ± 0.75	137.03 ± 1.56
	total	/	0.95 ± 0.13	10.49 ± 1.20	/	4.86 ± 0.59	14.05 ± 0.69	93.65 ± 3.68
P. angulata	aboveground	/	1.00 ± 0.00	18.07 ± 0.78	/	3.13 ± 0.23	58.93 ± 2.46	65.60 ± 3.70
	underground	/	1.50 ± 0.17	8.73 ± 0.21	/	15.33 ± 1.25	42.90 ± 1.47	66.97 ± 1.36
	total	/	1.02 ± 0.12	17.61 ± 0.82	/	3.73 ± 1.05	58.14 ± 1.68	65.67 ± 2.75
X. strumarium	aboveground	/	0.60 ± 0.10	11.53 ± 0.25	/	4.00 ± 0.26	54.77 ± 1.36	63.00 ± 4.04
	underground	/	1.60 ± 0.10	19.40 ± 0.96	/	9.70 ± 0.50	49.20 ± 1.05	156.80 ± 14.57
	total	/	0.71 ± 0.08	12.42 ± 0.84	/	4.64 ± 0.47	54.14 ± 1.14	73.61 ± 9.53
A. philoxeroides	aboveground	/	0.63 ± 0.06	19.87 ± 0.32	/	6.33 ± 0.15	70.60 ± 1.65	72.53 ± 3.77
	underground	/	1.93 ± 0.15	74.23 ± 4.95	/	11.33 ± 0.15	82.27 ± 4.75	56.33 ± 2.24
	total	/	1.12 ± 0.10	40.42 ± 3.22	/	8.22 ± 0.17	75.01 ± 3.26	66.41 ± 2.32
A. annua	aboveground	/	0.97 ± 0.06	10.53 ± 0.46	/	5.07 ± 0.25	21.00 ± 0.72	62.57 ± 1.20
	underground	/	2.47 ± 0.12	9.13 ± 0.38	/	8.30 ± 0.26	10.77 ± 0.70	101.03 ± 9.32
	total	/	1.14 ± 0.09	10.37 ± 0.35	/	5.45 ± 0.20	19.80 ± 0.62	67.07 ± 6.64
M. scabra	aboveground	/	0.40 ± 0.00	4.43 ± 0.38	/	5.40 ± 0.26	14.97 ± 0.45	66.30 ± 2.60
	underground	/	1.27 ± 0.06	26.10 ± 0.80	/	9.07 ± 0.75	14.93 ± 0.76	67.50 ± 1.97
	total	/	0.55 ± 0.05	8.07 ± 0.74	/	6.02 ± 0.51	14.96 ± 0.64	66.50 ± 2.37
A. conyzoides	aboveground	/	3.27 ± 0.06	6.33 ± 0.47	/	5.10 ± 0.17	19.87 ± 0.59	61.03 ± 3.60
	underground	/	4.10 ± 0.10	11.10 ± 1.14	/	6.87 ± 0.47	27.67 ± 1.53	111.13 ± 6.35
	total	/	3.34 ± 0.04	6.78 ± 1.05	/	5.26 ± 0.37	20.59 ± 1.42	65.69 ± 4.64
D. sanguinalis	aboveground	/	0.83 ± 0.06	13.10 ± 0.56	/	3.80 ± 0.26	27.23 ± 1.10	63.73 ± 3.16
	underground	/	1.57 ± 0.06	23.60 ± 0.98	/	6.27 ± 0.31	16.80 ± 0.80	158.47 ± 11.49
	total	/	0.95 ± 0.05	14.82 ± 0.84	/	4.21 ± 0.18	25.52 ± 0.78	79.29 ± 8.96
P. lapathifolia	aboveground	/	1.30 ± 0.00	1.27 ± 0.25	/	2.77 ± 0.15	33.63 ± 0.60	62.97 ± 1.60
	underground	/	1.90 ± 0.00	16.60 ± 0.56	9.93 ± 1.79	8.13 ± 0.67	48.27 ± 1.57	253.83 ± 6.54
	total	/	1.35 ± 0.08	2.47 ± 0.46	0.78 ± 1.63	3.19 ± 0.42	34.78 ± 1.46	77.95 ± 3.75
A. indica	aboveground	/	1.57 ± 0.06	7.10 ± 0.56	/	2.57 ± 0.15	19.23 ± 0.60	74.80 ± 3.06
	underground	7.93 ± 0.45	2.17 ± 0.15	50.27 ± 2.45	/	11.03 ± 0.31	37.40 ± 0.95	80.30 ± 0.26
	total	0.92 ± 0.12	1.64 ± 0.12	12.10 ± 1.53	/	3.55 ± 0.19	21.34 ± 0.75	75.44 ± 2.17
E. crusgalli var. zelayensis	aboveground	/	0.97 ± 0.06	67.50 ± 1.70	/	3.77 ± 0.12	24.73 ± 0.40	119.87 ± 11.86
	underground	/	2.00 ± 0.10	13.53 ± 0.72	/	6.47 ± 0.06	17.70 ± 0.79	118.57 ± 4.04
	total	/	1.08 ± 0.11	61.73 ± 1.74	/	4.06 ± 0.10	23.98 ± 0.69	119.73 ± 8.32
B. frondosa	aboveground	/	1.43 ± 0.06	18.80 ± 1.57	/	3.13 ± 0.15	21.60 ± 0.46	60.20 ± 2.08
	underground	/	1.30 ± 0.10	390.10 ± 13.2	171.83 ± 11.43	8.73 ± 0.91	126.10 ± 8.76	537.23 ± 4.87
	total	/	1.42 ± 0.10	49.74 ± 10.42	14.32 ± 10.66	3.60 ± 1.47	30.31 ± 6.43	99.95 ± 3.46
E. villosa	aboveground	/	1.07 ± 0.06	55.47 ± 1.11	/	3.33 ± 0.21	29.00 ± 0.46	60.00 ± 2.17
	underground	/	1.60 ± 0.10	18.40 ± 0.66	/	9.00 ± 0.56	17.00 ± 0.92	193.40 ± 11.69
	total	/	1.08 ± 0.09	54.28 ± 0.85	/	3.51 ± 0.43	28.62 ± 0.75	64.27 ± 6.74
P. zeylanica	aboveground	/	1.17 ± 0.06	12.40 ± 0.89	1.27 ± 0.15	4.97 ± 0.25	22.83 ± 0.61	70.33 ± 2.90
	underground	/	1.27 ± 0.06	14.30 ± 0.56	23.53 ± 0.91	9.87 ± 0.38	24.10 ± 0.61	156.97 ± 5.16
	total	/	1.18 ± 0.04	12.57 ± 0.73	3.25 ± 0.81	5.40 ± 0.31	22.95 ± 0.54	78.06 ± 3.75
C. rotundus	aboveground	/	4.20 ± 0.10	44.87 ± 1.78	6.53 ± 0.29	4.63 ± 0.25	35.90 ± 1.47	83.77 ± 2.82
	underground	/	2.33 ± 0.12	13.00 ± 0.53	83.33 ± 5.44	8.93 ± 0.67	37.37 ± 0.70	76.80 ± 1.47
	total	/	3.49 ± 0.14	32.74 ± 1.54	35.76 ± 3.93	6.27 ± 0.49	36.46 ± 1.24	81.12 ± 1.80

Note: The data are the mean ± standard deviation. “/” indicates a low content, below the MDL.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/land14010090/s1, Figure S1: Schematic diagram of quadrats setup in study area; Figure S2: Plant species composition at different elevations; Table S1: Distribution details of sampling sites; Table S2: Classification standard of Nemerow Synthesis Pollution Index; Table S3: Classification standard of Potential Ecological Risk Index; Table S4: Composition of dominant plants at different elevations in study area; Table S5: BCF and TF of dominant species for each heavy metal.

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