1 Introduction

It has been well documented that the riparian zone is an effective sink of metals derived from anthropogenic activities, such as industrial activities, mining exploration, agricultural activities, domestic waste discharge, and vehicle emission, due to its integration and accumulation of contaminants from aquatic systems (Haghnazar et al. 2020; Xiao et al. 2019). Consequently, the concentrations of soil metals in the fluctuation zones have been used to assess the pollution status of the basin (Ustaoğlu and Islam 2020; Zhao et al. 2020a). Metals have been recognized as one of the most serious pollutants because of their persistent nature, bioaccumulation, and high toxicity (do Nascimento and Xing 2006; Kostka and Leśniak 2020; Zhao et al. 2017a). Moreover, metals have detrimental effects on aquatic and terrestrial organisms and water quality and then pose harmful effects on human health by entering the food chain (Jiang et al. 2008; Wang et al. 2017; Zhao et al. 2017b). Therefore, it is worth devoting more attention to the distribution and ecological risk assessment of metals in the soil of water-level fluctuation zone, which has important theoretical and practical significance for aquatic environment ecological risk control and ecological restoration.

The construction and operation of hydroelectric dams can alter the migration and transport patterns of element transport, as well as metals (Frémion et al. 2016; Maavara et al. 2017; Liu et al. 2020; Zhao et al. 2020b). In addition, the fate of metals might be affected by the physical and geochemical processes occurring in fluctuation zones in the reservoirs (Zhang et al. 2019), which may cause an increasing accumulation of metals in the soils and sediments (Bing et al. 2016; Gao et al. 2016). Therefore, the distribution and pollution status of metals in the basin since the construction of reservoirs has attracted continuous attention from researchers. Nevertheless, existing related studies are mainly based on the Three Gorges Reservoir basin, which is the world’s largest hydropower construction (Bing et al. 2016; Gao et al. 2016; Ye et al. 2011), and less attention has been devoted to the reservoirs of Southwest China, which is enriched in hydropower resources with numerous reservoirs (Yin et al. 2020; Yuan et al. 2019).

As an important form of water resource utilization, cascade reservoirs have been constructed on the Jinsha River from 2011 to 2020, including the stations of Liyuan, Ahai, Jin’anqiao, Longkaikou, Ludila, Guanyinyan, Wudongde, Biahetan, Xiluodu, and Xiangjiaba (Yuan et al. 2019). Research by Hu et al. (2009) and Wu et al. (2013) indicated that the construction of the Liyuan and Ahai stations caused the sedimentation of lighter and more weatherable minerals. In addition, the Jinsha River, flows through Lijiang, Panzhihua, Liangshan, Chaotung, and Yibin cities, where a number of mining industries are distributed. This made the mainstream of the Yangtze River increasingly subject to anthropogenic disturbances, which might lead to metal pollution (Song et al. 2010). Therefore, it is necessary to increase the regularity of monitoring the change in metal concentrations in the soil of fluctuation zones of the upper reaches of the Yangtze River, including the Jinsha River.

The aims of the present study were to (i) reveal the spatial distribution of metals (Cd, Cr, Cu, Ni, Pb, and Zn) along the upper reaches of the Yangtze River; (ii) define the degree of metal pollution and assess their ecological risk; and (iii) discriminate and apportion the possible sources of the six metals. This research will provide a comprehensive understanding of the contamination status and potential sources of metals in soils of fluctuation zones in the upper reaches of the Yangtze River, and the results may facilitate the development of an effective river protection plan.

2 Materials and methods

2.1 Site description

All 13 sampling sites were distributed along the upper reaches of the Yangtze mainstream (Fig. 1), including 7 sites (S1–S7) upstream of the corresponding hydropower reservoirs on the Jinsha River and 6 sites (S8–S13) from the Three Gorgers Reservoir. The Jinsha River mainly flows through Sichuan, Yunnan, Tibet, and Qinghai provinces, with a total length of 3500 km and a natural drop of 5100 m. It is an important part of the upper reaches of the Yangtze River (Li et al. 2013). The annual power generation of the Jinsha River Basin is 592.7 billion kW·h, accounting for 50% of the Yangtze River basin. The Three Gorges reservoir, located in the lower reaches of the Jinsha River, lies in a 600 km valley from Yichang to upstream Chongqing. With the Three Gorges Dam fully functioning in 2010, the water level in the reservoir fluctuates from 145 m in summer to 175 m in winter, forming a fluctuation zone with a total area of 58,000 km2 (Zhang and Luo 2011). The region has a subtropical monsoon climate, with annual mean temperatures of 13.63–19.25 °C and precipitation 893–1306 mm.

Fig. 1
figure 1

Location of the sampling sites

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2.2 Sample collection and analysis

At each site, three to six sampling plots were randomly established in the elevation from the lowest to the highest of the water level fluctuation zone. However, at S13, samples were collected only between 145 and 165 m because the ecological restoration measures covered the higher elevation. At each plot, three topsoil samples from 0–20 cm were collected by using stainless spatula and then well mixed for analysis.

A total of 13 sampling sites were chosen along the upper stream of the Yangtze River, from the Ahai hydropower station on the Jinsha River to the Xiangxi River, which belongs to the Three Gorges reservoir, situated from 27°22′58′′N 100°31′8′′E to 31°0′59′′N 110°45′22′′E (Fig. 1). All surface soil samples were collected by clean polyethylene bags and stored in a refrigerator at 4 °C in clean polyethylene bags in the laboratory for analysis.

2.3 Chemical analysis

The thoroughly mixed soil samples were naturally air-dried and powdered with a mortar and pestle after removing the stones and plant roots. Then, the soil samples were passed through a 100-mesh nylon screen and loaded into clean polyethylene bags. For the analysis of metals, soil subsamples (0.2000 g) were weighed in Teflon vessels and digested in a microwave digestion system with a mixture of 8 mL HNO3 (68%) and 2 mL HClO4 (72%). After cooling to ambient temperature, the digested samples were filtered and diluted to a 25 mL volume with 1% HNO3. Finally, the concentrations of Cd, Cr, Cu, Ni, Pb, and Zn in the solutions were measured with inductively coupled plasma mass spectrometry (ICP-MS) (EXPEC 7000). Standard operating procedures were followed, with reagent blanks and three duplicate samples, as well as certified standard reference materials (GBW07385). Recoveries were obtained for Cd (90–102%), Cr (85–105%), Cu (86–97%), Ni (99–109%), Pb (85–92%), and Zn (90–104%). Relative standard deviations for the three duplicate samples were found to be less than 5%.

2.4 Environmental and ecological risk assessment

2.4.1 Enrichment factor (EF)

Enrichment factor (EF) was originally used for the assessment of air quality (Zoller et al. 1974) and only further used to evaluate the soil metal pollution status (Varol 2011). The EF was calculated as below:

$$\mathrm{EF}=\frac{({\mathrm{C}}_{\mathrm{i}}/{\mathrm{C}}_{\mathrm{ref}})\mathrm{Sample}}{({\mathrm{C}}_{\mathrm{i}}/{\mathrm{C}}_{\mathrm{ref}})\mathrm{Background}}$$

where Ci is the concentration of metal (i) in soil samples; Cref is the concentration of reference metal for geochemical normalization. In order to establish the geochemical background values in China (Table 1), the spatial distribution of soil metals over large national or continental areas has been studied for a long time (Chen et al. 1991). In the current study, Fe was chosen as the reference metal because of the following reasons: (1) Fe is associated with fine solid surfaces; (2) its geochemistry is similar to that of many trace metals, and (3) its natural concentration tends to be uniform (Daskalakis and O'Connor 1995). EF values were interpreted as follows: EF < 2 indicates minimal enrichment; 2 < EF < 5 is moderate enrichment; 5 < EF < 20 is significant enrichment; 20 < EF < 40 is very high enrichment; and EF > 40 is extremely high enrichment.

Table 1 The background values of soil metals in the Yangtze mainstream (mg kg−1)
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2.4.2 Contamination factor (Cf)

Contamination factor (Cf) is commonly used to assess the degree of metal pollution in soils, as given by Hakanson (1980). The Cf was calculated as below:

$${\text{Cf}}=\frac{{\mathrm{C}}_{\mathrm{i}}}{{\mathrm{C}}_{\mathrm{n}}^{\mathrm{i}}}$$

where Ci is the concentration of metal (i) in soil samples; \({C}_{n}^{i}\) is the background value (preindustrial) of metal (i) (Chen et al. 1991). Cf values were interpreted as follows: Cf < 1 indicates low contamination; 1 < Cf < 3 is moderate contamination; 3 < Cf < 6 is considerable contamination; and Cf > 6 is very high contamination.

2.4.3 Geoaccumulation index (Igeo)

Geoaccumulation index (Igeo) is a quantitative indicator commonly used to assess the pollution levels of metals in soil of the water-level fluctuation zone (Müller 1981). The Igeo was calculated as below:

$$Igeo={\mathrm{log}}_{2}\frac{{\mathrm{C}}_{\mathrm{i}}}{1.5\times {\mathrm{B}}_{\mathrm{i}}}$$

where Ci is the concentration of metal (i) in soils (Bhuiyan et al. 2010) instead of pelitic sediment fraction (< 2 µm) (Förstner et al. 1990); Bi is the geochemical background value of metal (i) (Chen et al. 1991). Igeo values consist of seven classes. Class 0 (unpolluted): Igeo ≤ 0; Class 1 (slightly polluted): 0 < Igeo < 1; Class 2 (moderately polluted): 1 < Igeo < 2; Class 3 (moderately to heavily polluted): 2 < Igeo < 3; Class 4 (heavily polluted): 3 < Igeo < 4; Class 5 (heavily to extremely polluted): 4 < Igeo < 5; Class 6 (extremely polluted): Igeo ≥ 5.

2.4.4 Potential ecological risk index

Potential ecological risk index (RI) not only reflects the risk of single metal pollution but also reflects the comprehensive impact of pollution from a variety of metals (Hakanson 1980). The RI was calculated as below:

$$\mathrm{RI}=\sum {\mathrm{E}}_{\mathrm{i}}=\sum [{\mathrm{T}}_{\mathrm{i}}\times \left(\frac{{\mathrm{C}}_{\mathrm{s}}^{\mathrm{i}}}{{\mathrm{C}}_{\mathrm{n}}^{\mathrm{i}}}\right)]$$

RI = where Ei is the potential ecological risk coefficient of metal (i); Ti is the toxic-response factor of metal (i), which is 1, 10, 5, 30, 5, 2, and 5 for Zn, As, Cu, Cd, Ni, Cr, and Pb, respectively (Hakanson 1980); \({C}_{s}^{i}\) is the measured concentration of metal (i) in soil samples; and \({C}_{n}^{i}\) is the geochemical background value of metal. Ei was divided into five classes: Ei < 40, 40 < Ei < 80, 80 < Ei < 160, 160 < Ei < 320, Ei > 320, representing low risk, moderate risk, considerable risk, high risk, and extreme high risk, respectively. RI was divided into four classes: RI < 150, 150 < RI < 300, 300 < RI < 600, and RI > 600, representing low risk, moderate risk, considerable risk, and (extremely) high risk, respectively.

2.5 Statistical analysis

Two-way ANOVA was used to examine the spatial variations of the elements by SPSS software version 20.0, which was also used for the multivariate statistical analysis and descriptive and correlation analyses. Multivariate statistical analysis, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), was conducted to help identify the potential sources of the metals.

3 Results and discussion

3.1 Spatial variation of soil metal concentrations

The mean values of six metals in soils of the water-level fluctuation zone from 13 sampling sites along the upper reaches of the Yangtze mainstream are given in Fig. 2. In this research, the concentration ranges of the six metals in soils were 0.14–0.69 (mean value: 0.36) mg kg−1 for Cd, 36.55–120.32 (61.00) mg kg−1 for Cr, 14.63–219.47 (74.82) mg kg−1 for Cu, 30.47–69.74 (44.18) mg kg−1 for Ni, 15.48–52.75 (28.15) mg kg−1 for Pb, and 60.83–155.41 (110.98) mg kg−1 for Zn. The average concentration of Cr did not exceed the background value, while the averages of Cd, Cu, Ni, Pb, and Zn were 3.70, 3.31, 1.64, 1.08, and 1.49 times higher than their corresponding background values, respectively (Chen et al. 1991). All the metals showed significant spatial variation at the sampling sites (p < 0.01), especially from S1 to S7 (Fig. 2), which might be attributed to the interception and blocking effect of some cascade dams along the Jinsha River (Akindele et al. 2020). According to previous studies, the average annual sediment load at Panzhihua station decreased by 75.9% during 2010–2015 due to the operation of cascade dams (Li et al. 2018). In recent years, a cascade of several large hydropower dams has been constructed and operated along the Jinsha River, which created significant social and economic benefits but has also been reported to cause ecological risk, modify the natural state of river landforms, and disrupt the transport of soil, metals, and nutrients (Yuan et al. 2019, 2021; Chen et al. 2019, 2020).

Fig. 2
figure 2

Spatial variation in Cd (A), Cr (B), Cu (C), Ni (D), Pb (E), Zn (F) at the 13 sampling sites (mean ± SE). The dotted line indicates the corresponding background value of each metal

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The maximum concentrations of Cd, Cr, Cu, Ni, Pb, and Zn were detected at S13, S6, S6, S4, S13, and S2, respectively. The metal concentrations at S6 (except Pb), S8, and S13 were significantly higher than those at the adjacent sample sites (p < 0.05). With the protection of The Origins of Three Rivers-Natural Resources Protecting Project and Strategy of Yangtze River Economic Belt proposed by State Council, the metal concentrations in this study were relatively lower when compared with the results that were found by Wang et al. (2014) on Yangtze River. However, historical pollution caused by past overexploitation still remains, and there still exist a few mining industries, metallurgical and chemical industries, and metal manufacturing industries in Liangshan, Zhaotong, and Yibin cities (Huang et al. 2008; Yuan et al. 2019) along the Jinsha River, which might be responsible for the high Cd, Cu, Cr, Ni, and Zn concentrations at S3, S4, and S6 and generally higher metal concentrations in the upper reaches (S1–S6). Sampling site S8, located in Shibaozhai, which was a tourist spot, was significantly affected by anthropogenic activities. Therefore, the relatively high concentrations of metals, especially Cd and Pb at S8, might be mainly due to human activities such as industrial activities and domestic sewage discharge from Chongqing city with population aggregation (Ye et al. 2011). In addition, previous studies showed that Cd might be correlated with the mining of phosphate rock and the application of phosphate fertilizer on agricultural land (Haghnazar et al. 2021a; Huang et al. 2018), which conformed to the result of this study. The maximum concentration of Cd was found at S13, located at the Xiangxi River with rich phosphate rock and agricultural land, consistent with the result of Wang et al. (2017).

3.2 Environmental risk assessment

3.2.1 Assessment based on enrichment factor (EF)

The average EF values of metals decreased in the order of Cd (2.26) > Cu (1.39) > Zn (0.94) > Ni (0.90) > Pb (0.71) > Cr (0.58) (Fig. 3A). EF is often used to evaluate anthropogenic influences on metal pollution in soils (Qu et al. 2018; Zhang et al. 2009). In the present study, the EF of Cu, Zn, Ni, Pb, and Cr at all sites showed minimal enrichment as a whole, while Cu at S6 had an EF value of 2.42. Previous studies have suggested that Cd is a typical anthropogenic metal affected by human activities (Hu et al. 2019; Wan et al. 2021). Consistent with this, Cd had the strongest enrichment factor in this study, especially at S8 and S13, which have intensive human activities. The EFs of Cd at S1, S3–S6, S9–S10, and S12 were less than 2, classified as minimal enrichment, while those at S2, S7, S8, S11, and S13 ranged from 2 to 5, indicating moderate enrichment. The maximum EF value of Cd, 4.71, came from S13, and Ni, Pb, and Zn possessed the maximum EF value at S13. The largest EF values of Cr and Cu were found at S8 and S6, which were 0.86 and 2.42, respectively.

Fig. 3
figure 3

The mean values of the enrichment factor (EF) (A), contamination factor (Cf) (B), geoaccumulation index (Igeo) (C), and ecological risk index for single metals (Ei) (D) at all sites. The dotted line indicates the corresponding classification value of each index. EF < 2 and 2 < EF < 5 indicates minimal enrichment and moderate enrichment, respectively. Cf < 1, 1 < Cf < 3, 3 < Cf < 6 and Cf > 6 indicates low contamination, moderate contamination, considerable contamination, and very high contamination, respectively. Igeo ≤ 0, 0 < Igeo < 1, 1 < Igeo < 2, and 2 < Igeo < 3 represents unpolluted, slightly polluted, moderately polluted, and moderately to heavily polluted, respectively. Ei < 40, 40 < Ei < 80, 80 < Ei < 160, and 160 < Ei < 320 represents low risk, moderate risk, considerable risk, and high risk, respectively

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3.2.2 Assessment based on contamination factor (Cf)

The average value of Cf decreased in the order of Cd (3.56) > Cu (3.08) > Ni (1.58) > Zn (1.55) > Pb (1.07) > Cr (0.98) (Fig. 3B). The Cf of Cd at S1, S3, S5, S9, and S10 was less than 3, classified as moderate contamination; S2, S4, and S6–S8 were between 3 and 6, classified as considerable contamination; and S13 was over 6, classified as very high contamination. The Cf of Cu at S9 and 11 was less than 1, S1, S2, S5, S7, S8, S10, and S12 were between 1 and 3, S13 was between 3 and 6, and S3, S4, and S6 were above 6. The Cf of Ni at all sites was between 1 and 3. The Cf values of Pb at S1, S3–S7, and S9 were less than 1, and those at the other sites were between 1 and 3. The Cf of Zn at all sites was between 1 and 3. The Cf of Cr at S1, S2, S7, and S9–S12 was less than 1, classified as low contamination, while S3–S8 and S13 were between 1 and 3, classified as moderate contamination. The maximum Cf values of Cd, Cr, Cu, Ni, Pb, and Zn were found at S13, S6, S3, S14, S13, and S6, which were 7.11, 1.73, 9.22, 2.59, 2.02, and 2.37, respectively.

3.2.3 Assessment based on geoaccumulation index (Igeo)

The average values of Igeo followed the descending order: Cd (1.10) > Cu (0.55) > Ni (0.05) > Zn (−0.09) > Pb (−0.62) > Cr (−0.74) (Fig. 3C). The Igeo values of Cd at S3 were less than zero, presenting to be uncontaminated, S5, S9, and S10 were between 0 and 1, classified as slightly polluted, and S1, S2, S4, S6–S8, S11–S12 were between 1 and 2, classified as moderately polluted, while S13 had the highest value (2.19), corresponding moderately to heavily polluted. The Igeo values of Cu at S2, S7, and S9–S12 were less than zero; S1, S5, and S8 were between 0 and 1; and S3, S4, S6, and S13 were between 1 and 2. The Igeo of Ni at S2, S5, S7, and S9–S12 was less than zero, and S1, S3, S4, S6, S8, and S13 were between 0 and 1. The Igeo of Zn at S1, S5, S7, and S9–S12 was less than zero, while that at other sites was between 0 and 1. The Igeo values of Pb at all sites except S2, S8, and S13 were less than zero. The Igeo of Cr at all sites except S6 (0.32) was less than zero. Generally, the great majority of the sampling sites were unpolluted or slightly polluted by Ni, Zn, Pb, and Cr, while S6 and S13 were moderately polluted or moderately to heavily polluted by Cd and Cu. The maximum Igeo values of Cd, Cr, Cu, Ni, Pb, and Zn were found at S13, S6, S6, S6, S13, and S2, which were 2.19, 0.32, 2.69, 0.78, 0.39, and 0.47, respectively.

3.3 Ecological risk evaluation

The average values of Ei followed the descending order: Cd (106.77) > Cu (15.38) > Ni (7.88) > Pb (5.33) > Cr (1.95) > Zn (1.55) (Fig. 3D). The Ei of Cd at all sites was greater than 40, especially S8 and S13, which were greater than 160, indicating considerable ecological risk, while the Ei of Cr, Cu, Ni, Pb, and Zn at all sites except Cu at S3 was less than 40, classified as low ecological risk. With great mobility, Cd can be easily transferred with weatherable minerals by hydraulics along rivers, which may cause Cd to accumulate and pose a high potential ecological risk downstream (Bing et al. 2016). The maximum Ei values of Cd, Cr, Cu, Ni, Pb, and Zn were found at S13, S6, S3, S4, S13, and S6, which were 213.41, 3.46, 46.08, 12.95, 10.14, and 2.37, respectively.

The RI values at the sampling sites followed the order of S13 > S8 > S6 > S4 > S2 > S11 > S12 > S7 > S3 > S10 > S1 > S9 > S5, ranging from 79.99 to 252.62 with an average of 138.87. The RI values at S4, S6, S8, and S13 were greater than 150, showing moderate ecological risk, while other sampling sites were classified as having low ecological risk (Fig. 4). The contribution of metals to the RI is demonstrated in Fig. 5, which shows that Cd is the largest contributor (76.89%), followed by Cu (11.08%). The contributions of Ni, Pb, Zn, and Cr are relatively small. In terms of the RI results, there was the highest potential ecological risk of metals at S13, located in Xiangxi, the area around which is characterized by widespread agricultural development, a dense population, and multiple phosphate mines (Yan et al. 2016).

Fig. 4
figure 4

The mean values of the ecological risk index (RI) of trace metals at all sites

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Fig. 5
figure 5

The contribution of metals to the ecological risk index (RI)

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3.4 Multivariate statistical analysis

The results of the correlation analysis of different metals showed that Cd had highly significant positive correlations with Pb and Zn (p < 0.01) (Table 2), and Cr had positive correlations with Cu, Ni, and Zn, illustrating that these metals in the soils possibly have similar origins. In addition, Zn had positive correlations with all the other elements.

The results of the PCA showed that two varifactors with eigenvalues greater than 1 explained 87.99% of the total variance (Table 3). The first varifactor (PC1), accounting for 51.46% of the total variance, had strong positive loadings on Cr (0.84), Cu (0.98), Ni (0.96), and Zn (0.65), and the other varifactor (PC2), accounting for 36.5%, had strong positive loadings on Cd (0.90) and Pb (0.96). In addition, Zn also had loads on PC2 (0.59), which indicated that the source of Zn was affected by many factors.

Table 2 Pearson correlation matrix of trace metals. **Means p < 0.01, * means p < 0.05
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Table 3 Principle component factor loadings
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The results of hierarchical cluster analysis (HCA) indicated that the six metals were grouped into two main clusters (Fig. 6A), including cluster 1 with Cr, Cu, Ni, and Zn and cluster 2 with Cd and Pb, which confirmed the PCA results. In this research, HCA analysis was also used to group sampling sites based on their similar characteristics (Fig. 6B). The results showed that the 13 sampling sites were grouped into three clusters. Cluster 1 consisted of seven sites (1, 5, 7, 9–12), which had relatively lower concentrations of the six metals. Cluster 2 consisted of S2, S8, and S13, which had relatively higher Cd, Pb, and Zn, and cluster 3 consisted of S3, S4, and S6, which had relatively higher Cu, Ni, and Cr.

Fig. 6
figure 6

Hierarchical clustering analysis shows the relevant association among the analyzed metals (A) and sampling sites (B)

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3.5 Source identification

According to the multivariate statistical analysis, two main sources were identified for the metals. PC1 revealed strong relationships between Cr, Cu, Ni, and Zn, which was consistent with the results of correlation analysis and HCA, indicating that those metals had similar sources. It has been reported that metals are mainly derived from natural processes or crustal material when EF < 1.5 and are mainly influenced by human activities when EF > 1.5 (Haghnazar et al. 2021b; Zhang and Liu 2002). Thus, Cu, Cr, Ni, and Zn more likely originated from minerals and rocks based on the results of the average EF values, which was consistent with previous studies (Pan et al. 2017; Zhuang et al. 2021). Similar to the EF results, the average values of CF, Igeo, and Ei of each metal also suggested that Cr, Ni, and Zn were uncontaminated and presented low ecological risk. However, the concentrations of Cr, Cu, and Ni remained relatively high at S3–S4 and S6 along the Jinsha River, which was mainly affected by mineral exploitation and industrial waste. Previous studies have shown that Cu originates from natural processes and the exploitation of multimetal minerals (Wang et al. 2011, 2014). Thus, PC1 was mainly contributed by crustal material, mineral exploitation, and industrial waste.

In this research, although the values of the environmental and ecological risk assessment parameters of Pb were relatively low on average, they presented high ecological risks at S2, S8, and S13. It has been demonstrated that the source of Pb, which should be of concern, results from industrial discharge and the combustion of gasoline (Haghnazar et al. 2022a; Zhu et al. 2015; Zhuang et al. 2021). Studies have also shown that Pb and Zn are related to the mineral deposits rich in metals plumbum and zinc, and Cd always occurs as an associated metal in the deposits as well as agricultural irrigation (Zhu et al. 2013). In addition, Cd, a typical anthropogenic metal affected by human activities, was found to be generated by domestic sewage, industrial waste and excessive application of chemical fertilizer (Ye et al. 2011; Wang et al. 2017). In this study, the concentrations of Cd, Pb, and Zn were relatively higher at S2, S8, and S13, with intensive human activities, such as widespread agricultural development of multiple phosphate mines and domestic sewage discharge, which have been reported to be the main sources of Cd, Pb, and Zn (Haghnazar et al. 2022b). Therefore, in this research, PC2 is considered to present mineral exploitation, domestic sewage, and agricultural activities, with high loadings on Cd, Pb, and Zn.

4 Conclusion

In this study, metals showed significant spatial variation at the 13 sampling sites along the upper reaches of the Yangtze mainstream. The concentrations of Cd and Cu were significantly high in the soils at all sampling sites, which suggested that Cd and Cu posed a higher threat to the ecological environment in the research area. The PCA and HCA results indicated that Cr, Cu, Ni, and Zn were mainly derived from crustal material, mineral exploitation, and industrial waste, while Cd and Pb were derived from agricultural activities and mineral exploitation. In addition, S2, S8, and S13 had relatively higher Cd, Pb, and Zn, and S3, S4, and S6 had relatively higher Cu, Ni, and Cr, while other sites (1, 5, 7, 9–12) had relatively lower concentrations of the six metals. The potential ecological risk index for all metals indicated that sites S1, S3, S5, S7, and S9–S12 posed low ecological risk, and S4, S6, S8, and S13 posed moderate ecological risk. In general, attention should be devoted to the environmental threat of metals, especially Cd, in the water-level fluctuation zone of the upper reaches of the Yangtze mainstream. In particular, contamination caused by mining, industrial waste, domestic waste, and agricultural activities is a potential cause for concern that would have to be considered in future pollution prevention and control strategies in environmental protection in the Yangtze River.