2.5.2. Geo-Accumulation Index (*Igeo*)

The geo-accumulation index (*Igeo*) was proposed by Müller [27], and can evaluate the pollution degree of heavy metals in various environmental media, especially sediments and soils. The pollution degrees of heavy metals are classified into seven levels based on *Igeo*, and the equation for *Igeo* is presented as follows:

$$I\_{\rm geo} = \log\_2 \frac{\mathbf{C}\_i}{\mathbf{1.5} \times \mathbf{B}\_i} \tag{2}$$

where, the meanings of *C<sup>i</sup>* and *B<sup>i</sup>* are consistent with EF. In order to minimize the variation of background values caused by petrogenesis, a constant of "1.5" is introduced. The classification of *Igeo* is shown in Table S3.

#### 2.5.3. Potential Ecological Risk Index (RI)

The potential ecological risk index (RI) is widely used to evaluate the pollution levels and ecological hazard degrees of heavy metals in sediments [28]. The equation for RI is as follows:

$$\text{RI} = \sum E\_r^i = \sum T\_r^i \times \mathbf{C}\_f^i = \sum T\_r^i \times \mathbf{C}\_i / \mathbf{B}\_i \tag{3}$$

where, the meanings of *C<sup>i</sup>* and *B<sup>i</sup>* are consistent with EF, *C i f* is the pollution index of metal *i*,

*E i r* is the potential ecological risk coefficient of metal *i*, *T i r* is the toxicity response coefficient of metal *i*, and, in this study, the *T i r* for Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb were 2, 5, 5, 1, 10, 30, 40, and 5, respectively. RI is the total potential ecological risk of all the investigated heavy metals. The classification and standard values of RI are shown in Table S4.

#### 2.5.4. Toxic Units (ΣTUs) and Toxic-Risk Index (TRI)

Sediments quality guidelines (SQGs) can be used to assess potential ecological risks caused by heavy metals in sediments. SQGs include threshold effect levels (TELs) and probable effect levels (PELs) [29]. Adverse biological effects rarely occur at concentrations lower than the TEL, but often occur at concentrations higher than the PEL. Based on the ratio of measured values to PEL, ΣTUs can be used to evaluate the toxic effects of sediments, and the equation is as follows:

$$\sum TUs = \sum \frac{\mathbf{C}\_i}{\mathbf{C}\_{PEL}} \tag{4}$$

After considering TEL and PEL, the comprehensive toxic effects of heavy metal in sediments, that is, TRI, is calculated using the following equation:

$$\text{TRI} = \sum TRI\_i = \sum \sqrt{\frac{\left(\text{C}\_i/\text{C}\_{PEL}\right)^2 + \left(\text{C}\_i/\text{C}\_{TEL}\right)^2}{2}} \tag{5}$$

where the meaning of *C<sup>i</sup>* is consistent with EF, *CPEL* and *CTEL* are the corresponding PEL, and TEL of metal *i*, respectively.

#### 2.5.5. Risk-Assessment Code (RAC)

In this study, the risk-assessment code (RAC) proposed by Singh et al. [30] has been used to assess the risk of secondary release of heavy metals from sediments in WLSH lake. The calculation equation of the RAC is as follows:

$$\text{RAC} = \mathbb{C}\_{\text{t}} / \mathbb{C}\_{\text{t}} \times 100\% \tag{6}$$

where *C<sup>e</sup>* is the content of exchangeable and carbonate-bound heavy metal and *C<sup>t</sup>* is the total content of heavy metal. According to RAC values, the bioavailability of heavy metal can be classified into five levels, which are shown in Table S5.

#### 2.5.6. Modified Potential Ecological Risk Index (MRI)

Different fractions of heavy metals have different potential ecological risks to the environment. A modified potential ecological risk index (MRI) was applied to assess the potential ecological risks of heavy metals. The MRI was calculated as follows:

$$
\Omega = \mathbf{A}\partial + \mathbf{B} \tag{7}
$$

$$
\tilde{\mathbf{C}}\_{l} = \mathbf{C}\_{l} \boldsymbol{\Omega} \tag{8}
$$

$$
\widetilde{\mathbf{C}\_f^i} = \widetilde{\mathbf{C}\_i} / \mathbf{B}\_i \tag{9}
$$

$$
\widetilde{E}^i\_r = T^i\_r \widetilde{C}^i\_f \tag{10}
$$

$$\text{MRI} = \sum E\_r^i \tag{11}$$

where, *C*e *i* , *C*f*<sup>i</sup> f* , e*Ei r* , and MRI are the modified forms of *C<sup>i</sup>* , *C i f* , *E i r* , and RI, respectively, Ω is the modified indicator, A is the ratio of exchangeable fraction to the total, the value of B is equal to 1-A, and *∂* is the toxicity coefficient corresponding to different percentage of exchangeable fractions. Table S5 shows the values of *∂* obtained according to the values of RAC and expert consultation.

#### *2.6. Statistical Analysis*

A geographic information system (GIS) was used to analyze the spatial distribution of heavy metals in sediment interstitial water and sediments from the WLSH Lake. The inverse distance (IDW) method was applied to map the spatial distribution of pollutants based on ArcGIS 10.2 software (ESRI, Inc., Redlands, CA, USA).

#### **3. Results and Discussion**

#### *3.1. Heavy Metals in the Sediment Interstitial Water*

The material exchange across the sediment–water interface is mainly through sediment interstitial water, and the growth environment of the benthos and its toxicity is closely related to sediment interstitial water. Therefore, sediment interstitial water plays an important role in the geochemical cycling of heavy metals in lake systems [31,32]. In this study, the concentrations of Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb in the sediment interstitial water of WLSH Lake were investigated and analyzed. The spatial distribution characteristics of heavy metals in the sediment interstitial water are illustrated in Figure 2.

As shown in Figure 2, there was a large difference in the spatial distribution of each metal in the sediment interstitial water of WLSH Lake. The concentrations of heavy metals in the western bank were generally higher than these in the eastern bank. The eight heavy metals could be roughly classified into two types. The first was those whose concentrations were generally higher in the estuary of the lake than in the center of the lake, with the highest value occurring in the estuary of the Ninth Drain in the west lake. Heavy metals of this type included Cr, Cu, Zn, and Hg. Concentrations of the second type, which included Ni, As, Cd, and Pb, were higher in the middle of the lake.

This study evaluated the toxicity of heavy metals in interstitial water of WLSH Lake to the aquatic ecosystem based on the National Recommended Water Quality Criteria by EPA [33]. This criteria includes criterion continuous concentration (CCC) and criterion maximum concentration (CMC). Chronic toxicity may occur to aquatic ecosystem if the concentration of a heavy metal in the water exceeds its corresponding CCC value. Acute toxicity may occur when the concentration exceeds its corresponding CMC value. The concentration of heavy metals in the sediment interstitial water of WLSH Lake and EPA water quality standard are shown in Table 1.

**Figure 2.** Spatial distributions of Cr (**a**), Ni (**b**), Cu (**c**), Zn (**d**), As (**e**), Cd (**f**), Hg (**g**), and Pb (**h**) in sediment interstitial water from the Wuliangsuhai Lake.

**Table 1.** Descriptive statistics of heavy metal concentrations in the sediment interstitial water of Wuliangsuhai Lake and EPA water quality criteria (µg/L).


<sup>1</sup> SD: standard deviation. <sup>2</sup> CMC: criterion maximum concentration. <sup>3</sup> CCC: criterion continuous concentration.

Comparing the concentrations of heavy metals in the sediment interstitial water with the standard concentrations, the results showed that the average concentrations of Ni, Zn, As, and Cd were less than the corresponding CCC values, that is, they would not cause toxicity to the aquatic ecosystem. The average concentration of Cu was higher than that of CCC-Cu, indicating that Cu in interstitial water of WLSH Lake may cause chronic toxicity to the benthos, which should be paid attention to. The average concentrations of Cr and Hg in the middle of the lake were less than the CCC value, while those in estuary were higher than the CMC, indicating that Cr and Hg in the estuary may cause acute toxicity to the aquatic ecosystem.

#### *3.2. Heavy Metal in the Surface Sediments*

### 3.2.1. Contents of Heavy Metals in the Surface Sediments

The descriptive statistics of the heavy metals concentrations in the surface sediments of WLSH Lake as well as the background values (the soil background values of Inner Mongolia from CNEMC, 1990), and the corresponding values based on SQGs are summarized in Table 2. According to the background values of WLSH Lake, they indicated that the concentrations of heavy metals except Cr and Zn in the surface sediments of this study were higher than their corresponding background values. Cd and Hg, in particular, had average values 10.19 and 4.25 times higher, respectively, than their background values.

**Table 2.** Descriptive statistics of heavy metal concentrations in the surface sediments of Wuliangsuhai Lake (mg/kg).


<sup>1</sup> TEL: threshold effect level. <sup>2</sup> PEL: probable effect level. <sup>3</sup> BV: background value.

The average concentrations of Cr, Ni, Cu, Zn, Cd, Hg, and Pb were lower than their corresponding TEL values. Furthermore, the maximum concentrations of Cr, Cu, As, Hg, and Pb exceeded the corresponding TEL values by 1.04, 2.07, 1.76, 2.83, and 1.51 times, respectively. In addition, the maximum concentrations of all the heavy metals were lower than their corresponding PEL values. Thus the adverse biological effects in some regions were probable but would not be frequent.

Comparing the heavy metal concentrations in the surface sediments of WLSH Lake with the published literature of other freshwater lakes in China and abroad (Table 3), revealed that compared with algae lakes (e.g., Taihu Lake and Chaohu Lake), the heavy metal concentrations in WLSH Lake were not high. Nevertheless, compared with other lakes in the Neimenggu-Xinjiang Plateau (e.g., Hulun Lake), the average concentrations of Cd and Hg in WLSH Lake were relatively high. In general, the pollution degrees of heavy metals in sediments from the lakes in economically developed areas (e.g., Taihu Lake (Jiangsu Province) and Dongting Lake (Hunan Province)) were higher than those in economically developing areas in China. Obviously, the heavy metal concentrations in lake sediments were related to the intensity of human activities.

**Table 3.** Comparison the concentrations of heavy metals in the surface sediments of Wuliangsuhai Lake and other freshwater lakes in China and abroad.


NA: not applicable.

3.2.2. Chemical Fractions of Heavy Metals in the Surface Sediments

The heavy metals in sediments exist in different fractions, mainly including the exchangeable fraction, the reducible fraction, the oxidizable fraction, and the residual fraction. Among these, the first three fractions are referred to as extractable fractions. They can be used by organisms and are potentially harmful to the ecological environment.

Figure 3 illustrates the percentages of chemical fractions for each heavy metal in the sediment from WLSH Lake. As shown in Figure 3, Ni, Cu, Cd, and Hg were mainly in the extractable fraction. The percentage of Cd in the exchangeable fraction ranged from 28.69 to 50.83%, and the lakeshore in the northwest had relatively high levels. The percentages of the reducible and oxidizable fractions ranged from 17.45 to 38.48% and 6.76 to 14.61%, respectively. In the extractable fraction, Hg was mainly in the oxidizable fraction, and ranged from 21.75 to 33.67%, followed by the reducible fraction, with the average value of 13.55%. Moreover, Ni and Cu were mainly in the reducible fraction, and the amounts of the acid-soluble and reducible fractions were relatively lower.

**Figure 3.** Percentages of the chemical fractions for (**a**) Cr, (**b**) Ni, (**c**) Cu, (**d**) Zn, (**e**) As, (**f**) Cd, (**g**) Hg, and (**h**) Pb in 23 sediment samples from Wuliangsuhai Lake.

The residual fraction is extremely stable and hardly used by organisms [25]. The percentages for chemical fractions of Cr, Zn, As, and Pb in the surface sediments of WLSH Lake were mainly in the residual fraction, especially for As and Pb, which were 94.05% and 81.57%, respectively. In contrast, to the residual fraction, the greater the proportion of the extractable fraction of the heavy metals, the greater their bioavailability, the more likely they are to be released to cause secondary pollution, and the more likely to pose a potential threat to the environment. In this study, the potential bioavailability of the eight metals was ranked as Cd > Cu > Ni > Hg > Cr > Zn > Pb > As.

#### 3.2.3. Pollution Assessment of Heavy Metals in the Surface Sediments

After normalization with the element Al, the EF value of each heavy metal was calculated by comparing the measured concentration to its corresponding background value, and the results are shown in Table 4. The average EF value of Cd was 3.51, showing that it reached moderate enrichment; the average EF value of Hg was 1.94, showing that Hg was in minimal enrichment, however, the EF value of Hg at S7 was as high as 10.62, reaching significant enrichment. The EF values at some sampling sites of Ni, Cu, and As approximated 1, indicating that the metals were considered to be partly contaminated. The EF values of Pb, Cr, and Ni in all sampling sites were lower than 1, indicating that these metals may not be contaminated by anthropogenic sources. The average enrichment degree decreased in the order of Cd > Hg > Pb > As > Cu > Ni > Cr > Zn.

**Table 4.** The EF value of each heavy metal in the surface sediments of Wuliangsuhai Lake.


Figure 4a–h illustrates the spatial distribution characteristics of *Igeo* values of each heavy metal, and Figure 4i was the violin plot of *Igeo*. As shown in Figure 4, the concentrations of Ni, As, and Hg showed similar spatial distribution. The highest concentrations of those metals were near the lake entrance in the northeastern lake owning to the large amount of drainage water from the Hetao irrigation area and industrial wastewater and domestic sewage from upstream, all discharged into the lake through lake entrance. In addition, dense emerged plants slowed the water flow. As a result, heavy metals in water were absorbed by sediments and accumulated. Moreover, the sampling sites with the highest Cd and Pb contents were located in the estuary near the Total Drain. This may have been because that the mobility of Cd and Pb is strong, and long-term closure of the water gate makes the sedimentary environment relatively stable, which is conductive to the accumulation of Cd and Pb. The high value area of heavy metals except Hg was in the northern lake. The reason for this is that mixed sewage is discharged from the northern lake, and then is diverted to the south after being blocked by the reeds. During the diversion, it is mixed with the waste water from the Total Drain, which makes a high content of heavy metals in the northern part of WLSH Lake. As shown in Figure 4i, the mean *Igeo* values of the heavy metals ranked as Cd > Hg > Pb > Ni > As > Cu > Cr > Zn, and the mean *Igeo* values of Ni, As, Cu, Cr, and Zn were all less than 0. According to *Igeo* classification, the mean *Igeo* of sediments in WLSH Lake indicated that it was unpolluted by Cr, Ni, Cu, Zn, or As, unpolluted to moderately polluted by Pb (0.10), moderately polluted by Hg (1.08), and moderately to strongly polluted by Cd (2.73). The maximum value of *Igeo*-Cd was 3.37, showing that it was strongly polluted. The *Igeo* values of Cr and Zn in all sampling sites were less than 0, indicating that the heavy metal pollution in the surface sediments of WLSH Lake was mainly caused by other metals. The sampling sites with *Igeo* values greater than 0 for Ni, Cu, and As accounted for 17.4%, 21.7%, and 30.4% respectively.

**Figure 4.** *Igeo* assessment results of heavy metals in the surface sediments (**a**) Cr, (**b**) Ni, (**c**) Cu, (**d**) Zn, (**e**) As, (**f**) Cd, (**g**) Hg, and (**h**) Pb from Wuliangsuhai Lake. (**i**) violin plot of *Igeo*.

The potential ecological risks of heavy metals in the surface sediments of WLSH Lake were calculated based on Equation (3). Figure 5a shows the *E i <sup>r</sup>* value of each metal, and Figure 5b illustrates the spatial distribution characteristics of RI. The mean *E i <sup>r</sup>* value were

ordered as follows: Cd > Hg > As > Pb > Ni > Cu > Cr > Zn. The *E i <sup>r</sup>* values of Cr, Ni, Cu, As, and Pb were less than 40 at all sampling sites, indicating that these metals were at low risk. The mean *E i <sup>r</sup>* values of Cd and Hg were 307.89 and 171.42, corresponding to high risk. The maximum *E i <sup>r</sup>* values of Cd and Hg were 519.20 and 505.51, respectively, corresponding to very high risk. With respect to Cd, 62.5% of the sampling sites were at considerable risk, and the rest were at high to very high risk. The average RI value of the surface sediments in WLSH Lake was 516.34. According to the classification of RI, the surface sediments of WLSH Lake posed a considerable risk. Due to the high enrichment and strong toxicity, Cd and Hg had the highest contribution rate to RI. The RI values at S1, S2, S7, and S15 sampling sites were all higher than 600, corresponding to very high risk. These sampling sites were located in the central lake. Overall, the ecological risks in the central lake were higher than these in lakeshore. The spatial distribution characteristics of RI were similar to those of Zn, Cd, Hg, and Pb.

The ΣTUs and TRI distributions and values of individual metals are shown in Figure 5e–h. Previous studies have shown that when ΣTUs < 4, the sediment will not cause the death of amphipod *C. volutator* (Pallas), nor will it cause the acute toxicity to *V. fischeri*. However, when ΣTUs is higher than 4, significant toxicity will occur [19,43]. Therefore, ΣTUs of 4 is the vital threshold to identify whether heavy metals in sediment will have toxic effects. However, there was no site with ΣTUs values higher than 4.0. The results of ΣTUs showed that the potential eco-toxicity of Ni, As, and Cr was higher than that of other metals, but this is not consistent with the actual pollution situation. This may because of the high PEL value, which cause misjudgment of potential eco-toxicity. Thus, ΣTUs is not suitable for WLSH Lake. Based on TRI results, TRI values ranged from 2.96 to 6.34, with the highest value in the center lake and the lowest in the midwestern lakeshore. Spatially, the TRI of the study area increased from the west to the middle, and then decreased from the middle to the east. This method takes the PEL and TEL into consideration, which can provide more reference information for actual pollution estimation.

The exchangeable fraction of heavy metals in sediments is the most sensitive to environmental changes. The exchangeable fraction is easily released under neutral or acidic conditions, due to its weak bonding force, so it has the ability of rapid desorption and high bioavailability. As shown in Figure 5g, the bioavailability of heavy metals in this study varied greatly, and the bioavailability of each heavy metal had spatial differences. In general, the heavy metals in the surface sediments of WLSH Lake can be classified into three groups: the RAC values of Cr, Zn, As, and Pb in all sampling sites were less than 10%, corresponding to no to low risk; the RAC values of Cu, Ni, and Hg were less than 30%, corresponding to low to medium risk; the RAC values of Cd ranged from 28.69% to 50.83%, with an average value of 35.50%, indicating that Cd in the surface sediments of WLSH Lake was at high risk, while Cd in S4 sampling site was at very high risk. The results showed that a high proportion of Cd in the surface sediments existed in the fraction of active adsorption. The assessment results were similar to those based on the total contents, but there were some differences, indicating that the chemical fractions had an impact on the ecological risk of sediments.

In this study, MRI, which is a modified index of RI, takes the toxicity and concentration of F1 fraction into consideration. Compared with RI, in some cases, MRI can better assess the actual ecological risk of sediments. As shown in Figure 5h, the MRI values of 23 sampling sites were all higher than the corresponding RI values. Both RI and MRI of S1, S2, S7, S12, and S15 were at very high risk. Moreover, RI of S4, S5, and S18 were at high risk, while the MRI values of these sampling sites were at very high risk. Compared with other sampling sites, MRI values were significantly higher than RI values at heavily polluted sites, showing that the percentage of F1 has a great influence on the MRI values. Since most of exchangeable heavy metals were mainly from anthropogenic sources, MRI can be considered to be more suitable for assessing the ecological risk of heavy metals in sediments which are under the influence of human activities.

**Figure 5.** Risk assessment by RI (**a**,**b**), ΣTUs (**c**,**d**), TRI (**e**,**f**), RAC (**g**), and MRI (**h**).

The above assessment results indicated that the heavy metals in the surface sediments of WLSH Lake were at considerable risk, with the pollution degree in the central part of northern lake being the highest. The enrichment degree, potential ecological risk, and biological toxicity of most heavy metals were higher in the central part of northern lake, while the southern lakeshore region was less polluted. Compared with other heavy metals, Cd was the primary pollutant in the surface sediments of WLSH Lake. The average concentration of Cd in the surface sediments of WLSH Lake was 10.1 times that of the corresponding value, reaching significant enrichment in some regions. This may be because these is a large amount of farmland around WLSH Lake, and the use of phosphate fertilizers causes the increase of Cd contents in the sediments. In addition, the plastic films used in greenhouses also contain Cd, further increasing the Cd loading in the sediments. Moreover, both industrial wastewater and domestic sewage are drained into the lake, leading a high risk of heavy metals.

#### *3.3. Heavy Metals in the Sediment Cores*

#### 3.3.1. Contents of Heavy Metals in the Surface Sediments

The contents of heavy metals in eight sediment cores are shown in Figure 6. The contents of heavy metals in sediment cores decreased with the depth. When the depth of the sediment reached 40 cm, the contents tended to gradually stabilize. Therefore, this study regarded the sediment layers blow 45 cm as the "unpolluted layer of the lake". Meanwhile, the sedimentation rate of the WLSH Lake is 9–13 mm/a [44] (The effect of lake disturbance on sedimentation rate was not considered). Based on this, the sediment profile of 40 cm corresponded to the 1970s. In this study, the "unpolluted sediment layer of the lake" was 45–80 cm, which can be considered as the "background layer" before industrialization.

**Figure 6.** Profile of heavy metals from sediment cores in Wuliangsuhai Lake: (**a**) Cr, (**b**) Ni, (**c**) Cu, (**d**) Zn, (**e**) As, (**f**) Cd, (**g**) Hg, (**h**) Pb.

#### 3.3.2. Pollution Assessment of Heavy Metals in Sediment Cores

Figure 7 illustrates the average values of RI, ΣTUs, and TRI of eight sediment cores. As shown in Figure 7a, the primary pollutant of sediment cores was Cd, and the contribution rate of Cd to RI in each layer was the highest. The RI values in the surface sediments (0–20 cm) were much higher than those of bottom sediments (depth > 20 cm). The RI value of sediments at the depth of 0–5 cm was 2.69 times that of the sediments at the depth of 75–80 cm, indicating that the surface sediments were greatly disturbed by human activities. It can be seen from Figure 7b,c that the vertical distribution characteristics of ΣTUs and TRI were similar. The values of ΣTUs and TRI of sediments at depth of 0–20 cm were greater

than those at depth > 20 cm. However, the variation trend of TRI with depth was more obvious than that of ΣTUs. Ni had the highest contribution rate to ΣTUs and TRI, and the contribution rates at the bottom sediments (depth > 20 cm) were higher than those at the surface sediments (0–20 cm), which also demonstrated that the Ni contents had little differences in each layer. In the surface sediments, the contribution rates of Cd to ΣTUs and TRI were 6.26% and 9.38%, respectively, showing that TRI was more sensitive to the changes in metal contents. Combined with the three assessment results, it was indicated that the ecological risk and biological toxicity at the surface sediments (0–20 cm) were higher than those at the bottom sediments. As the depth increased, the ecological risk and biological toxicity gradually decreased.

**Figure 7.** The assessment results of (**a**) RI, (**b**) ΣTUs, and (**c**) TRI of sediment cores from Wuliangsuhai Lake.

## **4. Conclusions**

In this work, eight heavy metals (Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb) in sediment interstitial water, surface sediments, and sediment cores from Wuliangsuhai Lake were investigated. The chemical fractions of heavy metals were obtained using the BCR extraction procedure. Then, the pollution assessment of these heavy metals was studied using enrichment factor, geo-accumulation index, potential ecological risk, SQGs, and modified potential ecological risk based on risk assessment code. Comparing the concentrations of heavy metals in the sediment interstitial water with the National Recommended Water Quality Criteria by EPA, indicated that Ni, Zn, As, and Cd would not be toxic to aquatic ecosystems, however, Hg and Cr in some regions may cause acute toxicity to the benthos. The mean concentrations of Ni, Cu, As, Cd, Hg, and Pb in the surface sediment exceeded the geochemical background values. Cr, Zn, Cd, and Pb exhibited similar spatial distributions in concentrations, with the highest value appearing at the estuary near the Total Drain. In contrast, the highest concentration values of Ni, As, and Hg were at the northeast of lake. Based on the pollution assessment results, Cd is the primary pollutants in WLSH Lake, which deserves attention. The bioavailability and mobility of heavy metals followed a decreasing order of Cd > Ni > Cu > Hg > Zn > Pb > As > Cr. The concentrations and ecological risk of heavy metals in sediment decreased with the increase in depth.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/w14081264/s1, Text S1: Steps of BCR, Table S1: Results of the recovery test of standard samples (GSD7), Table S2: Classification of EF, Table S3: Classification of *Igeo*, Table S4: Indices and grades of potential ecological risk, Table S5: Classification of RAC and values of *∂*.

**Author Contributions:** Conceptualization, methodology, and validation, Q.Z.; investigation, J.L. and H.J.; data curation, F.F.; writing—original draft preparation and review and editing, J.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China, No. 51809110, the Major Science and Technology Projects for Public Welfare of Henan Province, No. 201300311500, the Science and Technology project of Henan Province, No. 212102311147, the Key Project of Water Resources Science and Technology of Henan Province, No. GG201938 and No.GG201930, the Kaifeng Yellow River Basin Ecological Protection and High-quality Development Innovation Special Program, No. 2019012, and the Think Tank Research Projects of Zhengzhou Collaborative Innovation Major Funding (Zhengzhou University), grant No. 2019ZZXT01.

**Conflicts of Interest:** The authors declare no conflict of interest.
