Heavy metals pollution and potential ecological risk assessment in farmland soils from typical mining area: a case study

ABSTRACT The research aimed to investigate HMS, utilizing the Pearson correlation coefficient for speciation distribution analysis, PCA for assessing pollution characteristics and identifying sources, the Muller index to evaluate ecological risk level, and the Hakanson potential ecological risk index to determine the order of risk from heavy metals. The topsoil near SA was collected, and the contents of seven kinds of HMS, As, Cd, Pb, Zn, Ni, Cu and Cr were determined, so as to evaluate the types of high-risk heavy metal pollution further accurately. The research recorded valuable data showing that the concentration values of all seven HMS in the investigated area exceeded prescribed agricultural soil contamination limits. The concentrations of As, Cd, and Pb were found to be 8.30, 46.20, and 6.08 times higher than the screening values in Hunan Province, respectively. In the GYB sampling area, the coefficient of variation (CV) values for Cu, Pb, As, Zn, and Cd are all between 0.50 and 1.00. Notably, the CV value for Cd reaches 0.82, indicating a significant variation. Significant correlations were found between Cd and Zn (Cd-Zn), Pb and Zn (Pb-Zn), Ni and Cr (Ni-Cr) in the tested soils. The ecological risk index (${\rm{E}}_{\rm{r}}^{\rm{i}}$Eri) results showed that Cd was the primary pollutant in the study area, with the potential ecological hazards in the tested soils ranked as Cd>As>Pb>Cu>Zn>Ni>Cr. Combining both evaluation methods, the study area’s potential ecological risk order is SZY>GYB>CTL.


Introduction
Mining activities have had a significant impact on soil pollution worldwide, particularly in regions with extensive mining operations.Heavy metal pollution is one of the prominent environmental concerns associated with mining activities (Luo et al. 2022;Zhang, Song, and Zhou 2023).In China, certain regions have experienced severe soil pollution, especially in cultivated land and industrial and mining wastelands.These areas, including non-ferrous metal mining regions, have shown varying degrees of heavy metal contamination, with lead (Pb), cadmium (Cd), copper (Cu), arsenic (As), and chromium (Cr) being the most commonly observed pollutants (Fei et al. 2022;Sani et al. 2017).Understanding the impact and biological toxicity of heavy metals on the environment is crucial, as it depends not only on their total concentration but also on their speciation and distribution within the soil (Adio et al. 2017;Liu et al. 2022).The transformation of heavy metal forms in farmland soil can lead to environmental pollution and affect their toxicity and migration processes in nature.Therefore, a comprehensive evaluation of heavy metal pollution in mining areas based on total concentrations and morphological distribution characteristics is essential.
An important prerequisite for the treatment and remediation of contaminated soil is to scientifically investigate and evaluate the current situation of soil pollution.At present, several methods have been developed to assess the ecological risk of heavy metal pollution in soil.These methods include the geoaccumulation index method, enrichment coefficient method, potential ecological hazard index method, pollution load index method, RAC risk assessment method, secondary phase and primary phase distribution ratio method, Nemerow comprehensive index method, and health risk assessment method (Kong, Ying, and Lu 2022).However, each method has its limitations, and their combined use is often necessary for a comprehensive evaluation of heavy metal pollution.For example, the form based the toxicity characteristic leaching procedure method (TCLP) is difficult to truly reflect the real situation of heavy metal pollution in soil (Yang et al. 2021).The Nemero composite index method is not applicable to the presence of pollutants with large toxicity differences (Doabi et al. 2018).The whiteness equation of gray clustering method covers a narrow evaluation range, and the accuracy of evaluation results exhibit a low value (Yu et al. 2018).The evaluation method based on GIS and geo-statistics is not suitable for the case of poor correlation of regional variables (Kheir et al. 2014).Therefore, in order to comprehensively evaluate the soil environmental quality of the farmland in the study area, based on the actual situation of the study area, this work selects the potential ecological risk index method (RI) and the geo-accumulation index method (I geo ).Among them, the geo-accumulation index (I geo ) evaluates the level of soil contamination, and the potential ecological risk (RI) evaluates the level of individual and general ecological risk from heavy metals based on the soil contamination factor and the toxicity of each metal.
In this study, we focus on the potential ecological risk index method (RI) and the geo-accumulation index method (I geo ) to evaluate heavy metal pollution in farmland soil in a specific study area.The RI method considers the ecological impact of individual heavy metals, their toxicological levels, and the soil background values in different geographical environments (Li et al. 2018;Zang et al. 2017;Zhong et al. 2020).It provides a detailed assessment of the degree of harm and potential risks associated with heavy metals.The I geo method not only considers natural variation characteristics but also accounts for anthropogenic pollution factors and changes in environmental geochemical background values due to natural processes (Feng et al. 2019;Hakanson 1980).By utilizing these two methods, we aim to comprehensively evaluate the soil environmental quality and heavy metal pollution in the study area.
Although many scholars have done many investigations on the distribution and ecological risk assessment of HMS in SA and have also done some remediation research on the heavy metals in the soil, most of the above studies were conducted 5-10 years ago.In recent years, SA has continued to expand the scale of mining and smelting.These industrial activities are an important source of heavy metals in farmland soil.The "three wastes" produced by mining will enter the soil through surface runoff, atmospheric sedimentation, rainfall leaching and other effects, causing heavy metal pollution in farmland soil around the mine and its watershed.Therefore, there are still many problems in the research of the pollution characteristics and ecological risks associated with heavy metals.In view of this, it mainly includes the following aspects.First, it should be emphasized that due to the continuous demand of industrial development for metal materials, mining and ore smelting activities are more frequent than before.The environmental pollution in industrial and mining areas has also shown an upward trend in recent years, especially the sharp increase in the content of heavy metals in soil, which not only pollutes the environment but also affects the health of residents in mining areas.Therefore, it is of great significance to monitor and scientifically evaluate the heavy metals in the soil of industrial and mining areas.However, there are always differences on whether it is necessary to carry out continuous observation and research because these heavy metal pollution changes dynamically and has a cumulative effect.Secondly, there are many evaluation methods for heavy metals (Wang et al. 2021).At present, the commonly used methods include comprehensive evaluation method, pollution index evaluation method, clustering method, factor analysis method and other evaluation methods.The above methods can comprehensively evaluate the pollution degree of the test area, but they cannot separate the artificial anomaly from the natural anomaly and judge the artificial pollution of heavy metals in the supergene process.These studies have their own characteristics and different scope of application.At present, these studies have not been classified and systematized.Therefore, it is very timely and necessary to compare the land accumulation index evaluation method and ecological hazard index evaluation method used in the evaluation of soil heavy metals in SA, Hunan Province, China.Therefore, this research presents three main innovation points: the comprehensive evaluation of heavy metal pollution, the updated research approach on contamination, and the comparison of evaluation methods.The findings not only enhance our understanding of heavy metal pollution dynamics but also offer critical information for implementing sustainable environmental practices in mining areas.
In this study, to obtain the impact of mining and processing activities in SA on the soil in the surrounding mining areas, and seven heavy metals cadmium (Cd), chromium (Cr), copper (Cu), zinc (Zn), lead (Pb), arsenic (As) and nickel (Ni) in research area were selected as the research objects.Simultaneously, this work conducted an analysis to determine the types of major heavy metals present in the soil of the study area, along with their associated pollution levels.To evaluate the extent of heavy metal pollution in the soil and assess potential ecological risks, this work utilized the geo-accumulation index (I geo ) and the potential ecological risk index (RI).These indices helped us determine the pollution levels of heavy metals in the soil and assess the potential ecological risks within the study area.Both of these methods introduce the background value as the standard to normalize the content of heavy metals in soil.The results not only help to understand the natural change characteristics of the distribution of heavy metals but also can identify the impact of human activities on the environment, so that the pollution of heavy metals in soil can be better evaluated.

Materials and methods
The study focused on assessing soil pollution in the Shizhuyuan lead-zinc mining area in Hunan Province.Soil samples were collected from three sampling areas, including three under-investigation areas near the National SA Park.Heavy metal content in the soil was analyzed using a rigorous digestion process and an ICP-OES instrument.The modified BCR extraction method was utilized to assess different forms of heavy metals.Statistical analysis using PCA was conducted to identify factors influencing soil element content.The Geo-accumulation index (I geo ) and potential ecological risk assessment (RI) were employed to evaluate pollution levels and ecological risks associated with heavy metals.The study employed a comprehensive approach involving sampling, chemical analysis, statistical analysis, and pollution assessment to investigate soil pollution in the mining area.

The study area
Shizhuyuan lead-zinc mining area in Hunan Province is located at 112°13′-114°14′ E, 24°53′-26°50′ N. It belongs to the subtropical monsoon humid climate area, which is suitable for plant growth.The annual average temperature is 17.4°C.There are many types of non-ferrous metal resources and huge reserves in SA.A total of 110 types of minerals have been discovered, and more than 70 kinds of 7 types have been proven reserves, with a potential value of more than 260 billion, and rich reserves of tungsten, tin, molybdenum, bismuth, fluorine, lead, zinc and silver.It is a world-famous polymetallic deposit and it is known as the "World Nonferrous Metal Museum."While mining mineral resources brings economic benefits, it also destroys the surrounding ecological environment.The under-investigation area was affected by mining and smelting activities, and the soil showed varying degrees of pollution.

Sample collection
Fig. S1 illustrates that the map of sampling points in the under-investigation area.Three sampling areas were selected in this study, which are, respectively, arranged around SA. CTL is in the 6.2 km west of National SA Park, SZY point is in the center of National SA Park, and GYB is in the north of SA Park to 8 km.According to Chinese national standard (GB 15,618-2008, Environmental quality standards for soils), the soil in the above three areas was collected by random sampling method, and the GPS system is used to determine the location of sampling points in the sampling process.In the study area, soil samples were collected at each sampling point from a depth of 0-20 cm, resulting in a total of 55 samples.All roots, gravel, and other impurities were removed from the air-dried soil samples, and they were sieved through a 2 mm mesh.

Sample preparation and analysis
The soil was digested with HF, HClO 4 and HNO 3 solutions in order to determine the heavy metal content in the soil (Shen et al. 2020).In the soil analysis process, soil samples weighing 0.50 g and previously dried at 105°C were placed in a 30 mL polytetrafluoroethylene crucible.Distilled water was added to moisten the soil, followed by the addition of 10 mL of HF solution and 5 mL of HClO 4 -HNO 3 mixture (1:1 volume ratio).The mixture was digested using an electric heating plate until HClO 4 emitted a large amount of white smoke.Subsequently, another 5 mL of HClO 4 -HNO 3 mixture was added, and digestion continued until HClO 4 emitted a large amount of smoke, after which it was allowed to dry.To complete the digestion process, 5 mL of concentrated HNO 3 was added until dry.Then, 5 mL of HCl solution was added and heated for dissolution, and the volume was adjusted to 25 mL.The detection of heavy metal ion concentration in this work is completed via an inductively coupled plasma atomic emission spectrometer (ICP-OES, USA).The Table S1 for accuracy, precision, LOD and LOW of each element information was placed in the supporting information.We first calibrate the equipment with standard solutions to ensure the accuracy of the detection results.Subsequently, batch testing is carried out, and the testing equipment is always in a stable state, so the quality accuracy of the testing results can reach 99.99%.All agent used in the process of experiment was analytical pure.Soil samples were digested by microwave digester to prepare for further test of elements.The test method of heavy metals content reference the book-GB 15,618-1995.A 5 g sample was weighed and transferred into a 50 mL centrifuge tube, followed by the addition of 25 mL of deionized water.The tube was placed on a shaker and shaken at a speed of 120 r/min for 5 min.Subsequently, the suspension solution was allowed to stand for 30 min.To measure the pH value of the suspension, a pH meter (Lei Magnetic PHS-3C) was used.Prior to the pH measurement, the pH meter was calibrated using buffer solutions with pH values of 4.01, 6.86, and 9.18.
The modified BCR extraction method (Alan and Kara 2019b;Qureshi Arsalan et al. 2020) is used to extract various forms of heavy metals step by step (acid-soluble fraction (F1), reducible fraction (F2), oxidizable fraction (F3), and Residual fraction (F4)).To reduce the experimental error, the determination of the occurrence state of HMS was carried out three times.

Statistical analysis
One-way ANOVA was employed to demonstrate the variations in heavy metal concentrations in soil among the study areas.Principal component analysis (PCA) and Pearson analysis were utilized to identify the potential sources of heavy metal-contaminated soil.PCA method can judge whether the change of soil element content is affected by human factors according to the relationship between comprehensive variables and potential influencing factors, without detailed morphological analysis of elements and comparison with historical data (Yang et al. 2017;Zeng et al. 2022).According to the degree of correlation between each index, it is transformed into a few comprehensive indicators to reflect the overall characteristics, which is often used in the study of the source of elements in various media.In this dataset, heavy metals were identified as objects (total = 7).To ensure that the different variables in the model are independent of the units used, we automatically scale the columns of the data matrix by using a z-score normalization method so that each variable produces a mean of zero-unit variance.In order to associate a set of variables with specific factors in the interpretation process, we consider important loads with priority.
PCA roughly includes the following basic steps: ① standardization of raw data; ② Calculate the correlation coefficient matrix of each index; ③ Calculate eigenvalues and eigenvectors; ④ Calculate the contribution rate and cumulative contribution rate of eigenvalues, and extract the principal components.

Geo-accumulation index (I geo )
The I geo can not only evaluate the pollution degree of HMS and sediment but also further identify the main heavy metal pollutants, which is widely utilized in evaluation of HMS (Negahban et al. 2020).
The I geo takes into account the background value of the heavy metal content of the local soil, which is affected by natural geology such as sedimentary diagenesis, and fully pays attention to the impact of human production and life on HMS pollution.Therefore, I geo can be utilized to describe the impact of industrial production on the natural environment and the change of HMS distribution.Mainly considering the enrichment of elements, I geo has been utilized to describe the evaluation of HMS.The evaluation equation as follows: Where, C n represents the content of a certain HMS; B n represents the reference background value of HMS.The classification standards of I geo are shown in Table 1.

Potential ecological risk assessment (RI)
RI is an approach used to evaluate heavy metal pollution in soil or sediment by considering the sedimentological properties of heavy metals and their environmental behavior characteristics (Hakanson 1980).This method takes into account the influence of various pollutants presents in a specific environment, as well as the cumulative impact of multiple pollutants.It quantitatively assesses the potential degree of harm and combines ecological, environmental, and toxicological effects of heavy metals.It achieves this through the use of a comparable and equivalent attribute index grading method for evaluation.
① Single heavy metal pollution index: Where, C i f represents the pollution index of a certain HMS; C i represents the measured concentration of a certain HMS; C i n represents the evaluation reference value of a certain HMS.
Where, T i r represents the toxicity response coefficient of a certain HMS.According to the nitrogen content and burning amount of sediment; Hakanson was determined as follows: As = 10, Cd = 30, Pb=Cu = 5, Ni=Cr = 2, Zn = 1.As shown in Table 2 and Figure 1, the soil in the under-investigation area was neutral and acidic, and the pH value tends to normal distribution.The content of HMS experienced a high value, and  1.This suggests that other factors beyond pH alone may play a significant role in influencing the levels of heavy metal contamination in the soil.Further investigation and analysis are required to identify and understand these additional contributing factors.Table 1 Total averages of soil metals and comparisons with background values (n = 55, mg/kg).Therefore, the accumulation degree of As, Cd and Pb in soil exhibit significant differences.HMS will be enriched in crops and human body, thus endangering human health.In view of their potential harm to humans and the ecological environment, soils with the highest As, Cd, and Pb contents are not suitable for reuse in agricultural production activities, and more measures should be taken to reduce the content of heavy metals in soils.In the under-investigation area, local farmers no longer plant cash crops, but planted wheatgrass (Chang et al. 2022).This indicates that certain areas within the study site are more contaminated than others, posing potential risks to human health and the environment.However, compared with the previous the Pb content range of 305.28-1 061.54 mg/kg, the Zn content range of 612.95-1 064.21 mg/kg, the Cd content range of 4.11-9.05mg/kg, and the As content range of 346.77-533.49mg/kg report by the published literature (Liu, Probst, and Liao 2005).The important finding is that the results obtained in this study are consistent with those in published articles, indicating that the degree of heavy metal pollution of agricultural soil in this region has not decreased with time (Yan et al. 2022).By comparing the results with previous studies, the research further strengthens this observation, suggesting that measures taken so far have been ineffective in reducing heavy metal content in the soil.This finding emphasizes the urgent need for additional remediation measures and pollution control strategies to mitigate the adverse effects of heavy metal contamination.

Concentrations of HMS
As shown in Table 1, compared with other HMS, the content of Cd and Pb in the soil of SZY experienced a higher value, while the content of As in CTL sampling point far away from the mining area exhibit a higher value, and other elements were lower than that of SZY sampling point.The enrichment degree of HMS shows obvious signs and is unevenly distributed, and the enrichment of heavy metals is related to mineral mining and smelting activities.The smelting process will lead to a large amount of Cd and Pb being discharged into the environment, which is consistent with previous studies (Qiao et al. 2022).This spatial variation can be attributed to the influence of mining and smelting activities, particularly the release of Cd and Pb during the smelting process.These findings contribute to a better understanding of the sources and distribution patterns of heavy metals in the study area and can guide targeted remediation efforts.
The average degree of variation between sampling points in a soil sample population is usually expressed by the coefficient of variation (CV).It is generally considered that CV value <0.20 is weak variation, 0.20-0.50 is moderately strong variation, 0.50-1.00 is strong variation, and CV value ≥1.00 is abnormally strong variation (Nezhad, Tabatabaii, and Gholami 2015; Pan et al. 2016).The larger CV value, the more uneven the distribution of elements in the soil, which means that it is more affected by human activities.According to Table 1, the CV values of Cr and Ni in the three sampling areas are all less than 0.25, which is a weak variation.This indicated that Cr and Ni in the soils of the three sampling areas were less affected by human activities.It should be pointed out that the CV values of Pb and Zn in the CTL sampling area were less than 0.25, which also belonged to weak variation.Only the CV values of As, Cu, and Cd were between 0.26 and 0.50, which belonged to moderate variation.However, the CV values of Pb and Cd ranged from 0.50 to 1.00, indicating strong variation.The CV values of Cu, Pb, As, Zn, and Cd in the GYB sampling area are all between 0.50 and 1.00, and the CV value of Cd reaches 0.82, which is a strong variation.The CV values of heavy metals in the GYB sampling area were higher than those in the other two sampling areas, which indicated that the changes of heavy metal content in the surface soil of the three sampling areas in the study area were not only affected by the geological background but also affected by human activities.The soil heavy metal content in the GYB sampling area is more affected by mining and smelting activities.

The speciation of HMS
The migration and transformation of HMS and their impact on ecological effects and the environment are not only related to the content of heavy metals in soil but also have a great relationship with the form of HMS.In this work, the forms of HMS can be divided into four kinds by the improved BCR sequential extraction method (Alan and Kara 2019b;Fernández-Ondoño et al. 2017), such as F1, F2, F3, and F4.Among them, HMS in the form of weak acid extraction (F1) can be directly utilized by plants, HMS in reducible (F2) and oxidizable (F3) forms can be converted into HMS in weak acid extraction form (F1) under certain conditions, and heavy metals in residue form (F4) can exist stably for a long time.In the soil, it is not easy to be used by organisms, and it is not easy to migrate and transfer, so it is less harmful to the environment.Because As, Cd and Pb are the main pollution factors, the speciation of these three elements was studied.
The speciation fractions of As, Cd and Pb are shown in Figure 2. The residue speciation of As (F4) in the under-investigation area soil was much larger than other speciation, and the proportion of As (F4) in CTL was more than 75%.The residue speciation of As (F4) was not easy to migrate and transform, cannot be absorbed, and utilized by plants, and it exhibits low activity.This indicates that As in the test soil is less harmful to the environment and is mainly controlled by natural factors.Relevant studies suggest that rock weathered As-containing clastic deposits are the main source of As in soil (Mathee et al. 2018;Xu and Fu 2022).The morphological distribution of Cd in tested soil samples as shown in Figure 2. The reducible speciation of Cd (F2) experienced a dominant fraction, and the proportion of oxidizable Cd (F3) and residue Cd (F4) exhibit a relatively low value.The residue speciation of Cd (F4) in most sampling points experienced a lower value, which was less than 10%.Generally, the extractable states (F1+F2+F3) were more than 80%, indicating that the Cd from the topsoil in the whole study area is in a high activity state.Additionally, the proportion of extractable states Cd at sampling point SZY is higher than that at other sampling points, indicating that mining activities have a great impact on the form of Cd.The main fraction of Pb is reducible state and the content of residue speciation Pb exhibits a relatively low value.The content of oxidizable speciation Pb from some tested samples in sampling area CTL also exhibits significant differences.It should be emphasized that Pb in topsoil is also in a high activity state in the whole under-investigation area.This demonstrates that the Cd and Pb in the soil obtained in the study area can be absorbed and utilized by organisms, and there are different degrees of potential risks.

Pearson correlation coefficient
In this study, Pearson correlation coefficient and PAC were used to analyze the statistical data for each heavy metal.Analysis of the correlation among HMS can determine common sources or migration pathways.Pearson correlation coefficient method was utilized to investigate the correlation among seven HMS (Zhao et al. 2021).Pearson correlation coefficient and corresponding scatter diagram, respectively, as shown in Table S2 and Figure 3.There is a significant correlation among As-Ni, As-Cr, Cd-Pb, Cd-Zn, Pb-Cu and Zn-Cu, and corresponding correlation coefficients were 0.540, 0.553, 0.614, 0.706, 0.750 and 0.694, respectively.Especially, Pb-Zn and Ni-Cr in soil exhibit a highly positive correlation, and corresponding correlation coefficients were 0.908 and 0.887, respectively.These considerations have heightened interest in the possibility of providing they have a common sink in the sediments (Wang et al. 2020), which means that these pollutants may come from the mining of local lead-zinc mines.The relationships of Pb-Ni and Pb-Cr appear to be a significant negative correlation.From the results obtained so far, it seems that the HMS experienced little change with of pH value.On the whole, the contribution of the factors affecting the deposition of HMS is greater than that of the pH value.

Principal component analysis (PCA)
PCA was carried out for seven HMS variables, and the results are displayed in Table S3.The first principal component (PC1) and the second principal component (PC2) were extracted by the maximum variance rotation method.The eigenvalues were 3.533 (>1) and 2.305 (>1), the corresponding variance contribution ratios were 50.465% and 32.925%, respectively, and the cumulative contribution ratio was 83.390%.That is, the two principal components cover 83.39% of the total amount of original data information.Based on the information contained in Table S3, the element loads of Pb, Zn, Cd and Cu exhibit a higher value, and the loads are 0.96, 0.926, 0.753, 0.749, respectively, and there is a very significant positive correlation among Pb, Zn, Cd, and Cu.This suggests that the above four sources of heavy metals are similar.In addition, the coefficient of variation of heavy metals in the first principal component exhibits a relatively high value, indicating that it is more affected by human activities.The research shows that the main mining processes of Zn and Cu elements in the soil around SA are controlled by the degree of mining development, indicating that the PC1 is mainly affected by mining and smelting.These findings demonstrate that the spatial distribution of the PC1 is significantly positively correlated with the spatial content distribution of these above four HMS.
In PC2, Cr, Ni, As, and Cu elements have higher loads, and their loads are 0.761, 0.813, 0.897, and 0.436, respectively.When the same element has equivalent loads on different main components, it can be considered that the elements have two origins of principal components (Qiao et al. 2023).The Cu element has moderate loadings on PC1 and PC2, which are 0.749 and 0.436, respectively, indicating that there may be two different sources of Cu in soil.The content of Cu in the study area was 1.6-2.3times of the background value, and there was a relatively obvious accumulation, indicating that in addition to the geological background, there were also pollution sources generated by human activities.It should be emphasized that the element loadings of Pb, Zn, and Cd are relatively low in PC2, and their loadings are 0.063, 0.183, and 0.182, respectively, which indicates that the sources of the above three elements in PC2 are single.According to the soil background value data in Hunan Province in Table 2, as well as the spatial distribution of heavy metal elements in soil and the distribution of industrial and mining enterprises, it can be determined that the high-value areas of Pb, Zn and Cd are mainly distributed in some areas around the sampling point, and there are many industrial and mining enterprises in this area, indicating that Pb, Zn and Cd are mainly affected by industrial emissions.Based on the PCA results, it can be summarized that the heavy metals Pb, Zn, Cd, and Cu are likely to have the same origin or source of soil contamination, primarily influenced by mining and smelting activities.On the other hand, As, Ni, and Cr are also likely to share the same origin or source, resulting from a combination of geological background and pollution generated by human activities.
The statistical results of the number of points of HMS under different pollution degrees are shown in Table S4.Among the seven HMS, the pollution degree of As, Cd and Pb exhibited the strongest value, while the pollution characteristics of Zn, Cu, Ni, and Cr in the soil at most points experienced an insignificant level of pollution were observed as compared to that of the number of most polluted points.However, the cumulative index of the six sampling points for Zn and the two sampling points for Cu are all greater than 2, indicating that the soil at these sampling points is at least moderately contaminated by Zn and Cu.In the meantime, the soil is the least polluted by Ni and Cr.
The median value of As element's I geo is 2.27, which indicates that more than 50% of the soil is at least moderately polluted by As.Specifically, according to the evaluation criteria, the As element's I geo of 27 out of 55 sampling points is between 2 and 3, which is considered to be moderate pollution.Additionally, among them, the As element's I geo of eight sampling points is between 3 and 4, which belongs to severe pollution.Among the 55 sampling points, the median value of the Cd element's I geo is 4.05, which indicates that the pollution degree of Cd exhibited the worst level.Based on the evaluation criteria in Table 2, the Cd element's I geo is 4.05.These data strongly suggest that more than 50% of the tested soil at the sampling points are seriously polluted by Cd, which is similar to the pollution degree of As to the soil.Among them, the Cd element's I geo at 18 sampling points is between 4 and 5, which belongs to severe pollution.At the same time, the Cd element's I geo at other 10 sampling points is greater than 5, indicating that the soil in these 10 sampling points is highly polluted by Cd.It is usually postulated that that more than 50% of the soil is at least moderately polluted by Pb based on the median value of Pb element's I geo of 1.69.In contrast, the Pb pollution degree of half of the points in the other part of the soil is between no pollution and moderate pollution.However, several examples have dramatically underlined that the soil at the six sampling points belonged to the serious Pb pollution level in the light of Pb element's I geo higher than 3.

Potential ecological risk index (RI)
According to Eq. ( 2), ( 3) and (4), the E i r and RI of seven HMS in the under-investigation area were calculated with the screening value as the reference ratio.These results in Table 3 not only demonstrate the heavy metals pollution degree of but also confirm the order of E i r of seven HMS: Cd > As > Pb > Cu > Zn > Ni > Cr.Compared with the standard limit 320, the E i r of Cd in soil exhibit the highest value, reaching 1385.89.These findings provide new insights in understanding the high ecological risk in Cd pollution and suggest that it could serve as a high toxicity response parameter to evaluate the degree of soil pollution at this point.Likewise, the E i r of As reaches 102.84, which indicates that, like Cd, it is also a polluting metal with high ecological risk at this point.By contrast, at the same sampling point, the higher Pb content in the soil seems to increase the E i r value, but in fact, an unexpected finding of this study is the Pb E i r value of 28.74 is far smaller than Cd and As.This further proves that the toxicity response parameters of Pb are kept at a relatively low level.Through extensive research, the lowest potential risk parameters among the seven HMS of Zn, Cu, Ni and Cr have been identified, and the corresponding maximum E i r does not exceed 40.Herein, we can conclude from the results that these four HMS exhibit a low ecological risk to the soil in the sampling area.
In Table 3(b), compared with the overall risk, the RI in three survey areas experienced little change.However, the pollution near SZY mining area exhibited a higher profile, followed by GYB.This is mainly due to the existence of a small beneficiation plant in this area, and there is a natural barrier between the CTL and SZY mining areas, a hill.This indicates that the HMS are highly enriched and unevenly distributed due to the existence of industrial development such as metal mining and concentrate smelting near the sampling points.It is important to distinguish main pollution elements and those of other trials of imaginary pollution element, from the prevailing view that the lead-zinc mining contributes to an important source of lead and zinc pollution, although not a unique, way to the determination of heavy metal pollution.It may be an interesting observation in this work that Cd pollution is the most serious in the study area, while Zn shows the opposite trend, only low-dose pollution.

Conclusion
The research provides a theoretical basis for the prevention and control of local soil pollution and ecological environment-human health risk management.It has important practical significance and practical value.The main conclusions are as follows: (1) Soil Acidity: The soil in the under-investigation area is weakly acidic.The highest concentrations of As, Cd, and Pb were significantly higher than the screening values in Hunan Province, being 8.30, 46.20, and 6.08 times higher, respectively.The concentrations of Zn, Cu, Ni, and Cr were lower but still exhibited medium values, with differences compared to the screening values being more than 2.70 times, 2.54 times, and 1.49 times, respectively.(2) Cd Pollution: Cd is the most problematic element, with the highest pollution degree and a high activity state in the topsoil throughout the study area.The proportion of extractable Cd at sampling point SZY is higher than other points, indicating a significant impact of mining activities on Cd speciation.(3) Cu Pollution: Cu content in the study area was 1.6-2.3times higher than the background value, showing significant accumulation from both geological background and human activities.(4) Pollution Order: The order of the pollution degree based on the E i r index for the seven heavy metals is as follows: Cd > As > Pb > Cu > Zn > Ni > Cr.This ranking demonstrates the relative pollution levels of these metals in the under-investigation area.(5) Mining Impact: The soil in the under-investigation area, particularly at SZY and GYB sampling points, shows a higher pollution degree compared to CTL soil.Cd, Pb, Zn, and Cr in the underinvestigation area soil are confirmed to be primarily affected by mining development.(6) Future research should prioritize the development of effective and sustainable remediation strategies for controlling Cd pollution, understanding the speciation and mobility of Cd, identifying sources of Cu contamination, assessing pollution levels of Zn, Ni, and Cr, conducting long-term monitoring, and comparative studies to understand the impact of mining activities.These efforts will contribute to mitigating heavy metal pollution and promoting sustainable land use practices in the under-investigation area. in the center of National SA Park TCLP the toxicity characteristic leaching procedure groundwater pollution and studying the impact of organisms on geochemical processes.In addition, he explores the development and application of environmental functional materials and investigates the toxic effects of chemicals on organisms and ecosystems.His research also involves exploring environmental processes and tracing methods to better protect the environment and promote sustainable development.

Figure 2 .
Figure 2. The speciation fractions of As, Cd and Pb in the HMS.
Table2shows the risk screening values for the contamination of heavy metals (As, Cd, Pb, Cr, Zn, Cu and Ni) in agricultural land based on China National Standard GB 15,618-2018.Due to the nonuniformity of HMS pollution, 55 soil samples from 3 sampling points were statistically analyzed by using the minimum value, maximum value, and average value.The content characteristics of each heavy metal was known for in Table2.The distribution frequency of pH value and HMS was displayed in Figure1.

Table 2 .
Total averages of soil metals and comparisons with background values (n = 55, mg/kg).significant differences.The distribution parameters of HMS have certain regularity, and it showed normal skewed distribution.Compared with the background value of HMS in Hunan Province, the concentrations of seven HMS were greater than the background value.The variation range of As was 38.33-331.83mg/kg, with 32 samples ranging from 50 to 200, and the variation range of Cd was 0.79-13.86mg/kg, of which 45 samples were less than 6 mg/kg, and the variation range of Pb was 32.52-546.80mg/kg, with 49 samples were less than 300 mg/kg.Additionally, the highest contents of As, Cd and Pb were 8.30, 46.20 and 6.08 times of the screening value in Hunan Province, respectively.The concentrations of Zn, Cu, Ni and Cr exhibit a lower value.The highest concentrations of Zn, Cu and Ni experienced a medium value, and differences were observed as compared to that of screening value, more than 2.70 times, 2.54 times and 1.49 times, respectively, but the concentrations of Cr among HMS remained at a relatively small value, lower than the risk screening value.It is generally believed that there is a general trend where higher concentrations of heavy metals correspond to higher soil pH levels.It is evident that the pH changes in the three study areas (CTL, SZY, and GYB) do not align with the fluctuations in the heavy metal content in the soil.Despite variations in pH values, there is no consistent pattern indicating a direct correlation between pH and heavy metal concentrations in Table Figure 1.Frequency distribution of pH value and content of HMS.exhibited

Table 3 .
Mathematical statistical of RI of various heavy metals.