Characterization of Heavy Metal Contamination in Groundwater of Typical Mining Area in Hunan Province

Characterization of Heavy Metal Contamination in Groundwater of Typical Mining Area in Hunan Province | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characterization of Heavy Metal Contamination in Groundwater of Typical Mining Area in Hunan Province Wenjie Hao, Huan Liu, Shuli Hao, Kuanzhen Mao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3929847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This paper mainly carries out the research work by analysing and testing the groundwater quality in typical mining areas, based on the hydrogeochemical investigation data of groundwater monitoring points, and analyses the statistical characteristics of different index factors in antimony mining areas by using Spss and Surfer software. Firstly, using the standard deviation feature analysis, it is found that mining is more sensitive to the impact of As and Sb, and the degree of its pollution is the most serious, and the exceeding rate reaches 100%; later, the results of the analysis through the principal component analysis method show that the first principal component a1 has a higher correlation with Sb and Se in the original variables, the second principal component a2 has a stronger positive correlation with Ba in the original variables, and a stronger negative correlation with Mo, and the third principal component a3 has a stronger positive correlation with Mn in the original variables, and the negative correlation between Co and the third principal component is stronger. Finally, the correlation between each heavy metal indicator factor and the other six indicator factors was calculated by Spss software analysis to find out the pattern of the order of strength of the correlation of the indicator factors, and it was concluded that the concentration of Se and Mo had a stronger correlation with the other heavy metals in the study area, whereas the correlation of Mn and Co with the other influencing factors was lower. This paper summarises and refines the statistical characteristics of groundwater hydrogeochemistry in the mining area to provide reference for the diagnosis, prevention and control of groundwater pollution risk in this and similar mining areas. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Groundwater Heavy metals Statistical characterization Pollution contribution Correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introductory The Problem of Groundwater Pollution has become a major concern for governments, businesses, and the public [ 1 ] . There is a growing awareness among people, and water quality safety has increasingly become an important issue for national sustainable development and ensuring public health [ 2 – 5 ] . Therefore, conducting relevant research holds significant importance for the ecological environment construction and protection of regional groundwater resources [ 6 ] . Metal mining areas are one of the main sources of groundwater pollution, characterized by severe heavy metal contamination, which exhibits high toxicity, slow degradation, and ease of accumulation. Heavy metals such as Pb, Cr, Hg, Se, Mn, among others, possess strong toxicity. The cumulative effect of these heavy metal elements in the human body poses significant potential risks to the health of regional populations, causing severe harm to human organs [ 7 – 9 ] . The main source of these heavy metals is the mining activities that generate enormous value. The resulting tailings, dust, waste rocks, and wastewater from mining operations can lead to the diffusion and infiltration of heavy metals into soil and groundwater, causing severe pollution to groundwater bodies, thus resulting in irreparable harm to human health [ 10 ] . The antimony mining area in the study region has a wide variety of mineral deposits, resulting in a diverse range of heavy metal pollutants. This research aims to provide relevant personnel with a better understanding of the groundwater pollution situation in the mining area. It serves as a scientific basis for the prevention and control of groundwater pollution in the mining area and the management of public health risks. Consequently, effective measures can be formulated to reduce the health risks posed to residents. Overview of the Study Area 1.1 Socioeconomic Overview of the Study Area The investigation area is situated in the northwest of Lengshuijiang City, Hunan Province, encompassing the Mining Township, the Xikuangshan Administration, and the border area of Zhonglian Township. It lies approximately 13 kilometers south of Lengshuijiang City, with geographical coordinates ranging from 111°25′47″ to 111°31′22″ east longitude and 27°49′28″ to 27°43′05″ north latitude. The antimony mining industry in this region began production in 1897 and has a history of over a century. Mineral production and processing serve as the primary economic sources for the local government and residents, earning the area the titles of "World Capital of Antimony" and "Coal Sea of Jiangnan" [ 11 – 12 ] . Within this mining area, two large ore deposits, one medium-sized ore deposit, and three small-sized ore deposits have been identified. The antimony mineral field in the northern Xikuangshan area is the concentration zone for antimony mineral production, with an accumulated confirmed reserve of 26.265 million tons (metal reserve of 855,202 tons). The reserve is exceptionally abundant, ranking first in the nation and representing the world's largest antimony mineral field [ 13 – 14 ] . The total population residing in the mining area is approximately 17,100, comprising approximately 15,000 urban residents and around 2,100 rural villagers [ 15 , 16 ] . Over the past hundred years of mining operations in the Xikuangshan antimony mining area, the substantial generation of "three wastes"—waste gas, waste residue, and waste water—has caused severe environmental damage to local soil and groundwater. Consequently, it has posed varying degrees of health risks to the local residents [ 17 , 18 ] . As the subject of this investigation, the Xikuangshan mining area demonstrates typicality and representativeness, thus offering outcomes of considerable representational significance in the research. 1.2 Hydrogeological Conditions 1.2.1 Fault Zone Permeability and Aquifer Characteristics The fault zones within the area, including the F75, F72, F3 of the North-Northeast group, and the F19, F17, F104 of the North-Northwest group, constitute significant and mechanically similar normal faults within the ore deposit. Due to the surrounding rocks being predominantly composed of argillaceous limestone, sandstone, and shale, the development of karstification is not favored. The fractured zones within the fault zones consist mainly of fractured mudstones, sandstones, and limestone blocks with a dense cementation, which impedes groundwater movement and storage. Drilling observations within these fault zones did not reveal any water seepage. Water injection tests conducted via boreholes demonstrated minimal water injection rates (0.0014L/S·m), particularly within the main F75 fault, where 14 cross veins in the midsections of tunnels 7, 9, 11, and 13 displayed dry fault surfaces without any indications of groundwater activity. These fault zones are entirely filled with fragmented rocks such as sandstone, limestone, and shale. However, smaller secondary fault zones and fissures were observed in the tunnel exposures, facilitating water storage within the ore deposit, primarily channeling fractured water from silicified limestone and silicified rock zones. These secondary faults trend in the northeast and northwest directions, with an inclination exceeding 60°, extending several meters to tens of meters in length, and possessing a maximum width of approximately 1.5 meters. The predominant lithology comprises silicified limestone, followed by limestone, with flow rates ranging from 0.01 to 0.5L/S. Based on the above description, the fault zones within this mining area exhibit relatively poor water-bearing and water-conducting properties. 1.2.2 Groundwater Flow Status The Xikuangshan mining area is situated at the hydrological watershed of the aquifer system. To the east, the Yunxi biotite granite forms a hydrological barrier, while the F75 fault acts as the western hydrological boundary. Consequently, it creates a north-south flowing aquifer unit, effectively isolating the ore deposit from the regional groundwater. Moreover, a local hydrological watershed exists in the central part of the study area, positioned at Qilijiang, resulting in independent flow divisions between the northern and southern mines. Additionally, surface water bodies distributed within the ore deposit are all relatively small. As a result, the groundwater in the ore deposit primarily relies on infiltration from atmospheric precipitation for recharge. Natural features such as exposed rock cavities, fractures, and old workings become recharge zones for the aquifer. A few unclosed boreholes and surface silicified limestone fractured zones also contribute to the aquifer recharge. Consequently, the overall recharge area of the aquifer is relatively limited. The groundwater flow in the aquifer predominantly exists in the form of limestone fissures, karst caves, interlayer fractured zones, and fractured fissures in silicified limestone. Generally, the groundwater flows from the Qilijiang watershed in the central part of the study area, discharging southwards and northwards from the study area. The runoff distance is relatively short. The primary discharge method is in the form of springs, with only a minimal portion discharged through faults. Extensive mining activities in the Xikuangshan area have led to the formation of artificial discharge zones in deeper mining regions. The dynamics of the ore deposit groundwater are primarily characterized by significant fluctuations in water level and quantity. Since it is predominantly replenished by rainfall, variations in groundwater dynamics are closely related to precipitation. Seasonal changes are evident, with groundwater levels rapidly rising during the rainy season, leading to a sharp increase in flow rates. For instance, observation at Spring No. 7 recorded a flow rate of 2.129L/S before rainfall, surging to 14.13L/S within the initial 30 minutes of rainfall. The monthly amplitude of groundwater levels is generally around 2.40 meters, with an average annual amplitude of 7.6 meters. Mining activities in the area have moderately impacted the underground aquifer due to groundwater extraction. However, water scarcity has not been observed, and there have been minimal occurrences of wells or springs running dry. Overall, the impact on regional groundwater balance remains relatively slight. Sample Collection and Testing Methods 2.1 Principles of Sampling Point Layout Based on the "Technical Specifications for Groundwater Environmental Monitoring" (HJ164-2020), "Technical Guidelines for Site Environmental Monitoring" (HJ25.2-2014), "Sanitary Standards for Drinking Water" (GB5749-2006), "Groundwater Quality Standards" (DZ/T0290-2017), as well as the "Technical Regulations for National Integrated Water Resources Planning," the following principles for sample point layout were formulated: (1) The southern and northern mining areas belong to different hydrogeological units. Accordingly, sampling points were arranged based on the distinct hydrogeological conditions of these independent units. (2) Considering the distribution of pollution sources such as leachate from mine waste, ore washing wastewater, and pit drainage, water quality monitoring points were set up at the groundwater recharge, flow, and discharge sections in each hydrogeological unit of the southern and northern areas. The spacing between points generally ranged from 150 to 200 meters. (3) In addition to the existing monitoring wells, domestic wells, and springs within the mining area, additional groundwater quality monitoring points were selectively added and adjusted based on the actual exposure of groundwater and the conditions of recharge, flow, and discharge. The placement of monitoring points was adjusted according to the specific conditions, avoiding uniform distribution. 2.2 Layout of Monitoring Network (1) The controlled area of the groundwater quality pollution monitoring network within the mining area covers an area of 30km². (2) A total of 26 groundwater quality pollution monitoring points were established within the mining area. In this study, a total of 26 groundwater sampling points were collected within the research area. Their distribution is illustrated in Fig. 1 . 2.3 Principles for Selecting Heavy Metal Evaluation Indicators Multiple characterization factors need to be considered to achieve the groundwater functional assessment's objective. However, the selection of characterization indicators varies widely, and it's not advisable to mechanically or indiscriminately include all factors in the evaluation index system. Doing so might dilute the role of dominant factors, affecting the accuracy of the assessment results. Therefore, it's essential to make an optimal selection of evaluation factors based on principles such as: the principle of dominance, measurability, operability, comprehensiveness, flexibility, among others. The mineral composition in the study area is relatively straightforward, with relatively simple chemical components. Apart from the primary element antimony (Sb), associated elements include As, Hg, Ag, Cu, Pb, Sn, V, Zn, Mo, Ga, and B. Following the classification system established by the International Agency for Research on Cancer (IARC) and the World Health Organization (WHO) for assessing the reliability of the carcinogenicity of chemical substances, essential trace elements for the human body in the associated elements within the study area include Mo, Co, Mn; harmful substances comprise As, Ba, Sb; carcinogenic substances include As, non-carcinogenic substances consist of Mn, Se, and potentially carcinogenic substances encompass Co, Sb. Considering the aforementioned principles and the evaluation objectives of this study, the heavy metals Mo, As, Co, Mn, Ba, Sb, and Se were chosen as evaluation indicators. 2.4 Sampling and Testing Methods (1) Groundwater Sampling and Preservation In accordance with the "Standard Examination Methods for Drinking Water" (GB5749-2006, GB/T5750.1-5750.13-2006), containers were acid-washed or alkali-washed as specified before sampling, followed by rinsing with distilled water. The sampling instruments were rinsed with the source water at least three times before sampling. Non-disposable groundwater sampling equipment was used, requiring pre- and post-sampling cleaning. Wastewater generated during the cleaning process should be collected and disposed of properly. For sample bottles without preservatives, they were rinsed with the water to be sampled two to three times before groundwater sampling. Samples containing heavy metals such as As, Mn, Sb were collected in 250ml polyethylene plastic bottles, adjusted to pH less than 2 with nitric acid, and analyzed within 10 days. The frequency of this sampling was once and the sampling period was two days. (2) Groundwater Sample Testing Methods Adhering to the requirements of the "Standard Examination Methods for Drinking Water" (GB/T5750.6-2006) and "Water Quality-Determination of Barium" (HJ 602–2011) for various indicators, different instruments were equipped to ensure the quality of sample analysis based on the quality level of different analysis methods. Main testing instruments included gas chromatography-mass spectrometer, atomic absorption spectrophotometer, and atomic fluorescence spectrometer, among others. The standard testing methods used for various detection components are detailed in Table 1 . Table 1 Test method of heavy metal evaluation index factors for groundwater samples Elemental Standard Methodologies As GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (6.1) Hydride atomic fluorescence method Se GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (7.1) Hydride atomic fluorescence method Sb GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (19.1) Hydride atomic fluorescence method Co GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (14.1) Flameless atomic absorption spectrophotometry Mo GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (13.1) Flameless atomic absorption spectrophotometry Mn GB/T 5750.6–2006 Standard Test Methods for Drinking Water METAL INDICATORS (4.2) Flame atomic absorption spectrophotometry Ba HJ 602–2011 Water quality Determination of barium Graphite Furnace Atomic Absorption Spectrophotometry The Statistical Analysis of Heavy Metal Pollution in Underground Water of Mining Areas Before evaluating heavy metal pollution in underground water, it is essential to analyze the statistical characteristics and distribution patterns of indicator elements in the study area using statistical methods. Through statistical characteristic analysis, one can grasp the apparent features of heavy metal pollution in the underground water of the study area, offering the necessary basis for evaluating the results [ 23 ] . In this section, SPSS and Excel software were primarily employed to analyze the experimental detection data of selected heavy metal evaluation indicators, namely Mo, As, Co, Mn, Ba, Sb, and Se. The analysis mainly encompasses parameters like maximum value, minimum value, mean, variance, kurtosis, skewness, and coefficient of variation. This translation could be used for an academic paper. The minimum and maximum values represent extreme values in the data, indicating the degree of heterogeneity within the dataset. The median is a type of central tendency that describes the typical scenario within a set of data. The mean represents a measure of central tendency within a dataset and serves as an indicator reflecting the trend of the data set. In statistical work, the mean (average) and standard deviation are the two most important measures used to describe the trend and dispersion of data sets. Variance and standard deviation are used to measure the deviation of a random variable from its expected value and are often employed to study the deviation between mathematical variables and their mean in various scenarios. The coefficient of variation, similar to variance and standard deviation, primarily indicates the degree of dispersion within data, influenced by both the dispersion level of variables and the average level of variable values. The statistical characteristic values of the indicator elements in the study area can be found in Table 2 . This translation could be used in an academic paper. Table 2 Statistical Characteristics of Heavy Metal Indicator Factors Mn As Ba Co Mo Sb Se Range 0.110 0.650 0.057 0.0005 0.0090 15.362 0.019 Minimum 0.010 0.030 0.009 0.0001 0.0010 0.008 0.002 Maximum 0.120 0.680 0.066 0.0006 0.0100 15.370 0.021 Median 0.010 0.240 0.022 0.0001 0.0010 0.120 0.002 Mean 0.020 0.299 0.028 0.0002 0.0025 1.765 0.005 Std. Deviation 0.024 0.227 0.017 0.0002 0.0023 3.910 0.006 Variance 0.001 0.051 0 0 0 15.291 0 Skewness 3.338 0.232 0.987 1.617 1.840 2.540 1.727 Kurtosis 12.278 -1.591 -0.094 1.395 3.343 6.056 1.662 CV 119.87% 75.73% 58.52% 79.22% 92.04% 221.59% 111.12% P.index 20.09% 2991.00% 4.05% 0.38% 3.57% 35293.20% 53.76% O.Std.index 3.85% 100% 0 0 0 100% 20% Note: (1) CV (Coefficient of Variation) = (Standard Deviation / Mean) × 100%; Standard values refer to the Drinking Water Standards (GB5749-2006); P.index (Pollution Index) = Mean / Standard Value; O.Std.index (Exceedance Rate) = (Number of exceedances / Total monitored data points)×100% 3.1 Analysis of Standard Deviation Characteristics of Heavy Metal Evaluation Index Concentrations From the data analysis results, the standard deviations for Mn, As, Ba, Co, Mo, Sb, and Se are 0.024081, 0.22651, 0.0166, 0.000152, 0.002301, 3.910359, and 0.006, respectively. Among these, As and Sb have relatively large standard deviations, with their maximum concentrations being 22.67 and 1921.25 times the minimum concentrations, respectively. These results indicate a significant level of dispersion in the concentrations of As and Sb within the sampled water, suggesting a strong correlation with anthropogenic factors in the surrounding environment, where human influences predominantly affect these two index factors. In contrast, the variance for the other five ions is almost zero, indicating that these five ions are primarily influenced by natural factors, or a combination of natural and anthropogenic influences at relatively similar levels. 3.2 Analysis of Kurtosis and Skewness Characteristics of Heavy Metal Evaluation Index Concentrations Under natural conditions, the concentration curves of heavy metal ions generally conform to a normal distribution. Kurtosis and skewness can reflect whether the concentration of certain ions conforms to or deviates from the normal distribution, and the degree of deviation can generally indicate the influence of anthropogenic factors on these ions. Kurtosis [ 23 ] is a measure reflecting the shape of the distribution of a random variable. The kurtosis K of a random variable x is defined as: K = E[ x -E( x )] 4 /[Var( x )] 2 When K > 3, it represents a leptokurtic curve, indicating that the variable values are densely distributed around the mode. When K = 3, it matches the degree of sharpness of a normal distribution curve. When K < 3, it represents a platykurtic curve, suggesting a relatively uniform dispersion of variable values around the mode. Skewness measures the degree and direction of the distribution's asymmetry and is a dimensionless value. The skewness S of a random variable x is defined as: S = E[ x -E( x )] 3 /[Var( x )] 3/2 When S > 0, the distribution of x is positively skewed. When S = 0, it is symmetric around the mode. When S 3, 3.343 > 3, and 6.056 > 3, respectively, and the skewness values are 3.338 > 0, 1.84 > 0, and 2.54 > 0, respectively. Based on the analysis, it can be inferred that the concentrations of these three ions deviate from a normal distribution, indicating varying degrees of influence from anthropogenic factors, especially notable for Mn and Sb influenced by the surrounding anthropogenic environment. Meanwhile, As, Ba, Co, and Se exhibit kurtosis values of -1.591, -0.094, 1.395, and 1.662, respectively, with absolute values less than 3, suggesting platykurtic curves. The skewness values are 0.232, 0.987, 1.617, and 1.727, all greater than 0, indicating positive skewness. Similarly, these three factors are also influenced to varying degrees by anthropogenic factors. 3.3 Analysis of Statistical Characteristics of Heavy Metal Evaluation Index Concentrations From the perspective of the coefficient of variation, the degree of variability among the seven factors is in the following order: Sb > Mn > Se > Mo > Co > As > Ba. Particularly, the coefficients of variation for As, Se, and Sb exceed 100%, even reaching 200%. The pollution indices of the heavy metal indicator factors are as follows, in descending order: Sb > As > Se > Mn > Ba > Mo > Co. Notably, the pollution index for As reaches 2991%, while Sb is exceptionally high at 35293.2%. From the standpoint of the mean values, antimony and arsenic exhibit severely polluted states. The Table 2 shows that the exceedance rates for As and Sb are 100%, while for Se and Mn, they are 20% and 3.85%, respectively. The exceedance rate for the remaining three indicator factors is 0%. 3.4 Analysis of Pollution Characteristics of Heavy Metal Exceedances To visually depict the pollution status of each sampling point's water samples in both groundwater and surface water for indicators showing exceedance rates, we have generated concentration status diagrams for the prevailing concentrations of Mn, As, Sb, and Se, which have non-zero exceedance rates. These diagrams illustrate a comparison between the concentrations of each exceedance factor against the standard values at each sampling point and depict the contrast among the sampling points, as shown in Fig. 2 . According to the Drinking Water Standard (GB5749-2006), the standard concentration for Mn is 0.1 mg/L. From Fig. 2 , it's evident that only Spring 22 in Chuanshan Village of the mining area and surface water points DB1 and DB4 in Dongxia Village of the mining area and Shengli Village in Zhonglian Township respectively, exceed the standard concentration for Mn. Furthermore, it's notable that the Mn content in surface water is generally higher than in groundwater. Essentially, only point 22 exceeds the average Mn concentration in surface water, and only three groundwater sampling points exhibit Mn concentrations higher than those in surface water. Figure 3 illustrates that both surface water and groundwater within the study area exhibit arsenic (As) concentrations higher than the standard value of 0.01 mg/L for drinking water. Notably, the surface water point DB2 in Tanjia Community, Minshan Town, records an alarming concentration of 11.93 mg/L. Regarding antimony (Sb), after handling the outliers, spring water point 13 north of Minshan Hospital in Taotang Street, Minshan Town, was identified as an outlier and removed. Drainage from hospital facilities such as treatment rooms, laboratories, wards, laundry rooms, X-ray imaging, isotope therapy diagnostic rooms, and operating theaters intensifies the antimony pollution in the groundwater in that area, potentially compromising the accuracy of the water pollution assessment in the research area. This particular data point has been considered an outlier and excluded from the analysis. Figure 4 illustrates that the concentrations of Sb in all sampling points within the research area exceed the drinking water standard value of 0.005 mg/L, indicating severe antimony pollution. The minimum recorded concentration surpasses 0.0084 mg/L, more than 1.68 times the drinking water standard. Moreover, the Sb concentrations in four surface water sampling points across the research area are consistently higher than the median Sb concentration in groundwater. The standard value for selenium (Se) in drinking water is 0.01 mg/L in Fig. 5, and the exceedance rate at the groundwater monitoring points in the surveyed area is 20%. Water from the Qilijiang Iron Mine (11), in direct contact with the mine area, exhibits significant pollution with a concentration of 0.047 mg/L, considered an outlier. After removing this outlier, the analysis reveals some sampling points with selenium concentrations at 0 mg/L, while the maximum concentration reaches 0.021 mg/L, twice the standard value, suggesting a distinctly regional pattern of pollution. Overall, the analysis indicates that arsenic and antimony pollution is most severe in the groundwater of the study area, followed by selenium and manganese, with minimal contamination from barium, cobalt, and molybdenum. Principal Component Analysis of Heavy Metal Elements The abundant variety of mineral resources in the study area leads to a multitude of factors affecting the groundwater quality in this region. This paper utilizes SPSS software to conduct a principal component analysis on selected indicator factors. This approach, while minimizing data loss, involves linear combinations and discarding minor details, replacing multidimensional variables with a few comprehensive ones. This method aims to reduce workload while ensuring an accurate assessment of the groundwater quality in the study area. 4.1 Principles of Principal Component Analysis Principal Component Analysis (PCA) [ 24 – 27 ] is a statistical method for dimensionality reduction, aiming to quantitatively study multiple-dimensional factors within the same system. The fundamental concept lies in the assumption that among numerous correlated factors, there are dominant common factors. By analyzing the internal structural relationships within the correlation matrix of the original variables, this method identifies representative and targeted indicators, facilitating their mutual comparison. Additionally, it objectively determines the respective weight values, effectively capturing the main contradictions and significantly reducing subjectivity. In the evaluation of heavy metal pollution in groundwater, PCA proves highly persuasive, offering a robust mathematical model. F1 = a11 ZX1 + a21 ZX2+…+ an1 ZXn F2 = a12 ZX1 + a22 ZX2+…+ an2 ZXn„„„ (4.1) Fm = a1m ZX1 + a2m ZX2+…+ anm ZXn Composite evaluation function: $$\text{F}=\frac{{{\lambda }}_{1}}{{{\lambda }}_{1}+{{\lambda }}_{2}+\dots {{\lambda }}_{\text{p}}}{\text{F}}_{1}+\frac{{{\lambda }}_{2}}{{{\lambda }}_{1}+{{\lambda }}_{2}+\dots {{\lambda }}_{\text{p}}}{\text{F}}_{2}+\frac{{{\lambda }}_{\text{p}}}{{{\lambda }}_{1}+{{\lambda }}_{2}+\dots {{\lambda }}_{\text{p}}}{\text{F}}_{\text{p}}$$ 4.2 Where anm represents the eigenvectors corresponding to the eigenvalues of the covariance matrix of the original variable matrix X; ZX1, ZX2, ZXn are the standardized values of the original variable matrix X; λ1, λ2, ..., λp represent the eigenvalues of the ZX matrix; n denotes the number of factors; m represents the number of samples; p stands for the number of principal components. 4.2 Results and Analysis The analysis results of heavy metal indicator factors in water samples at sampling points using SPSS are shown in the Table 3 . Table 3 Evaluation index system correlation coefficient matrix eigenvalues and contribution rate ingredient Initial eigenvalue Extracted principal components eigenvalue contribution rate % Cumulative contribution % eigenvalue contribution rate % Cumulative contribution % 1 2.902 41.463 41.463 2.902 41.463 41.463 2 1.306 18.650 60.113 1.306 18.650 60.113 3 1.119 15.979 76.093 1.119 15.979 76.093 4 0.840 12.000 88.093 5 0.361 5.159 93.252 6 0.284 4.053 97.305 7 0.189 2.695 100.000 From the Table 4 , when the cumulative contribution rate is greater than or equal to 75%, three main components can be extracted. The eigenvalue of the first principal component is 2.902, the second principal component is 1.306, and the third principal component is 1.119. The contribution rate of each eigenvalue represents the weight of each principal component, i.e., λ1 = 41.463%, λ2 = 18.65%, λ3 = 15.979%. Therefore, three principal components are extracted, and a principal component matrix is established. Table 4 Principal Component Matrix Principal Components Factor 1 2 3 Mn -0.302 0.083 0.613 As 0.703 -0.538 -0.013 Ba 0.583 0.750 -0.067 Co 0.358 -0.119 -0.746 Mo 0.660 -0.554 0.273 Sb 0.840 0.337 0.059 Se 0.841 0.108 0.322 The initial factor loading matrix displays the correlation coefficient values of each principal component with its corresponding variables. In the principal component matrix, dividing each column vector of the mth component by the square root of the mth eigenvalue provides the coefficients corresponding to each indicator for the mth principal component, i.e., the eigenvectors a1, a2, and a3, as shown in Table 5 . Table 5 Eigenvectors of the Correlation Matrix Eigenvectors X1 X2 X3 X4 X5 X6 X7 a1 -0.177 0.413 0.342 0.21 0.387 0.493 0.493 a2 0.073 -0.47 0.656 -0.104 -0.485 0.295 0.095 a3 0.579 -0.012 -0.063 -0.705 0.258 0.056 0.304 The eigenvectors obtained from Table 5 were multiplied by the standardized variables of the seven original factors to obtain the principal components. F1=-0.177*ZMn + 0.413*ZAs + 0.342*ZBa + 0.21*ZCo + 0.387*ZMo + 0.493*ZSb + 0.493*ZSe F2 = 0.073*ZMn+-0.47*ZAs + 0.656*ZBa+-0.104*ZCo+-0.485*ZMo + 0.295*ZSb + 0.095*ZSe F3 = 0.579*ZMn+-0.012*ZAs+-0.063*ZBa+-0.705*ZCo + 0.258*ZMo + 0.056*ZSb + 0.304*ZSe The comprehensive assessment index was calculated from the principal component functions and their corresponding eigenvalues, as shown in Table 6 . Table 6 Principal Component Analysis Results. Sampling Point First Principal Component F1 Second Principal Component F2 Third Principal Component F3 Comprehensive Principal Component F Ranking 11 5.419 -0.805 -2.913 5.498 1 12 3.719 0.422 0.545 2.866 2 15 3.205 0.178 1.668 2.186 3 13 4.099 2.385 3.044 2.069 4 9 1.828 0.033 -0.366 1.62 5 16 0.839 0.121 -0.732 0.873 6 3 0.375 -1.086 -0.167 0.6 7 14 1.249 0.678 1.619 0.454 8 1 -0.239 -0.853 -0.128 0.024 9 21 0.056 1.198 -0.796 -0.003 10 20 0.135 1.454 -0.681 -0.025 11 2 -0.367 -2.291 0.916 -0.046 12 10 -0.726 -1.366 0.659 -0.482 13 5 -0.689 -0.461 0.149 -0.514 14 8 -0.765 -1.774 1.186 -0.567 15 6 -1 -0.53 0.362 -0.816 16 24 -1.327 0.393 -0.657 -1.018 17 19 -1.44 0.233 -0.644 -1.081 18 04 -1.407 -0.217 -0.156 -1.086 19 18 -1.443 0.335 -0.211 -1.223 20 7 -1.566 -0.517 0.4544 -1.317 21 23 -1.836 0.115 -0.509 -1.422 22 17 -1.772 0.004 0.018 -1.487 23 25 -1.887 0.019 -0.275 -1.506 24 22 -1.954 1.994 -1.547 -1.656 25 26 -2.502 0.339 -0.839 -1.939 26 (1) According to Table 3 , the eigenvalues of the first, second, and third principal components are 2.902, 1.306, and 1.119 respectively. The contribution rates of the principal components are 41.463%, 18.650%, and 15.979% respectively, accumulating to 76.093%. This indicates that these three principal components essentially encompass the influencing factors of heavy metal pollution in the groundwater of the research area. The first principal component, in particular, contains more information and exhibits a stronger correlation with water quality. (2) From Table 5 , it can be observed that the first principal component a1 has a higher correlation of 0.493 with the original variables Sb and Se. This suggests that these two factors are the primary contributors to this type of pollution, while the pollution contribution of the other five indicators is relatively minor. The second principal component a2 shows a strong positive correlation of 0.656 with the original variable Ba and a significant negative correlation of -0.485 with Mo, indicating a relatively high contribution of Ba and Mo to this principal component. The third principal component a3 has a positive correlation of 0.579 with the original variable Mn and a strong negative correlation of -0.705 with Co, suggesting a significant contribution of Mn and Co to a3. (3) Based on the comprehensive principal component score F, Table 6 is obtained. A higher F value indicates more severe heavy metal pollution. Among these, sampling points 11, 12, and15 rank in the top three, signifying the most severe heavy metal pollution at these locations. On the other hand, sampling points 25, 22, and 26 rank at the bottom three, indicating the least polluted water samples among the research sampling points. (4) The distribution of heavy metal pollution shows that the less polluted monitoring points 26, 22 and 25 are located in the upstream area of the groundwater, and the most seriously polluted points are mainly distributed in the middle of the mining area or the downstream area of the groundwater. It means that the background value of heavy metal ions in the investigation area is small, and the heavy metal ions in the mining area and the surrounding groundwater mainly come from the mining of tin, antimony and iron ore, and the mining of minerals is the main source of heavy metal ions in the groundwater. Conclusion (1) Among the evaluation indicator factors of groundwater monitoring points in the research area, the standard deviation analysis reflects that As and Sb are more sensitive to the influence of mining activities. Mn, Se, Mo, Co, and Ba are primarily influenced by natural geological and hydrogeological conditions, with less impact from human activities. Skewness and kurtosis values of heavy metal evaluation indicators reveal varying degrees of anthropogenic environmental impact on the seven factors. The pollution sources of Mn, Mo, and Sb are relatively concentrated, while those of As, Ba, Co, and Se are more dispersed. The exceedance rate for As and Sb reaches 100%, with Se and Mn exceeding by 20% and 3.85%, respectively, while the exceedance rate for the remaining three indicator factors is 0. (2) The severity of pollution in the research area's groundwater is highest for As and Sb, followed by Se and Mn. In contrast, Ba, Co, and Mo's pollution levels among these three heavy metals have not significantly surpassed drinking water standards. (3) Through principal component analysis, the first principal component a1 exhibits a higher correlation of 0.493 with the original variables Sb and Se. The second principal component a2 shows a strong positive correlation of 0.656 with the original variable Ba and a notable negative correlation of -0.485 with Mo. The third principal component a3 displays a positive correlation of 0.579 with the original variable Mn and a strong negative correlation of -0.705 with Co. (4) Analysis using SPSS software computed the strength of correlation between various indicator heavy metal factors and other factors in the following order: Se > Mo > As > Sb > Ba > Mn > Co, with correlation values of 0.344, 0.303, 0.241, 0.238, 0.228, 0.137, 0.119 respectively. This indicates a stronger correlation between Se and Mo concentrations and other heavy metals in the research area, while Mn and Co exhibit lower correlation with other influencing factors. It suggests that Mn and Co concentrations demonstrate relatively independent distributions and generally exhibit a negative correlation with other indicator factors. Declarations Conflicts of Interest : The authors declare no conflict of interest. Funding: This research was funded by the National Key R&D Program of China (Grant No.2019YFC1509605), the Open Project Program of Hebei Center for Ecological and Environmental Geology Research (No. JSYF-202302). Author Contribution Writing—original draft preparation, W.H.; writing—review and editing, K.M. and W.H.; methodology, H.L.; visualization, S.H.; data curation, K.M. All authors have read and agreed to the published version of the manuscript. Data Availability Statement: The data have been explained in the paper. References Jiang Wanjun, Meng Lishan, Liu Futian, et al. Current Status and Development, Utilization, and Protection Suggestions of Groundwater Resources and Environmental Quality in Zhangjiakou Area. North China Geology, 2022, 45(3): 44–54. Liu Shengfeng, Huang Jun, Zhou Yuan, et al. Investigation of Groundwater Quality and Health Risk Assessment in Rong County, Guangxi. People's Pearl River, 2019, 40(12): 6–12. Wang J, Liu G, Liu H, et al. Multivariate Statistical Evaluation of Dissolved Trace Elements and Water Quality Assessment in the Middle Reaches of Huaihe River, Anhui, China. Science of the Total Environment, 2017, 583: 421–431. Wang J, Li Y, Huang J, et al. Growing water scarcity, food security, and government responses in China. Global Food Security, 2017, 14:9–17. Liu Siwen, Huang Yuanying, Zhao Wenbo, et al. Water Quality and Health Risk Assessment in the Northern Part of the Huangpi River Basin in Ganbei, Jiangxi Province, Rock and Mineral Test. Environmental Chemistry, 2022, 41(3): 488–498. Sun Qifa, Yang Ke, Sun Zhuo'an, et al. Groundwater Quality Characteristics in the Changchun New District and Its Evaluation of Ecological Health. Geological Science of China, 2022, 49(3): 834–848. Chen Renxiang, Gao Yang, Song Yong, et al. Groundwater Quality Characteristics and Health Risk Assessment in the Zudong Rare Earth Mine Area of Longnan. Nonferrous Metals (Mining Part), 2021, 73(3): 111–118. Li Shilong, Xiong Jianhua, Deng Chaobing, et al. Heavy Metal Pollution Status and Health Risk Assessment of Liujiang River in the Xijiang River Basin, Guangxi. Guangxi Science, 2018, 25(4): 393–399. Lin Jin, Liang Wenjing, Jiao Yang, et al. Ecological Health Risk Assessment of Heavy Metals in Farmland Soil around the Tongguan County Gold Mine Area in Shaanxi Province. Geological Science of China, 2021, 48(3): 749–763. Yang Yan, Yu Yunjiang, Wei Weiwei, et al. Health Risk Assessment of Heavy Metal Pollution in Shallow Groundwater for Urban and Suburban Residents in Changzhou City. Environmental Chemistry, 2013, 32(2): 202–211. Wu Wenhui, Zou Hui, Zhu Ganghui, et al. Characteristics of Groundwater Heavy Metal Pollution in a Mining Area in Central Hunan and Its Health Risk Assessment. Journal of Ecology and Rural Environment, 2018, 34(11): 1027–1033. She Wei, Jie Yucheng, Xing Hucheng, et al. Absorption and Enrichment Characteristics of Corchorus capsularis in Antimony Mining Areas in Lengshuijiang, Hunan. Journal of Agricultural and Environmental Sciences, 2010, 29(1): 91–96. Mo Changli, Wu Fengchang, Fu Zhiyou, et al. Preliminary Study on the Pollution Status of Antimony, Arsenic, and Mercury in Agricultural Soils in the Xikuangshan Antimony Mining Area, Hunan. Acta Mineralogica Sinica, 2013, 33(3): 344–350. Li Xuehua. Study on Ecological Risk Assessment and Restoration Technology of Sediments in Antimony Mining Areas. Beijing: Beijing Forestry University, 2013. Lei Ming, Zeng Min, Zheng Yuanming, et al. Heavy Metal Pollution and Potential Risk Assessment of Rice Soils in Mining and Smelting Areas in Hunan. Acta Scientiae Circumstantiae, 2008, 28(6): 1212–1220. Lv Bingxu. Study on Characteristics and Evaluation Methods of Heavy Metal Pollution in Groundwater in Metal Mines. Shijiazhuang: Shijiazhuang Institute of Economics, 2012. Xie Shurong, Peng Bo. Evaluation of Soil Heavy Metal Pollution in Xikuangshan Antimony Mine in Hunan. Yunnan Geographic Environment Research, 2007, 19(4): 128–132. She Wei, Jie Yucheng, Xing Hucheng, et al. Absorption and Enrichment Characteristics of Corchorus capsularis in Lengshuijiang Antimony Mining Area, Hunan. Journal of Agricultural and Environmental Sciences, 2010, 29(1): 91–96. Wang Jinzhe, Zhang Guanghui, Shen Jianmei, et al. Discussion on the Basis and Principles for the Selection of Evaluation Indicators for Groundwater Function. Hydrogeology and Engineering Geology, 2008, 35(2): 76–81. Bai Derong. Discussion on the Basis and Principles for the Selection of Evaluation Indicators for Groundwater Function. Inner Mongolia Science and Technology and Economy, 2009(22): 370–372. Aitiyeguli·Rexiti, Maimaituxun·Wang Weiwei, et al. Human Health Risk Assessment of Heavy Metal Pollution in the Bositeng Lake Basin Groundwater. Journal of Ecotoxicology, 2019, 14(2): 251–259. Guo Xingmei, Li Ning, Kang Yuan, et al. Health Risk Assessment of Heavy Metals in Rural Drinking Water in Foshan City. Journal of Jinan University (Natural Science & Medicine Edition), 2014, 35(1). Wang Xuemin. Misunderstanding of Skewness and Kurtosis Concepts. Statistics and Decision, 2008 (12): 145–146. Wang Wei, Zhang Xin, Hu Xiaotao. Evaluation of Groundwater Resources Carrying Capacity in Irrigation Districts Based on Principal Component Analysis. Journal of Water Resources and Architectural Engineering, 2010, 8(1): 5–6. Gao Weidong. Water Quality Evaluation of Mine Area Groundwater Based on Principal Component Analysis. Safety and Environmental Engineering, 2009, 16(1): 28–30. Xing Xuguang, Shi Wenjuan, Zhang Yidan, et al. Evaluation of Xi'an City's Groundwater Resources Carrying Capacity Based on Principal Component Analysis. Hydrology, 2013 (2): 35–38. Wang Haibo, Li Huping, Hou Junlin. Application of SPSS-based Principal Component Analysis in Groundwater Environmental Quality Evaluation - Taking Sanshangliang Area of Dalad Banner as an Example. Western Resources, 2012 (1): 89–91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Apr, 2024 Reviews received at journal 11 Apr, 2024 Reviewers agreed at journal 04 Apr, 2024 Reviews received at journal 17 Feb, 2024 Reviewers agreed at journal 14 Feb, 2024 Reviewers agreed at journal 14 Feb, 2024 Reviewers invited by journal 14 Feb, 2024 Editor assigned by journal 14 Feb, 2024 Editor invited by journal 14 Feb, 2024 Submission checks completed at journal 14 Feb, 2024 First submitted to journal 04 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3929847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":272976042,"identity":"5893465e-d3b8-486d-8057-8e1fef9aa6a3","order_by":0,"name":"Wenjie Hao","email":"","orcid":"","institution":"Center for Hydrogeology and Environmental Geology, CGS","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Hao","suffix":""},{"id":272976043,"identity":"a2eedb21-c82c-4a36-9703-8a667e2bbd6e","order_by":1,"name":"Huan Liu","email":"","orcid":"","institution":"Qingdao Geological and Mineral Geotechnical Engineering Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Liu","suffix":""},{"id":272976044,"identity":"23179cd6-c1c3-4034-970b-83442b947ebc","order_by":2,"name":"Shuli Hao","email":"","orcid":"","institution":"Center for Hydrogeology and Environmental Geology, CGS","correspondingAuthor":false,"prefix":"","firstName":"Shuli","middleName":"","lastName":"Hao","suffix":""},{"id":272976045,"identity":"7e948730-4286-44d7-95bf-6d312fd7e518","order_by":3,"name":"Kuanzhen Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACNmbm4x8kDGzs+NmbDxCnhY+9LY3BoiAtWbLnWAJxWuR4zpgxVHw4zLhhRo4BkQ6TSDB7cMPgMLMBQ87HG28Y7OR0GwhrSTecYZDOZ85wdrPlHIZkY7MDhLUckJYwsGa2bOzdJs3DcCBxG2EtiQ3SfwyYGTcc5nlGpBaew2wSEgbOjBuO8bARqYW9jdlAwgAUyGzGlnMMiPCLfDP/xwcSf4BRKf/44Y03FXZyBLWgAAkeIqMGWQupOkbBKBgFo2BEAADq0T6ZcEQlTwAAAABJRU5ErkJggg==","orcid":"","institution":"Qingdao Geological and Mineral Geotechnical Engineering Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Kuanzhen","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2024-02-05 04:16:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3929847/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3929847/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51214360,"identity":"4539d192-2fbe-4fa4-9f3e-f648b0ae1d11","added_by":"auto","created_at":"2024-02-16 06:46:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132925,"visible":true,"origin":"","legend":"\u003cp\u003eThe sampling point distribution in the study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/64b1227975b8912cce4fcd10.jpg"},{"id":51214128,"identity":"b132d92a-8d67-4b9e-b648-4373735cbc0b","added_by":"auto","created_at":"2024-02-16 06:38:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":104339,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent Status of Mn Concentrations\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/cfc87e6ec1b68a8c012c1e6c.png"},{"id":51214126,"identity":"cd736df1-7758-4795-a7bc-24c8bfba33c4","added_by":"auto","created_at":"2024-02-16 06:38:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87026,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent Status of As Concentrations\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/e99f2edaa8dae505c302cac4.png"},{"id":51214129,"identity":"8c2e43b7-c036-4937-9dec-a4e1975fc9be","added_by":"auto","created_at":"2024-02-16 06:38:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97554,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent Status of Se Concentrations\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/4cf8d9aee75cefe48d5bb1ac.png"},{"id":51214130,"identity":"91434070-c8b5-450e-b019-7eb3fc13a3cc","added_by":"auto","created_at":"2024-02-16 06:38:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":71903,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent Status of Sb Concentrations\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/f76391e6ad131a788cce5ec7.png"},{"id":51214695,"identity":"448df782-b21e-4998-b248-92d3ce9da69c","added_by":"auto","created_at":"2024-02-16 06:54:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":998333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3929847/v1/982a09d8-6d3f-4412-898e-7c31e128ca41.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of Heavy Metal Contamination in Groundwater of Typical Mining Area in Hunan Province","fulltext":[{"header":"Introductory","content":"\u003cp\u003eThe Problem of Groundwater Pollution has become a major concern for governments, businesses, and the public\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. There is a growing awareness among people, and water quality safety has increasingly become an important issue for national sustainable development and ensuring public health \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Therefore, conducting relevant research holds significant importance for the ecological environment construction and protection of regional groundwater resources \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Metal mining areas are one of the main sources of groundwater pollution, characterized by severe heavy metal contamination, which exhibits high toxicity, slow degradation, and ease of accumulation. Heavy metals such as Pb, Cr, Hg, Se, Mn, among others, possess strong toxicity. The cumulative effect of these heavy metal elements in the human body poses significant potential risks to the health of regional populations, causing severe harm to human organs \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The main source of these heavy metals is the mining activities that generate enormous value. The resulting tailings, dust, waste rocks, and wastewater from mining operations can lead to the diffusion and infiltration of heavy metals into soil and groundwater, causing severe pollution to groundwater bodies, thus resulting in irreparable harm to human health \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe antimony mining area in the study region has a wide variety of mineral deposits, resulting in a diverse range of heavy metal pollutants. This research aims to provide relevant personnel with a better understanding of the groundwater pollution situation in the mining area. It serves as a scientific basis for the prevention and control of groundwater pollution in the mining area and the management of public health risks. Consequently, effective measures can be formulated to reduce the health risks posed to residents.\u003c/p\u003e "},{"header":"Overview of the Study Area","content":"\u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003e1.1 Socioeconomic Overview of the Study Area\u003c/h2\u003e \u003cp\u003eThe investigation area is situated in the northwest of Lengshuijiang City, Hunan Province, encompassing the Mining Township, the Xikuangshan Administration, and the border area of Zhonglian Township. It lies approximately 13 kilometers south of Lengshuijiang City, with geographical coordinates ranging from 111\u0026deg;25\u0026prime;47\u0026Prime; to 111\u0026deg;31\u0026prime;22\u0026Prime; east longitude and 27\u0026deg;49\u0026prime;28\u0026Prime; to 27\u0026deg;43\u0026prime;05\u0026Prime; north latitude. The antimony mining industry in this region began production in 1897 and has a history of over a century. Mineral production and processing serve as the primary economic sources for the local government and residents, earning the area the titles of \"World Capital of Antimony\" and \"Coal Sea of Jiangnan\" \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWithin this mining area, two large ore deposits, one medium-sized ore deposit, and three small-sized ore deposits have been identified. The antimony mineral field in the northern Xikuangshan area is the concentration zone for antimony mineral production, with an accumulated confirmed reserve of 26.265\u0026nbsp;million tons (metal reserve of 855,202 tons). The reserve is exceptionally abundant, ranking first in the nation and representing the world's largest antimony mineral field\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The total population residing in the mining area is approximately 17,100, comprising approximately 15,000 urban residents and around 2,100 rural villagers \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOver the past hundred years of mining operations in the Xikuangshan antimony mining area, the substantial generation of \"three wastes\"\u0026mdash;waste gas, waste residue, and waste water\u0026mdash;has caused severe environmental damage to local soil and groundwater. Consequently, it has posed varying degrees of health risks to the local residents\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. As the subject of this investigation, the Xikuangshan mining area demonstrates typicality and representativeness, thus offering outcomes of considerable representational significance in the research.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Hydrogeological Conditions\u003c/h3\u003e\n\u003cp\u003e1.2.1 Fault Zone Permeability and Aquifer Characteristics\u003c/p\u003e \u003cp\u003eThe fault zones within the area, including the F75, F72, F3 of the North-Northeast group, and the F19, F17, F104 of the North-Northwest group, constitute significant and mechanically similar normal faults within the ore deposit. Due to the surrounding rocks being predominantly composed of argillaceous limestone, sandstone, and shale, the development of karstification is not favored. The fractured zones within the fault zones consist mainly of fractured mudstones, sandstones, and limestone blocks with a dense cementation, which impedes groundwater movement and storage. Drilling observations within these fault zones did not reveal any water seepage. Water injection tests conducted via boreholes demonstrated minimal water injection rates (0.0014L/S\u0026middot;m), particularly within the main F75 fault, where 14 cross veins in the midsections of tunnels 7, 9, 11, and 13 displayed dry fault surfaces without any indications of groundwater activity. These fault zones are entirely filled with fragmented rocks such as sandstone, limestone, and shale. However, smaller secondary fault zones and fissures were observed in the tunnel exposures, facilitating water storage within the ore deposit, primarily channeling fractured water from silicified limestone and silicified rock zones. These secondary faults trend in the northeast and northwest directions, with an inclination exceeding 60\u0026deg;, extending several meters to tens of meters in length, and possessing a maximum width of approximately 1.5 meters. The predominant lithology comprises silicified limestone, followed by limestone, with flow rates ranging from 0.01 to 0.5L/S. Based on the above description, the fault zones within this mining area exhibit relatively poor water-bearing and water-conducting properties.\u003c/p\u003e \u003cp\u003e1.2.2 Groundwater Flow Status\u003c/p\u003e \u003cp\u003eThe Xikuangshan mining area is situated at the hydrological watershed of the aquifer system. To the east, the Yunxi biotite granite forms a hydrological barrier, while the F75 fault acts as the western hydrological boundary. Consequently, it creates a north-south flowing aquifer unit, effectively isolating the ore deposit from the regional groundwater. Moreover, a local hydrological watershed exists in the central part of the study area, positioned at Qilijiang, resulting in independent flow divisions between the northern and southern mines. Additionally, surface water bodies distributed within the ore deposit are all relatively small.\u003c/p\u003e \u003cp\u003eAs a result, the groundwater in the ore deposit primarily relies on infiltration from atmospheric precipitation for recharge. Natural features such as exposed rock cavities, fractures, and old workings become recharge zones for the aquifer. A few unclosed boreholes and surface silicified limestone fractured zones also contribute to the aquifer recharge. Consequently, the overall recharge area of the aquifer is relatively limited.\u003c/p\u003e \u003cp\u003eThe groundwater flow in the aquifer predominantly exists in the form of limestone fissures, karst caves, interlayer fractured zones, and fractured fissures in silicified limestone. Generally, the groundwater flows from the Qilijiang watershed in the central part of the study area, discharging southwards and northwards from the study area. The runoff distance is relatively short. The primary discharge method is in the form of springs, with only a minimal portion discharged through faults. Extensive mining activities in the Xikuangshan area have led to the formation of artificial discharge zones in deeper mining regions.\u003c/p\u003e \u003cp\u003eThe dynamics of the ore deposit groundwater are primarily characterized by significant fluctuations in water level and quantity. Since it is predominantly replenished by rainfall, variations in groundwater dynamics are closely related to precipitation. Seasonal changes are evident, with groundwater levels rapidly rising during the rainy season, leading to a sharp increase in flow rates. For instance, observation at Spring No. 7 recorded a flow rate of 2.129L/S before rainfall, surging to 14.13L/S within the initial 30 minutes of rainfall. The monthly amplitude of groundwater levels is generally around 2.40 meters, with an average annual amplitude of 7.6 meters.\u003c/p\u003e \u003cp\u003eMining activities in the area have moderately impacted the underground aquifer due to groundwater extraction. However, water scarcity has not been observed, and there have been minimal occurrences of wells or springs running dry. Overall, the impact on regional groundwater balance remains relatively slight.\u003c/p\u003e"},{"header":"Sample Collection and Testing Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Principles of Sampling Point Layout\u003c/h2\u003e \u003cp\u003eBased on the \"Technical Specifications for Groundwater Environmental Monitoring\" (HJ164-2020), \"Technical Guidelines for Site Environmental Monitoring\" (HJ25.2-2014), \"Sanitary Standards for Drinking Water\" (GB5749-2006), \"Groundwater Quality Standards\" (DZ/T0290-2017), as well as the \"Technical Regulations for National Integrated Water Resources Planning,\" the following principles for sample point layout were formulated:\u003c/p\u003e \u003cp\u003e(1) The southern and northern mining areas belong to different hydrogeological units. Accordingly, sampling points were arranged based on the distinct hydrogeological conditions of these independent units.\u003c/p\u003e \u003cp\u003e(2) Considering the distribution of pollution sources such as leachate from mine waste, ore washing wastewater, and pit drainage, water quality monitoring points were set up at the groundwater recharge, flow, and discharge sections in each hydrogeological unit of the southern and northern areas. The spacing between points generally ranged from 150 to 200 meters.\u003c/p\u003e \u003cp\u003e(3) In addition to the existing monitoring wells, domestic wells, and springs within the mining area, additional groundwater quality monitoring points were selectively added and adjusted based on the actual exposure of groundwater and the conditions of recharge, flow, and discharge. The placement of monitoring points was adjusted according to the specific conditions, avoiding uniform distribution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Layout of Monitoring Network\u003c/h3\u003e\n\u003cp\u003e(1) The controlled area of the groundwater quality pollution monitoring network within the mining area covers an area of 30km\u0026sup2;.\u003c/p\u003e \u003cp\u003e(2) A total of 26 groundwater quality pollution monitoring points were established within the mining area.\u003c/p\u003e \u003cp\u003eIn this study, a total of 26 groundwater sampling points were collected within the research area. Their distribution is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Principles for Selecting Heavy Metal Evaluation Indicators\u003c/h2\u003e \u003cp\u003eMultiple characterization factors need to be considered to achieve the groundwater functional assessment's objective. However, the selection of characterization indicators varies widely, and it's not advisable to mechanically or indiscriminately include all factors in the evaluation index system. Doing so might dilute the role of dominant factors, affecting the accuracy of the assessment results. Therefore, it's essential to make an optimal selection of evaluation factors based on principles such as: the principle of dominance, measurability, operability, comprehensiveness, flexibility, among others.\u003c/p\u003e \u003cp\u003eThe mineral composition in the study area is relatively straightforward, with relatively simple chemical components. Apart from the primary element antimony (Sb), associated elements include As, Hg, Ag, Cu, Pb, Sn, V, Zn, Mo, Ga, and B. Following the classification system established by the International Agency for Research on Cancer (IARC) and the World Health Organization (WHO) for assessing the reliability of the carcinogenicity of chemical substances, essential trace elements for the human body in the associated elements within the study area include Mo, Co, Mn; harmful substances comprise As, Ba, Sb; carcinogenic substances include As, non-carcinogenic substances consist of Mn, Se, and potentially carcinogenic substances encompass Co, Sb.\u003c/p\u003e \u003cp\u003eConsidering the aforementioned principles and the evaluation objectives of this study, the heavy metals Mo, As, Co, Mn, Ba, Sb, and Se were chosen as evaluation indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sampling and Testing Methods\u003c/h2\u003e \u003cp\u003e(1) Groundwater Sampling and Preservation\u003c/p\u003e \u003cp\u003eIn accordance with the \"Standard Examination Methods for Drinking Water\" (GB5749-2006, GB/T5750.1-5750.13-2006), containers were acid-washed or alkali-washed as specified before sampling, followed by rinsing with distilled water. The sampling instruments were rinsed with the source water at least three times before sampling. Non-disposable groundwater sampling equipment was used, requiring pre- and post-sampling cleaning. Wastewater generated during the cleaning process should be collected and disposed of properly. For sample bottles without preservatives, they were rinsed with the water to be sampled two to three times before groundwater sampling. Samples containing heavy metals such as As, Mn, Sb were collected in 250ml polyethylene plastic bottles, adjusted to pH less than 2 with nitric acid, and analyzed within 10 days. The frequency of this sampling was once and the sampling period was two days.\u003c/p\u003e \u003cp\u003e(2) Groundwater Sample Testing Methods\u003c/p\u003e \u003cp\u003eAdhering to the requirements of the \"Standard Examination Methods for Drinking Water\" (GB/T5750.6-2006) and \"Water Quality-Determination of Barium\" (HJ 602\u0026ndash;2011) for various indicators, different instruments were equipped to ensure the quality of sample analysis based on the quality level of different analysis methods. Main testing instruments included gas chromatography-mass spectrometer, atomic absorption spectrophotometer, and atomic fluorescence spectrometer, among others. The standard testing methods used for various detection components are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest method of heavy metal evaluation index factors for groundwater samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElemental\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethodologies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (6.1) Hydride atomic fluorescence method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (7.1) Hydride atomic fluorescence method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (19.1) Hydride atomic fluorescence method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (14.1) Flameless atomic absorption spectrophotometry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (13.1) Flameless atomic absorption spectrophotometry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGB/T 5750.6\u0026ndash;2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Test Methods for Drinking Water METAL INDICATORS (4.2) Flame atomic absorption spectrophotometry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHJ 602\u0026ndash;2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater quality Determination of barium Graphite Furnace Atomic Absorption Spectrophotometry\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"The Statistical Analysis of Heavy Metal Pollution in Underground Water of Mining Areas","content":" \u003cp\u003eBefore evaluating heavy metal pollution in underground water, it is essential to analyze the statistical characteristics and distribution patterns of indicator elements in the study area using statistical methods. Through statistical characteristic analysis, one can grasp the apparent features of heavy metal pollution in the underground water of the study area, offering the necessary basis for evaluating the results \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In this section, SPSS and Excel software were primarily employed to analyze the experimental detection data of selected heavy metal evaluation indicators, namely Mo, As, Co, Mn, Ba, Sb, and Se. The analysis mainly encompasses parameters like maximum value, minimum value, mean, variance, kurtosis, skewness, and coefficient of variation. This translation could be used for an academic paper.\u003c/p\u003e \u003cp\u003eThe minimum and maximum values represent extreme values in the data, indicating the degree of heterogeneity within the dataset. The median is a type of central tendency that describes the typical scenario within a set of data. The mean represents a measure of central tendency within a dataset and serves as an indicator reflecting the trend of the data set. In statistical work, the mean (average) and standard deviation are the two most important measures used to describe the trend and dispersion of data sets. Variance and standard deviation are used to measure the deviation of a random variable from its expected value and are often employed to study the deviation between mathematical variables and their mean in various scenarios. The coefficient of variation, similar to variance and standard deviation, primarily indicates the degree of dispersion within data, influenced by both the dispersion level of variables and the average level of variable values. The statistical characteristic values of the indicator elements in the study area can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This translation could be used in an academic paper.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Characteristics of Heavy Metal Indicator Factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e 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Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e221.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e111.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP.index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2991.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35293.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO.Std.index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: (1) CV (Coefficient of Variation) = (Standard Deviation / Mean) \u0026times; 100%; Standard values refer to the Drinking Water Standards (GB5749-2006);\u003c/p\u003e \u003cp\u003eP.index (Pollution Index)\u0026thinsp;=\u0026thinsp;Mean / Standard Value; O.Std.index (Exceedance Rate) = (Number of exceedances / Total monitored data points)\u0026times;100%\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of Standard Deviation Characteristics of Heavy Metal Evaluation Index Concentrations\u003c/h2\u003e \u003cp\u003eFrom the data analysis results, the standard deviations for Mn, As, Ba, Co, Mo, Sb, and Se are 0.024081, 0.22651, 0.0166, 0.000152, 0.002301, 3.910359, and 0.006, respectively. Among these, As and Sb have relatively large standard deviations, with their maximum concentrations being 22.67 and 1921.25 times the minimum concentrations, respectively. These results indicate a significant level of dispersion in the concentrations of As and Sb within the sampled water, suggesting a strong correlation with anthropogenic factors in the surrounding environment, where human influences predominantly affect these two index factors. In contrast, the variance for the other five ions is almost zero, indicating that these five ions are primarily influenced by natural factors, or a combination of natural and anthropogenic influences at relatively similar levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of Kurtosis and Skewness Characteristics of Heavy Metal Evaluation Index Concentrations\u003c/h2\u003e \u003cp\u003eUnder natural conditions, the concentration curves of heavy metal ions generally conform to a normal distribution. Kurtosis and skewness can reflect whether the concentration of certain ions conforms to or deviates from the normal distribution, and the degree of deviation can generally indicate the influence of anthropogenic factors on these ions. Kurtosis \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e is a measure reflecting the shape of the distribution of a random variable. The kurtosis K of a random variable x is defined as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;E[\u003cem\u003ex\u003c/em\u003e-E(\u003cem\u003ex\u003c/em\u003e)]\u003csup\u003e4\u003c/sup\u003e /[Var(\u003cem\u003ex\u003c/em\u003e)]\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhen K\u0026thinsp;\u0026gt;\u0026thinsp;3, it represents a leptokurtic curve, indicating that the variable values are densely distributed around the mode.\u003c/p\u003e \u003cp\u003eWhen K\u0026thinsp;=\u0026thinsp;3, it matches the degree of sharpness of a normal distribution curve.\u003c/p\u003e \u003cp\u003eWhen K\u0026thinsp;\u0026lt;\u0026thinsp;3, it represents a platykurtic curve, suggesting a relatively uniform dispersion of variable values around the mode.\u003c/p\u003e \u003cp\u003eSkewness measures the degree and direction of the distribution's asymmetry and is a dimensionless value. The skewness S of a random variable x is defined as:\u003c/p\u003e \u003cp\u003e \u003cem\u003eS\u003c/em\u003e\u0026thinsp;=\u0026thinsp;E[\u003cem\u003ex\u003c/em\u003e-E(\u003cem\u003ex\u003c/em\u003e)]\u003csub\u003e3\u003c/sub\u003e/[Var(\u003cem\u003ex\u003c/em\u003e)]\u003csup\u003e3/2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhen S\u0026thinsp;\u0026gt;\u0026thinsp;0, the distribution of x is positively skewed.\u003c/p\u003e \u003cp\u003eWhen S\u0026thinsp;=\u0026thinsp;0, it is symmetric around the mode.\u003c/p\u003e \u003cp\u003eWhen S\u0026thinsp;\u0026lt;\u0026thinsp;0, the distribution of x is negatively skewed.\u003c/p\u003e \u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be observed that the kurtosis of Mn, Mo, and Sb are 12.278\u0026thinsp;\u0026gt;\u0026thinsp;3, 3.343\u0026thinsp;\u0026gt;\u0026thinsp;3, and 6.056\u0026thinsp;\u0026gt;\u0026thinsp;3, respectively, and the skewness values are 3.338\u0026thinsp;\u0026gt;\u0026thinsp;0, 1.84\u0026thinsp;\u0026gt;\u0026thinsp;0, and 2.54\u0026thinsp;\u0026gt;\u0026thinsp;0, respectively. Based on the analysis, it can be inferred that the concentrations of these three ions deviate from a normal distribution, indicating varying degrees of influence from anthropogenic factors, especially notable for Mn and Sb influenced by the surrounding anthropogenic environment. Meanwhile, As, Ba, Co, and Se exhibit kurtosis values of -1.591, -0.094, 1.395, and 1.662, respectively, with absolute values less than 3, suggesting platykurtic curves. The skewness values are 0.232, 0.987, 1.617, and 1.727, all greater than 0, indicating positive skewness. Similarly, these three factors are also influenced to varying degrees by anthropogenic factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of Statistical Characteristics of Heavy Metal Evaluation Index Concentrations\u003c/h2\u003e \u003cp\u003eFrom the perspective of the coefficient of variation, the degree of variability among the seven factors is in the following order: Sb\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Se\u0026thinsp;\u0026gt;\u0026thinsp;Mo\u0026thinsp;\u0026gt;\u0026thinsp;Co\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Ba. Particularly, the coefficients of variation for As, Se, and Sb exceed 100%, even reaching 200%. The pollution indices of the heavy metal indicator factors are as follows, in descending order: Sb\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Se\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;Mo\u0026thinsp;\u0026gt;\u0026thinsp;Co. Notably, the pollution index for As reaches 2991%, while Sb is exceptionally high at 35293.2%. From the standpoint of the mean values, antimony and arsenic exhibit severely polluted states. The Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the exceedance rates for As and Sb are 100%, while for Se and Mn, they are 20% and 3.85%, respectively. The exceedance rate for the remaining three indicator factors is 0%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of Pollution Characteristics of Heavy Metal Exceedances\u003c/h2\u003e \u003cp\u003eTo visually depict the pollution status of each sampling point's water samples in both groundwater and surface water for indicators showing exceedance rates, we have generated concentration status diagrams for the prevailing concentrations of Mn, As, Sb, and Se, which have non-zero exceedance rates. These diagrams illustrate a comparison between the concentrations of each exceedance factor against the standard values at each sampling point and depict the contrast among the sampling points, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to the Drinking Water Standard (GB5749-2006), the standard concentration for Mn is 0.1 mg/L. From Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it's evident that only Spring 22 in Chuanshan Village of the mining area and surface water points DB1 and DB4 in Dongxia Village of the mining area and Shengli Village in Zhonglian Township respectively, exceed the standard concentration for Mn. Furthermore, it's notable that the Mn content in surface water is generally higher than in groundwater. Essentially, only point 22 exceeds the average Mn concentration in surface water, and only three groundwater sampling points exhibit Mn concentrations higher than those in surface water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure 3 illustrates that both surface water and groundwater within the study area exhibit arsenic (As) concentrations higher than the standard value of 0.01 mg/L for drinking water. Notably, the surface water point DB2 in Tanjia Community, Minshan Town, records an alarming concentration of 11.93 mg/L.\u003c/p\u003e \u003cp\u003eRegarding antimony (Sb), after handling the outliers, spring water point 13 north of Minshan Hospital in Taotang Street, Minshan Town, was identified as an outlier and removed. Drainage from hospital facilities such as treatment rooms, laboratories, wards, laundry rooms, X-ray imaging, isotope therapy diagnostic rooms, and operating theaters intensifies the antimony pollution in the groundwater in that area, potentially compromising the accuracy of the water pollution assessment in the research area. This particular data point has been considered an outlier and excluded from the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates that the concentrations of Sb in all sampling points within the research area exceed the drinking water standard value of 0.005 mg/L, indicating severe antimony pollution. The minimum recorded concentration surpasses 0.0084 mg/L, more than 1.68 times the drinking water standard. Moreover, the Sb concentrations in four surface water sampling points across the research area are consistently higher than the median Sb concentration in groundwater.\u003c/p\u003e \u003cp\u003eThe standard value for selenium (Se) in drinking water is 0.01 mg/L in Fig.\u0026nbsp;5, and the exceedance rate at the groundwater monitoring points in the surveyed area is 20%. Water from the Qilijiang Iron Mine (11), in direct contact with the mine area, exhibits significant pollution with a concentration of 0.047 mg/L, considered an outlier. After removing this outlier, the analysis reveals some sampling points with selenium concentrations at 0 mg/L, while the maximum concentration reaches 0.021 mg/L, twice the standard value, suggesting a distinctly regional pattern of pollution. Overall, the analysis indicates that arsenic and antimony pollution is most severe in the groundwater of the study area, followed by selenium and manganese, with minimal contamination from barium, cobalt, and molybdenum.\u003c/p\u003e \u003c/div\u003e "},{"header":"Principal Component Analysis of Heavy Metal Elements","content":"\u003cp\u003eThe abundant variety of mineral resources in the study area leads to a multitude of factors affecting the groundwater quality in this region. This paper utilizes SPSS software to conduct a principal component analysis on selected indicator factors. This approach, while minimizing data loss, involves linear combinations and discarding minor details, replacing multidimensional variables with a few comprehensive ones. This method aims to reduce workload while ensuring an accurate assessment of the groundwater quality in the study area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principles of Principal Component Analysis\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis (PCA) \u003csup\u003e[\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e is a statistical method for dimensionality reduction, aiming to quantitatively study multiple-dimensional factors within the same system. The fundamental concept lies in the assumption that among numerous correlated factors, there are dominant common factors. By analyzing the internal structural relationships within the correlation matrix of the original variables, this method identifies representative and targeted indicators, facilitating their mutual comparison. Additionally, it objectively determines the respective weight values, effectively capturing the main contradictions and significantly reducing subjectivity. In the evaluation of heavy metal pollution in groundwater, PCA proves highly persuasive, offering a robust mathematical model.\u003c/p\u003e \u003cp\u003e \u003cem\u003eF1\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ea11\u003c/em\u003eZX1\u0026thinsp;+\u0026thinsp;\u003cem\u003ea21\u003c/em\u003eZX2+\u0026hellip;+\u003cem\u003ean1\u003c/em\u003eZXn\u003c/p\u003e \u003cp\u003e \u003cem\u003eF2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ea12\u003c/em\u003eZX1\u0026thinsp;+\u0026thinsp;\u003cem\u003ea22\u003c/em\u003eZX2+\u0026hellip;+\u003cem\u003ean2\u003c/em\u003eZXn\u0026bdquo;\u0026bdquo;\u0026bdquo; (4.1)\u003c/p\u003e \u003cp\u003e \u003cem\u003eFm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ea1m\u003c/em\u003eZX1\u0026thinsp;+\u0026thinsp;\u003cem\u003ea2m\u003c/em\u003eZX2+\u0026hellip;+\u003cem\u003eanm\u003c/em\u003eZXn\u003c/p\u003e \u003cp\u003eComposite evaluation function:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{F}=\\frac{{{\\lambda }}_{1}}{{{\\lambda }}_{1}+{{\\lambda }}_{2}+\\dots {{\\lambda }}_{\\text{p}}}{\\text{F}}_{1}+\\frac{{{\\lambda }}_{2}}{{{\\lambda }}_{1}+{{\\lambda }}_{2}+\\dots {{\\lambda }}_{\\text{p}}}{\\text{F}}_{2}+\\frac{{{\\lambda }}_{\\text{p}}}{{{\\lambda }}_{1}+{{\\lambda }}_{2}+\\dots {{\\lambda }}_{\\text{p}}}{\\text{F}}_{\\text{p}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4.2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere anm represents the eigenvectors corresponding to the eigenvalues of the covariance matrix of the original variable matrix X; ZX1, ZX2, ZXn are the standardized values of the original variable matrix X; λ1, λ2, ..., λp represent the eigenvalues of the ZX matrix; n denotes the number of factors; m represents the number of samples; p stands for the number of principal components.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Results and Analysis\u003c/h2\u003e \u003cp\u003eThe analysis results of heavy metal indicator factors in water samples at sampling points using SPSS are shown in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation index system correlation coefficient matrix eigenvalues and contribution rate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eingredient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eInitial eigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eExtracted principal components\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003econtribution rate %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative contribution %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eeigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003econtribution rate %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCumulative contribution %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e76.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, when the cumulative contribution rate is greater than or equal to 75%, three main components can be extracted. The eigenvalue of the first principal component is 2.902, the second principal component is 1.306, and the third principal component is 1.119. The contribution rate of each eigenvalue represents the weight of each principal component, i.e., λ1\u0026thinsp;=\u0026thinsp;41.463%, λ2\u0026thinsp;=\u0026thinsp;18.65%, λ3\u0026thinsp;=\u0026thinsp;15.979%. Therefore, three principal components are extracted, and a principal component matrix is established.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal Component Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePrincipal Components\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe initial factor loading matrix displays the correlation coefficient values of each principal component with its corresponding variables. In the principal component matrix, dividing each column vector of the mth component by the square root of the mth eigenvalue provides the coefficients corresponding to each indicator for the mth principal component, i.e., the eigenvectors a1, a2, and a3, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvectors of the Correlation Matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvectors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eX4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eX6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eX7\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe eigenvectors obtained from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e were multiplied by the standardized variables of the seven original factors to obtain the principal components.\u003c/p\u003e \u003cp\u003eF1=-0.177*ZMn\u0026thinsp;+\u0026thinsp;0.413*ZAs\u0026thinsp;+\u0026thinsp;0.342*ZBa\u0026thinsp;+\u0026thinsp;0.21*ZCo\u0026thinsp;+\u0026thinsp;0.387*ZMo\u0026thinsp;+\u0026thinsp;0.493*ZSb\u0026thinsp;+\u0026thinsp;0.493*ZSe\u003c/p\u003e \u003cp\u003eF2\u0026thinsp;=\u0026thinsp;0.073*ZMn+-0.47*ZAs\u0026thinsp;+\u0026thinsp;0.656*ZBa+-0.104*ZCo+-0.485*ZMo\u0026thinsp;+\u0026thinsp;0.295*ZSb\u0026thinsp;+\u0026thinsp;0.095*ZSe\u003c/p\u003e \u003cp\u003eF3\u0026thinsp;=\u0026thinsp;0.579*ZMn+-0.012*ZAs+-0.063*ZBa+-0.705*ZCo\u0026thinsp;+\u0026thinsp;0.258*ZMo\u0026thinsp;+\u0026thinsp;0.056*ZSb\u0026thinsp;+\u0026thinsp;0.304*ZSe\u003c/p\u003e \u003cp\u003eThe comprehensive assessment index was calculated from the principal component functions and their corresponding eigenvalues, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal Component Analysis Results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling Point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst Principal Component F1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond Principal Component F2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThird Principal Component F3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComprehensive Principal Component F\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(1) According to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the eigenvalues of the first, second, and third principal components are 2.902, 1.306, and 1.119 respectively. The contribution rates of the principal components are 41.463%, 18.650%, and 15.979% respectively, accumulating to 76.093%. This indicates that these three principal components essentially encompass the influencing factors of heavy metal pollution in the groundwater of the research area. The first principal component, in particular, contains more information and exhibits a stronger correlation with water quality.\u003c/p\u003e \u003cp\u003e(2) From Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it can be observed that the first principal component a1 has a higher correlation of 0.493 with the original variables Sb and Se. This suggests that these two factors are the primary contributors to this type of pollution, while the pollution contribution of the other five indicators is relatively minor. The second principal component a2 shows a strong positive correlation of 0.656 with the original variable Ba and a significant negative correlation of -0.485 with Mo, indicating a relatively high contribution of Ba and Mo to this principal component. The third principal component a3 has a positive correlation of 0.579 with the original variable Mn and a strong negative correlation of -0.705 with Co, suggesting a significant contribution of Mn and Co to a3.\u003c/p\u003e \u003cp\u003e(3) Based on the comprehensive principal component score F, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e is obtained. A higher F value indicates more severe heavy metal pollution. Among these, sampling points 11, 12, and15 rank in the top three, signifying the most severe heavy metal pollution at these locations. On the other hand, sampling points 25, 22, and 26 rank at the bottom three, indicating the least polluted water samples among the research sampling points.\u003c/p\u003e \u003cp\u003e(4) The distribution of heavy metal pollution shows that the less polluted monitoring points 26, 22 and 25 are located in the upstream area of the groundwater, and the most seriously polluted points are mainly distributed in the middle of the mining area or the downstream area of the groundwater. It means that the background value of heavy metal ions in the investigation area is small, and the heavy metal ions in the mining area and the surrounding groundwater mainly come from the mining of tin, antimony and iron ore, and the mining of minerals is the main source of heavy metal ions in the groundwater.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e(1) Among the evaluation indicator factors of groundwater monitoring points in the research area, the standard deviation analysis reflects that As and Sb are more sensitive to the influence of mining activities. Mn, Se, Mo, Co, and Ba are primarily influenced by natural geological and hydrogeological conditions, with less impact from human activities. Skewness and kurtosis values of heavy metal evaluation indicators reveal varying degrees of anthropogenic environmental impact on the seven factors. The pollution sources of Mn, Mo, and Sb are relatively concentrated, while those of As, Ba, Co, and Se are more dispersed. The exceedance rate for As and Sb reaches 100%, with Se and Mn exceeding by 20% and 3.85%, respectively, while the exceedance rate for the remaining three indicator factors is 0.\u003c/p\u003e \u003cp\u003e(2) The severity of pollution in the research area's groundwater is highest for As and Sb, followed by Se and Mn. In contrast, Ba, Co, and Mo's pollution levels among these three heavy metals have not significantly surpassed drinking water standards.\u003c/p\u003e \u003cp\u003e(3) Through principal component analysis, the first principal component a1 exhibits a higher correlation of 0.493 with the original variables Sb and Se. The second principal component a2 shows a strong positive correlation of 0.656 with the original variable Ba and a notable negative correlation of -0.485 with Mo. The third principal component a3 displays a positive correlation of 0.579 with the original variable Mn and a strong negative correlation of -0.705 with Co.\u003c/p\u003e \u003cp\u003e(4) Analysis using SPSS software computed the strength of correlation between various indicator heavy metal factors and other factors in the following order: Se\u0026thinsp;\u0026gt;\u0026thinsp;Mo\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Sb\u0026thinsp;\u0026gt;\u0026thinsp;Ba\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Co, with correlation values of 0.344, 0.303, 0.241, 0.238, 0.228, 0.137, 0.119 respectively. This indicates a stronger correlation between Se and Mo concentrations and other heavy metals in the research area, while Mn and Co exhibit lower correlation with other influencing factors. It suggests that Mn and Co concentrations demonstrate relatively independent distributions and generally exhibit a negative correlation with other indicator factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflicts of Interest\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the National Key R\u0026amp;D Program of China (Grant No.2019YFC1509605), the Open Project Program of Hebei Center for Ecological and Environmental Geology Research (No. JSYF-202302).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWriting\u0026mdash;original draft preparation, W.H.; writing\u0026mdash;review and editing, K.M. and W.H.; methodology, H.L.; visualization, S.H.; data curation, K.M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data have been explained in the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJiang Wanjun, Meng Lishan, Liu Futian, et al. Current Status and Development, Utilization, and Protection Suggestions of Groundwater Resources and Environmental Quality in Zhangjiakou Area. North China Geology, 2022, 45(3): 44\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Shengfeng, Huang Jun, Zhou Yuan, et al. Investigation of Groundwater Quality and Health Risk Assessment in Rong County, Guangxi. People's Pearl River, 2019, 40(12): 6\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Liu G, Liu H, et al. Multivariate Statistical Evaluation of Dissolved Trace Elements and Water Quality Assessment in the Middle Reaches of Huaihe River, Anhui, China. Science of the Total Environment, 2017, 583: 421\u0026ndash;431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Li Y, Huang J, et al. Growing water scarcity, food security, and government responses in China. Global Food Security, 2017, 14:9\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Siwen, Huang Yuanying, Zhao Wenbo, et al. Water Quality and Health Risk Assessment in the Northern Part of the Huangpi River Basin in Ganbei, Jiangxi Province, Rock and Mineral Test. Environmental Chemistry, 2022, 41(3): 488\u0026ndash;498.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Qifa, Yang Ke, Sun Zhuo'an, et al. Groundwater Quality Characteristics in the Changchun New District and Its Evaluation of Ecological Health. Geological Science of China, 2022, 49(3): 834\u0026ndash;848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Renxiang, Gao Yang, Song Yong, et al. Groundwater Quality Characteristics and Health Risk Assessment in the Zudong Rare Earth Mine Area of Longnan. Nonferrous Metals (Mining Part), 2021, 73(3): 111\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Shilong, Xiong Jianhua, Deng Chaobing, et al. Heavy Metal Pollution Status and Health Risk Assessment of Liujiang River in the Xijiang River Basin, Guangxi. Guangxi Science, 2018, 25(4): 393\u0026ndash;399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin Jin, Liang Wenjing, Jiao Yang, et al. Ecological Health Risk Assessment of Heavy Metals in Farmland Soil around the Tongguan County Gold Mine Area in Shaanxi Province. Geological Science of China, 2021, 48(3): 749\u0026ndash;763.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Yan, Yu Yunjiang, Wei Weiwei, et al. Health Risk Assessment of Heavy Metal Pollution in Shallow Groundwater for Urban and Suburban Residents in Changzhou City. Environmental Chemistry, 2013, 32(2): 202\u0026ndash;211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Wenhui, Zou Hui, Zhu Ganghui, et al. Characteristics of Groundwater Heavy Metal Pollution in a Mining Area in Central Hunan and Its Health Risk Assessment. Journal of Ecology and Rural Environment, 2018, 34(11): 1027\u0026ndash;1033.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShe Wei, Jie Yucheng, Xing Hucheng, et al. Absorption and Enrichment Characteristics of Corchorus capsularis in Antimony Mining Areas in Lengshuijiang, Hunan. Journal of Agricultural and Environmental Sciences, 2010, 29(1): 91\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMo Changli, Wu Fengchang, Fu Zhiyou, et al. Preliminary Study on the Pollution Status of Antimony, Arsenic, and Mercury in Agricultural Soils in the Xikuangshan Antimony Mining Area, Hunan. Acta Mineralogica Sinica, 2013, 33(3): 344\u0026ndash;350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Xuehua. Study on Ecological Risk Assessment and Restoration Technology of Sediments in Antimony Mining Areas. Beijing: Beijing Forestry University, 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei Ming, Zeng Min, Zheng Yuanming, et al. Heavy Metal Pollution and Potential Risk Assessment of Rice Soils in Mining and Smelting Areas in Hunan. Acta Scientiae Circumstantiae, 2008, 28(6): 1212\u0026ndash;1220.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv Bingxu. Study on Characteristics and Evaluation Methods of Heavy Metal Pollution in Groundwater in Metal Mines. Shijiazhuang: Shijiazhuang Institute of Economics, 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Shurong, Peng Bo. Evaluation of Soil Heavy Metal Pollution in Xikuangshan Antimony Mine in Hunan. Yunnan Geographic Environment Research, 2007, 19(4): 128\u0026ndash;132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShe Wei, Jie Yucheng, Xing Hucheng, et al. Absorption and Enrichment Characteristics of Corchorus capsularis in Lengshuijiang Antimony Mining Area, Hunan. Journal of Agricultural and Environmental Sciences, 2010, 29(1): 91\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Jinzhe, Zhang Guanghui, Shen Jianmei, et al. Discussion on the Basis and Principles for the Selection of Evaluation Indicators for Groundwater Function. Hydrogeology and Engineering Geology, 2008, 35(2): 76\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai Derong. Discussion on the Basis and Principles for the Selection of Evaluation Indicators for Groundwater Function. Inner Mongolia Science and Technology and Economy, 2009(22): 370\u0026ndash;372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAitiyeguli\u0026middot;Rexiti, Maimaituxun\u0026middot;Wang Weiwei, et al. Human Health Risk Assessment of Heavy Metal Pollution in the Bositeng Lake Basin Groundwater. Journal of Ecotoxicology, 2019, 14(2): 251\u0026ndash;259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Xingmei, Li Ning, Kang Yuan, et al. Health Risk Assessment of Heavy Metals in Rural Drinking Water in Foshan City. Journal of Jinan University (Natural Science \u0026amp; Medicine Edition), 2014, 35(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Xuemin. Misunderstanding of Skewness and Kurtosis Concepts. Statistics and Decision, 2008 (12): 145\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Wei, Zhang Xin, Hu Xiaotao. Evaluation of Groundwater Resources Carrying Capacity in Irrigation Districts Based on Principal Component Analysis. Journal of Water Resources and Architectural Engineering, 2010, 8(1): 5\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Weidong. Water Quality Evaluation of Mine Area Groundwater Based on Principal Component Analysis. Safety and Environmental Engineering, 2009, 16(1): 28\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing Xuguang, Shi Wenjuan, Zhang Yidan, et al. Evaluation of Xi'an City's Groundwater Resources Carrying Capacity Based on Principal Component Analysis. Hydrology, 2013 (2): 35\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Haibo, Li Huping, Hou Junlin. Application of SPSS-based Principal Component Analysis in Groundwater Environmental Quality Evaluation - Taking Sanshangliang Area of Dalad Banner as an Example. Western Resources, 2012 (1): 89\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Groundwater, Heavy metals, Statistical characterization, Pollution contribution, Correlation","lastPublishedDoi":"10.21203/rs.3.rs-3929847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3929847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper mainly carries out the research work by analysing and testing the groundwater quality in typical mining areas, based on the hydrogeochemical investigation data of groundwater monitoring points, and analyses the statistical characteristics of different index factors in antimony mining areas by using Spss and Surfer software. Firstly, using the standard deviation feature analysis, it is found that mining is more sensitive to the impact of As and Sb, and the degree of its pollution is the most serious, and the exceeding rate reaches 100%; later, the results of the analysis through the principal component analysis method show that the first principal component a1 has a higher correlation with Sb and Se in the original variables, the second principal component a2 has a stronger positive correlation with Ba in the original variables, and a stronger negative correlation with Mo, and the third principal component a3 has a stronger positive correlation with Mn in the original variables, and the negative correlation between Co and the third principal component is stronger. Finally, the correlation between each heavy metal indicator factor and the other six indicator factors was calculated by Spss software analysis to find out the pattern of the order of strength of the correlation of the indicator factors, and it was concluded that the concentration of Se and Mo had a stronger correlation with the other heavy metals in the study area, whereas the correlation of Mn and Co with the other influencing factors was lower. This paper summarises and refines the statistical characteristics of groundwater hydrogeochemistry in the mining area to provide reference for the diagnosis, prevention and control of groundwater pollution risk in this and similar mining areas.\u003c/p\u003e","manuscriptTitle":"Characterization of Heavy Metal Contamination in Groundwater of Typical Mining Area in Hunan Province","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 06:38:08","doi":"10.21203/rs.3.rs-3929847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-12T05:49:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-11T09:08:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4c046b65-52f4-4878-bf6e-56730b3b57b0","date":"2024-04-04T04:05:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-17T14:57:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129acb3f-b8e8-4c34-9f38-a5abef8014fc","date":"2024-02-15T04:46:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"55c0edbb-0719-4e7e-9182-373bf3066c35","date":"2024-02-14T14:15:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-14T13:47:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-14T13:46:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-14T10:32:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-14T10:12:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-05T03:59:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0eba44f5-bf48-49d7-bcb8-14448dc795fc","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28774038,"name":"Earth and environmental sciences/Environmental sciences"},{"id":28774039,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2024-05-29T07:12:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-16 06:38:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3929847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3929847","identity":"rs-3929847","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}