Spatial distribution, sources and health risk assessment of heavy metals in shallow groundwater in a typical coal mining area in Huainan, China

Spatial distribution, sources and health risk assessment of heavy metals in shallow groundwater in a typical coal mining area in Huainan, China | 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 Research Article Spatial distribution, sources and health risk assessment of heavy metals in shallow groundwater in a typical coal mining area in Huainan, China Lei Han, Jie Ma, Qimeng Liu, Yu Liu, Hongbao Dai, Cancan Wu, Hao Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6247727/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The availability of uncontaminated groundwater is of pivotal significance for the sustainable sustenance of human development. High concentrations of heavy metals in groundwater can pose substantial risks to human health. This study explores the spatial distribution patterns, sources of pollution, and health risk assessment of heavy metals (Mn, Ni, U, Zn, V, Cu, Cr, Cd and Pb) in the shallow groundwater of the Huainan coal mining area in China. The concentrations of Mn and Ni were found to be relatively high. The spatial distribution characteristics of the heavy metals were analyzed using inverse distance weighting, revealing that the spatial distribution of V, Ni, Cd, Pb and U was similar, suggesting characteristics of typical point source pollution. The PMF model indicated that mining activities, industrial sources and local geogenic processes were the main factors affecting groundwater quality, with contributions of 42.76%, 440.78% and 16.46%, respectively. The health risk assessment results demonstrate that the non-carcinogenic risk of each heavy metal is within the safety threshold; however, the carcinogenic risk posed by Ni should not be overlooked. It is observed that the carcinogenic risk and non-carcinogenic risk values for children exceed those for adults. Consequently, groundwater in the study area must undergo specific purification measures before utilization. The findings of this study offer a scientific foundation for ensuring the quality of groundwater and the safety of drinking water in plain areas affected by coal mining. Heavy metals Soruce identification Groundwater Health risk assessment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Groundwater is a significant source of freshwater, with a wide range of applications. It is used for various purposes, including human consumption, industrial processes, and agricultural activities (Jiang et al. 2021 ; Wen et al. 2019 ). It is estimated that more than half of the world's cities use groundwater as a source of drinking water (Bricker et al. 2017 ). However, as industrialization progresses, human demand for energy is increasing, and the problem of groundwater pollution has become a central research topic (Zhu et al. 2020 ). Common groundwater pollution problems include eutrophication (Z. Li 2018 ), organic pollution (Pan et al. 2024 ), and heavy metal pollution (Sridhar and Parimalarenganayaki 2024 ). Groundwater contamination can be attributed to two primary causes: natural and anthropogenic. The former is predominantly associated with elevated background pollutants within the aquifer medium, a phenomenon known as hydrothermal action (Etikala et al. 2024 ). Heavy metals are usually found in low concentrations in unpolluted groundwater and mainly come from the weathering of rocks (Tiwari et al. 2016 ). Anthropogenic causes are principally associated with agricultural non-point source pollution, industrial wastewater and domestic sewage (Torres-Martínez et al. 2020 ). However, the groundwater contaminated by mining activities is predominantly characterised by the presence of heavy metals(Zhu et al. 2020 ). Heavy metals have garnered significant attention among the numerous pollutants due to their high toxicity, non-biodegradability, and bioaccumulative properties (Yu et al. 2022a ). Consequently, to prevent the adverse effects of heavy metals in groundwater in areas affected by coal mining activities, it is necessary to assess the sources and health risks of heavy metals in groundwater in these areas. Trace elements such as heavy metals in groundwater can harm human health, impacting vital organs such as the nervous system, bones, kidneys, cardiovascular disease and cancer (Abba et al. 2024 ; Yadav et al. 2024 ). Consequently, it is imperative to ascertain the content, spatial distribution, sources and health risk assessment of heavy metals in the environment (Yadav et al. 2024 ). The spatial distribution of common heavy metal pollution is often explored using interpolation methods, such as Kriging and Inverse Distance Weighting, which are frequently combined with the characteristics of the surface environment in the study area to initially infer the spatial distribution pattern of heavy metals (Deng et al. 2024 ). (Jiang et al. 2021 ) utilized inverse distance weighting interpolation to investigate the spatial distribution characteristics of heavy metals in groundwater influenced by mining, and it was determined that mining and industrial activities could substantially augment the concentration of heavy metals. (Eziz et al. 2023 ) employed ordinary kriging interpolation to map the spatial distribution of heavy metals in groundwater in China's chili production areas. (Ravindra and Mor 2019 ) utilized Kriging interpolation to analyze the spatial distribution of heavy metals in groundwater in Chandigarh, India, and identified significant spatial heterogeneity in the distribution of As and other heavy metals. The identification of the sources of heavy metals is often facilitated by the utilization of tools such as multivariate statistical analysis (Jiang et al. 2021 ), source apportionment models (Yu et al. 2022a ), and machine learning (Zhu et al. 2020 ).(Yan et al. 2024 ) employed correlation analysis and principal component analysis to identify the sources of heavy metals in the soil of Tengzhou City, China. (Huang et al. 2024 ) utilized absolute principal component-multiple linear regression (APCS-MLR) to ascertain the predominant influence of natural and agricultural sources of heavy metals in soil in Sanya City. In a separate study, (Sheng et al. 2022 ) et al. utilized PMF and APCS-MLR models to identify the sources of heavy metals in groundwater in typical arid oasis areas in Northwest China. Their findings indicated that industrial and agricultural activities were the primary sources. Huainan, a city with a long history of coal mining, exemplifies the importance of coal resources in driving socio-economic development. However, coal extraction has also been linked to significant environmental challenges (Qiu et al. 2023 ). Consequently, understanding the spatial distribution of heavy metals in groundwater and conducting health risk assessments are crucial for effectively managing and controlling heavy metal pollution (Sheng et al. 2022 ). The objectives of this study are threefold: firstly, to investigate the spatial distribution characteristics of heavy metal elements in shallow groundwater in the study area; secondly, to identify the main sources of heavy metals using multivariate statistical analysis; and thirdly, to assess the human health risk posed by heavy metals in groundwater in the study area. The study's results will support the protection of shallow groundwater in coal mining areas and will help to understand the distribution and sources of heavy metals in coal mining areas in the plains. Materials and methods 2.1 Study area The study area is located in Panji District, Huainan City, at 116°21′~117°11′E longitude and 32°32′~33°06′N latitude. Huainan City is located at the southern end of the Yellow-Huai-Huai Plain, bordering the Huaihe River, and covers an area of 590 square kilometers. The stratigraphy of Panji District belongs to the North China Stratigraphic Zone, and after long-term geological action, the ground surface is covered by the topsoil layer of the Quaternary system with a thickness of 1201 − 564 meters. The thickness is between 1201 and 564 metres. Due to the thick topsoil layer and the multi-layered quicksand layer, the water content is high. The district is located at the southern end of the Huanghuai Plain, with a high topography in the northwest and a low topography in the southeast, with a gentle slope and a slope gradient of one in five thousand, and an elevation of 18–22 meters above sea level, with the highest point being Gulugang in Hetong Township at an elevation of 23.86 meters above sea level, and the lowest point being Tangyuohu in Gaohuang Township at an elevation of 16.9 meters above sea level. Due to the change of river course and the flooding and siltation of Yellow and Huai sediments, the district's terrain is mostly river valley silt plains and irregular Tu Fu Gangtou. Panji District is a subtropical monsoon climate zone. Influenced by the monsoon, winter and summer are long, spring and autumn are short, and the four seasons are distinct. The average annual temperature is 15.1°C, the highest year 16.1°C, the lowest year 14.3°C; the extreme maximum temperature is 41.6°C, the extreme minimum temperature is minus 22.2°C. The average number of sunshine hours is 2298, with 2603.9 hours in the highest year and 1891.3 hours in the lowest year. The average annual rainfall is 905.6 mm, ranging from 1558 mm in the highest year to 347 mm in the lowest year. The average annual frost-free period is 215.5 days. 2.2 Sample collection and analysis Forty shallow groundwater samples (Fig. 1 ) were collected in July 2024 around the Zhuzhuang Mine in Panji District, Huainan City, China. The coordinates of the sampling points were recorded on site. The samples were pumped for about 10 minutes before sampling and immediately returned to the laboratory, where they were filtered through a 0.22 acetate filter membrane, and nitric acid was added after filtration to make the pH of the samples less than 2. Heavy metals were measured using an inductively coupled plasma mass spectrometer (ICP-MS, Shimadzu, ICP-MS 2030LF). Quality assurance and quality control (QA/QC) samples were prepared to ensure the accuracy of the measurements. 2.3 Statistical analysis Sampling point distributions and the spatial distribution of each heavy metal were mapped using Arcgis 10.2 (Esri, Redland, CA). Principal component analysis was performed using IBM SPSS 16.0 (IBM, USA). Positive matrix factorization (PMF) was used to quantitative recognition the contribution of mixture sources. PMF were using EPA PMF 5.0 for windows. The Rstudio ( http://www.r-project.org/ ) was used for statistical analysis and correlation heatmap creation. 2.4 Health risk assessment The human health risk assessment was based on the model recommended by the US Environmental Protection Agency (Wu et al. 2021 ). Heavy metals in drinking water affect human health mainly through direct consumption(Sun et al. 2024 ; Wu et al. 2021 ). The assessment subjects were divided into adults and children according to the population distribution (Sun et al. 2024 ). The calculation procedure was as follows: $$\:{ADD}_{ingestion}=\frac{{C}_{w}\times\:IR\times\:EF\times\:ED}{BW\times\:AT}$$ 1 Where ADDingestion is the average daily direct intake dose; Cw is the content of heavy metals in water samples (µg/L); IR is the amount of water consumed per day; EF is the frequency of exposure; ED is the duration of exposure; BW is body weight; and AD is the average time (day). $$\:HQ=ADD/RfD$$ 2 $$\:HI=\sum\:HQ$$ 3 $$\:CR=\sum\:ADD\times\:SF$$ 4 where RfD is the reference dose, SF represents the slope factor for heavy metals, and CR is the total carcinogenic risk via direct digestion. Results and discussion 3.1 Comprehensive characteristics of heavy metals Table 1 shows the statistical results of heavy metals in the groundwater of the study area. The average concentrations of these heavy metals were in the order of Mn > Ni > U > Zn > V > Cu > Cr > Cd > Pb. The highest average concentration was found for Mn (90.76 µg/L), with a quarter of the samples exceeding the Chinese maximum drinking water quality standard (GB 5749 − 2022) for Mn < 100 µg/L. Similarly, Ni was the next highest mean concentration (15.20 µg/L), with a quarter of the samples exceeding the maximum Chinese drinking water quality standard (GB 5749 − 2022) for Ni. Similarly, Ni was the next highest average concentration (15.20 µg/L), with a quarter of the samples exceeding the maximum Chinese drinking water quality standard (GB 5749 − 2022) (Ni < 20 µg/L). Except for Mn and Ni, which had mean values greater than 10 µg/L, the mean concentrations of the other heavy metals were less than 10 µg/L. Mn is found in high concentrations in groundwater in the Huaibei Plain region of China, and many studies have suggested that the high levels of Mn are naturally occurring (Feng and Yu 2024a ; Meng et al. 2023 ). of Mn, Zn and Ni are relatively large, suggesting that these heavy metals are exceedingly unevenly distributed (Eziz et al. 2023 ). Table 1 Descriptive statistics of heavy metal concentrations in groundwater (µg/L). Items V Mn Ni Cu Zn Cd Pb U Cr Min 0.14 0.25 6.65 0.55 1.35 0.00 0.02 0.02 0.03 Max 16.25 836.26 44.60 3.01 67.50 0.12 0.05 24.70 8.49 Mean 4.29 90.76 15.20 1.16 4.38 0.033 0.025 8.75 0.28 SD 3.69 182.40 7.93 0.52 10.49 0.02 0.01 6.35 1.32 CV a 0.86 2.01 0.52 0.44 2.39 0.73 0.22 0.73 4.73 MAC b 100 100 20 1000 1000 5 10 30 50 a CV = Coefficient of variation b MAC = maximum allowable concentration in drinking-water according to the China National Standard (GB 5749–2022) and World Health Organization (WHO) 3.2 Spatial variation and characteristics of trace metals Inverse distance weights were used to analyze the spatial distribution characteristics of heavy metal elements in groundwater in the study area (Jiang et al. 2021 ). As shown in Fig. 2 , the spatial distributions of V, Ni, Cd, Pb and U were relatively similar, and the distributions showed typical point-source pollution characteristics, and most of these high-value areas were located in the vicinity of urban and rural residential areas and mining waste (fly ash) piles. The spatial distributions of Zn and Cr were highly similar in characteristics, and the hotspot areas were located in the vicinity of villages. Therefore, it is initially inferred that heavy metals in groundwater in the study area are mainly affected by mining activities and agricultural activities, and the point source characteristics are extremely obvious (Jiang et al. 2021 ). Generally, there is a strong relationship between heavy metal content in groundwater and geological background and anthropogenic activities in the study area (Chorol and Gupta 2023 ). 3.3 Multivariate Statistical Analysis 3.3.1 Correlation between Heavy Metals Correlation analysis is often used to investigate the relationship between water quality indicators in groundwater, and generally, positive correlations have been found between water quality parameters with similar sources or similar transport and transformation processes. The results of the correlation analysis of heavy metals in groundwater in the study area are shown in Fig. 3 . Significant positive correlations ( p < 0.01) were found between V, U, Pb and Ni, suggesting that these heavy metals may have similar origins; Ni and Pb are typical heavy metals originating from industrial activities, and therefore V, U, Pb and Ni may originate from industrial activities (Zhai et al. 2022 ). The correlation coefficient between Cr and Zn is 0.96, and their spatial distributions are very similar, suggesting that Cr and Zn originate from the same or similar migratory transformation processes. The correlation coefficients of Cu with Cd, Zn and Cr are 0.51, 0.42 and 0.43, respectively, and these heavy metals are usually associated with anthropogenic activities, agriculture, and industrial pollution (Meng et al. 2023 ). Ni and Cd showed a significant negative correlation with a coefficient of -0.33, indicating that Ni and Cd come from different pollution sources (Jiang et al. 2021 ). 3.3.2 Principal Component Analysis Principal component analysis (PCA) is a highly effective method of exploring the sources of heavy metals, and PCA has been widely applied to identify the sources of heavy metals in different environmental media (Yu et al. 2022b ). The results of KMO and Bartlett's Sphericity test were 0.0.615 and 0.00, respectively, which responded to the fact that these parameters can be used for principal component analysis(Chen et al. 2022 ).The results of principal component analysis of heavy metals in groundwater in the study area are shown in Table 2 . The analysis identified three principal components, collectively accounting for 73.391% of the total variance. As shown in Table 3 , PC1 accounted for 31.77% of the total variance, with Cr, Cd, Zn, and Cu exhibiting higher loading values. Cr is typically considered to be more intensely disturbed by human activities, and fertilizers, pesticides, and herbicides used in the agricultural production process contain trace amounts of Cr (Meng et al. 2023 ). Zn may originate from the domestic waste, and the rural domestic waste's recovery rate is not high. Zn may originate from domestic waste, and the current recycling rate of rural domestic waste is not very high, with a significant amount of rubbish and domestic sewage being disposed of unorganized (X. Li et al. 2024 ). Table 2 Results of principal component analysis of heavy metals in groundwater PCs Initial eigenvalue Extract the sum of squares of the load Square sum of rotational load eigenvalue Percentage of variance/% Cumulative contribution % eigenvalue Percentage of variance/% Cumulative contribution % eigenvalue Percentage of variance/% Cumulative contribution % 1 3.01 33.42 33.42 3.01 33.42 33.42 2.86 31.77 31.77 2 2.48 27.50 60.92 2.48 27.50 60.92 2.52 28.04 59.81 3 1.12 12.47 73.39 1.12 12.47 73.39 1.22 13.58 73.39 4 0.83 9.24 82.63 5 0.66 7.37 90.01 6 0.47 5.24 95.25 7 0.27 2.98 98.23 8 0.13 1.41 99.64 9 0.03 0.36 100.00 Table 3 Heavymetal factor loadings ingroundwater Heavy metals PC1 PC2 PC3 V 0.06 0.90 -0.08 Mn -0.12 0.00 0.92 Ni -0.18 0.89 0.03 Cu 0.68 0.18 -0.33 Zn 0.93 0.01 0.11 Cd 0.76 -0.32 -0.15 Pb 0.11 0.66 0.01 U -0.25 0.60 -0.46 Cr 0.92 0.02 0.09 Additionally, Cu may leach into groundwater through agricultural activities, domestic waste, and domestic wastewater (Aithani et al. 2020 ). Consequently, PC1 can be identified as an agricultural face source of pollution. PC2 explained a total of 28.04% of the overall variables with high loading values for V, Ni, Pb and U (Table 3 ). Although Ni was considered more of geological origin (Lin et al., 2 016), one-fourth of the groundwater in the study area had Ni content greater than MAC. (Lin et al. 2016 ) explored the source of Ni in groundwater in the Huaibei Coalfield and concluded that mining activities are the main cause of Ni exceedance. Ni and V have been reported in some industrial wastewater from alloy manufacturing, electroplating and smelting (Parveen et al. n.d.). Pb enters the environment through automobile exhaust emissions and lead oxide in tires after tire wear (Parveen et al. n.d.). Thus, PC2 can be identified as an industrial source. PC3 explained 13.58% of the overall variable and showed a significant positive loading with Mn. The presence of Mn in groundwater in the plains of Anhui Province, China, is attributed to natural sources (Feng and Yu 2024b ), thus confirming that PC3 is a natural source. 3.3.3 Source apportionment using PMF In the random seed model, three source factors were identified by systematically evaluating different factor numbers to determine the optimal factor count. This ensures that Q(true) ≈ Q(robust) and establishes a strong correlation between prediction and observation (H. Zhang et al. 2020 ). The contribution percentages of each factor and their corresponding factor characteristics are presented in Fig. 4 . In Factor 1(F1), as analyzed by the PMF model, Cd, Ni, Cu, and Zn exhibit higher contributions. In this study area, the presence of Cd and Ni in groundwater is predominantly associated with mining activities. Prior research has indicated that the concentration of Cd in coal gangue mountains and coal chemical industrial parks within mining regions tends to be relatively elevated (Jiang et al. 2021 ). Thus, F1 can be identified as a mining activity. Mn is the main parameter in Facter 2 (F2), indicating that the groundwater in the study area is in a reducing environment, the background value is high or the industrial wastewater has an influence on it (Q. Zhang et al. 2023 ). The Mn content in coal gangue and mining waste is significantly high, and during long-term accumulation, it will gradually leach into the environment (Jiang et al. 2021 ). Combined with the spatial distribution characteristics of Mn (as shown in Fig. 2 ), it can be inferred that the pollution is predominantly spot-like in nature, with 75% of the groundwater exhibiting low concentrations of Mn. In the analysis of the source of Mn in the groundwater of the mining area in northern Anhui, China, Mn is primarily attributed to geological origins (Feng and Yu 2024a ; Meng et al. 2023 ). Therefore, F2 is considered a natural source. Factor 3 (F3) was characterized by U and V. V and U are considered to be the signature elements of electroplating wastewater and industrial wastewater, so F3 is identified as an industrial source. 3.4 Health risk assessment As posited by (Parveen et al. n.d.), exposure to heavy metals can occur via three primary routes: ingestion of contaminated food or water, inhalation of airborne particles containing heavy metals, and dermal exposure. However, ingesting heavy metals through drinking water is considered the most significant route (Alidadi et al. 2019 ). The results of the non-carcinogenic risk assessment are illustrated in Fig. 6 . The magnitude of non-carcinogenic risk posed by heavy metals in the groundwater of the study area for both adults and children was in the order of U > V > Ni > Mn > Cr > Cd > Cu > Zn > Pb, and the HQ values were all less than 1 (Alidadi et al. 2019 ). HQ values greater than 1 indicate a higher probability of non-carcinogenic health risk. As demonstrated in Fig. 6 , the HI values for adults and children were less than 1, indicating that the non-carcinogenic risk from heavy metals in the groundwater in the study area is within the safe range. Children were exposed to higher non-carcinogenic health risks compared to adults, with the non-carcinogenic risk for children being 1.5 times higher than that for adults. The carcinogenic potential of Ni, Pb, Cr and Cd is demonstrated in Fig. 6 . Carcinogenic risk (CR) values greater than 1.0 × 10 − 4 indicate a high carcinogenic risk, with CR values between 1.0 × 10 − 6 and 1.0 × 10 − 4 being considered acceptable thresholds recommended by the USEPA, and CRs less than 1.0 × 10 − 6 being negligible levels (Sun et al. 2024 ). The mean CR for Ni for adults and children was greater than 1.0 × 10 − 4, indicating that Ni poses a non-negligible carcinogenic risk. For Cr, the carcinogenic risk for both adults and children exceeded the safety threshold in only one water sample, and the CR for Pb and Cd were less than the safety threshold. Both carcinogenic and non-carcinogenic risks were more significant in children than in adults. This phenomenon may be attributed to the heightened sensitivity of children to heavy metals during their growth and development, coupled with their comparatively lower body weight, which renders them more susceptible to carcinogenic and non-carcinogenic risks (Sun et al. 2024 ; Wu et al. 2021 ).The findings underscore the inadequacy of shallow groundwater in the study area for consumption as drinking water. The presence of carcinogenic risk in the shallow groundwater of the study area has been attributed to anthropogenic disturbances (industrial and agricultural activities). Therefore, the shallow groundwater should be purified before use or consumption as municipal water (Sun et al. 2024 ). Conclusions This study analyzed the content and spatial distribution characteristics of Mn, Ni, U, Zn, V, Cu, Cr, Cd and Pb. The sources of heavy metals were identified using correlation and principal component analysis. Finally, the health risk was calculated by combining this with a health risk assessment model. The conclusions that can be drawn from this study are as follows: (1) The average concentrations of the heavy metals under scrutiny were found to be Mn > Ni > U > Zn > V > Cu > Cr > Cd > Pb. It was determined that one-quarter of the shallow groundwater samples had concentrations of Mn and Ni that exceeded the drinking water quality standards. The spatial distribution characteristics demonstrated that the groundwater with high concentrations of heavy metals was predominantly located in close proximity to urban and rural residential areas, as well as the dumping sites of mining waste. (2) Principal component analysis (PCA) extracted three principal components (PCs), which cumulatively explained 73.391% of the total variance. PC1 accounted for 31.77% of the total variance, with Cr, Cd, Zn and Cu exhibiting higher loading values. Consequently, PC1 was identified as a indicator of pollution from agricultural surface sources. PC2 accounted for 28.04% of the total variance, with V, Ni, Pb and U exhibiting higher loading values. The PC2 were indicative of an industrial source. PC3 explained 13.58% of the overall variables and showed a significant positive loading with Mn. In combination with the results of previous studies, PC3 was considered as a natural background source. The PMF model indicated that mining activities, industrial sources and local geogenic processes were the main factors affecting groundwater quality, with contributions of 42.76%, 440.78% and 16.46%, respectively. (3) The health risk assessment results demonstrated that Ni's carcinogenicity risk to adults and children was relatively high, and the health risk of point source pollution should also be considered.The carcinogenicity risk of Cr to adults and children exceeded the safety thresholds for only one sample, and the carcinogenicity risks of Pb and Cd were less than the safety thresholds. The carcinogenic and non-carcinogenic risks of heavy metals in the study groundwater were found to be higher for children than for adults. It is therefore recommended that shallow groundwater in the study area be consumed only after the necessary purification and that municipal water supplies be used as drinking water sources. Declarations Acknowledgements This research was supported by the National Key Research and Development Project (2024ZD1004204), the Scientific Research Project of Anhui Colleges and Universities (2023AH052224 and 2023AH052232), Natural Science Research Project of Anhui Educational Committee (No. 2022AH050797), Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (No.EC2023012), Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters (No. 2022-MGDP-01), Independent Research Fund of the State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (No. SKLMRDPC19ZZ06), the Coal Research Centre for Comprehensive Prevention and Control of Mine Water Hazard (134092220042204), the Key Scientific Research Project of Suzhou University (2022yzd01), and the Outstanding Academic and Technical Backbone of Suzhou University (2020XJGG11). 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Occurrence, controlling factors and noncarcinogenic risk assessment based on Monte Carlo simulation of fluoride in mid-layer groundwater of Huaibei mining area, North China. Science of The Total Environment , 856 , 159112. https://doi.org/10.1016/j.scitotenv.2022.159112 Ravindra, K., & Mor, S. (2019). Distribution and health risk assessment of arsenic and selected heavy metals in Groundwater of Chandigarh, India. Environmental Pollution , 250 , 820–830. https://doi.org/10.1016/j.envpol.2019.03.080 Sheng, D., Meng, X., Wen, X., Wu, J., Yu, H., & Wu, M. (2022). Contamination characteristics, source identification, and source-specific health risks of heavy metal(loid)s in groundwater of an arid oasis region in Northwest China. Science of The Total Environment , 841 , 156733. https://doi.org/10.1016/j.scitotenv.2022.156733 Sridhar, D., & Parimalarenganayaki, S. (2024). Evaluation of human and ecological health risks associated with the potentially toxic heavy metals in groundwater of Vellore city, Tamil Nadu, India. Journal of Hazardous Materials Advances , 16 , 100497. https://doi.org/10.1016/j.hazadv.2024.100497 Sun, L., Liu, T., Duan, L., Tong, X., Zhang, W., Cui, H., et al. (2024). Spatial and temporal distribution characteristics and risk assessment of heavy metals in groundwater of Pingshuo mining area. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH , 46 (4), 141. https://doi.org/10.1007/s10653-024-01906-7 Tiwari, A. K., Singh, P. K., Singh, A. K., & De Maio, M. (2016). Estimation of Heavy Metal Contamination in Groundwater and Development of a Heavy Metal Pollution Index by Using GIS Technique. Bulletin of Environmental Contamination and Toxicology , 96 (4), 508–515. https://doi.org/10.1007/s00128-016-1750-6 Torres-Martínez, J. A., Mora, A., Knappett, P. S. K., Ornelas-Soto, N., & Mahlknecht, J. (2020). Tracking nitrate and sulfate sources in groundwater of an urbanized valley using a multi-tracer approach combined with a Bayesian isotope mixing model. Water Research , 182 , 115962. https://doi.org/10.1016/j.watres.2020.115962 Wen, X., Lu, J., Wu, J., Lin, Y., & Luo, Y. (2019). Influence of coastal groundwater salinization on the distribution and risks of heavy metals. Science of The Total Environment , 652 , 267–277. https://doi.org/10.1016/j.scitotenv.2018.10.250 Wu, H., Xu, C., Wang, J., Xiang, Y., Ren, M., Qie, H., et al. (2021). Health risk assessment based on source identification of heavy metals: A case study of Beiyun River, China. Ecotoxicology and Environmental Safety , 213 , 112046. https://doi.org/10.1016/j.ecoenv.2021.112046 Yadav, A. K., Kulsoom, M., Kumar, M., Pat Raw, K., & Kumar, N. (2024). Health risk assessment due to heavy metal contamination in groundwater of Basuhi River Basin, Jaunpur, India. ENVIRONMENTAL SUSTAINABILITY , 7 (2), 251–260. https://doi.org/10.1007/s42398-024-00318-8 Yan, B., Li, X., Yang, J., Wang, M., Zhang, R., & Song, X. (2024). Assessment of health risks based on different populations and sources of heavy metals on agricultural lane in Tengzhou City by APCS-MLR models. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH , 46 (11), 443. https://doi.org/10.1007/s10653-024-02227-5 Yu, H., Lin, M., Peng, W., & He, C. (2022a). Seasonal changes of heavy metals and health risk assessment based on Monte Carlo simulation in alternate water sources of the Xinbian River in Suzhou City, Huaibei Plain, China. Ecotoxicology and Environmental Safety , 236 , 113445. https://doi.org/10.1016/j.ecoenv.2022.113445 Yu, H., Lin, M., Peng, W., & He, C. (2022b). Seasonal changes of heavy metals and health risk assessment based on Monte Carlo simulation in alternate water sources of the Xinbian River in Suzhou City, Huaibei Plain, China. Ecotoxicology and Environmental Safety , 236 , 113445. https://doi.org/10.1016/j.ecoenv.2022.113445 Zhai, Y., Zheng, F., Li, D., Cao, X., & Teng, Y. (2022). Distribution, Genesis, and Human Health Risks of Groundwater Heavy Metals Impacted by the Typical Setting of Songnen Plain of NE China. International Journal of Environmental Research and Public Health , 19 (6), 3571. https://doi.org/10.3390/ijerph19063571 Zhang, H., Cheng, S., Li, H., Fu, K., & Xu, Y. (2020). Groundwater pollution source identification and apportionment using PMF and PCA-APCA-MLR receptor models in a typical mixed land-use area in Southwestern China. Science of The Total Environment , 741 , 140383. https://doi.org/10.1016/j.scitotenv.2020.140383 Zhang, Q., Wang, H., Xu, Z., Li, G., Yang, M., & Liu, J. (2023). Quantitative identification of groundwater contamination sources by combining isotope tracer technique with PMF model in an arid area of northwestern China. Journal of Environmental Management , 325 , 116588. https://doi.org/10.1016/j.jenvman.2022.116588 Zhu, G., Wu, X., Ge, J., Liu, F., Zhao, W., & Wu, C. (2020). Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a self-organizing map (SOM). Journal of Cleaner Production , 257 , 120664. https://doi.org/10.1016/j.jclepro.2020.120664 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6247727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430715044,"identity":"0ed4b635-c4e1-479a-8389-536397b85e4f","order_by":0,"name":"Lei Han","email":"","orcid":"","institution":"CCTEG Xi’an Research Institute (Group) Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Han","suffix":""},{"id":430715045,"identity":"acdf775a-219a-4139-b388-b441d6b78dde","order_by":1,"name":"Jie Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACCQjFzMbAfODAhx+kaWFLPDizhwQtQMBjfJiDjQgd/LPbH3/mzbFj52Pv+XCYgYdBnl/sAAFL7pwxMObdlszMxnN2w+ECCwbDmbMTCFhzI4chmXcbMzObRO6GwzN4GBIMbhPQIn8j/cFh3m31zGzybx4c5mEjQovBjQTDZt5th4G28DAQp8XwRo4x49xtx4F+STMABrIEYb/I3Uh//OHttupk+fbDjz98+GEjzy9NQAsMJENpCbyqUIAd8UpHwSgYBaNgxAEAeoZBHiz/DFAAAAAASUVORK5CYII=","orcid":"","institution":"Suzhou University","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Ma","suffix":""},{"id":430715046,"identity":"1d81afac-31e4-40e3-8333-c6b58a242ae0","order_by":2,"name":"Qimeng Liu","email":"","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qimeng","middleName":"","lastName":"Liu","suffix":""},{"id":430715047,"identity":"ab0983fe-6e73-436d-ab7d-f179696bb944","order_by":3,"name":"Yu Liu","email":"","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Liu","suffix":""},{"id":430715048,"identity":"73f7d000-f00a-4f15-8caa-cbb0df488fb5","order_by":4,"name":"Hongbao Dai","email":"","orcid":"","institution":"Suzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hongbao","middleName":"","lastName":"Dai","suffix":""},{"id":430715049,"identity":"ce2b9489-9d47-43b8-8013-443ca1cc0c9e","order_by":5,"name":"Cancan Wu","email":"","orcid":"","institution":"Suzhou University","correspondingAuthor":false,"prefix":"","firstName":"Cancan","middleName":"","lastName":"Wu","suffix":""},{"id":430715050,"identity":"e9813dbe-e794-40df-8246-d9bf18772edd","order_by":6,"name":"Hao Yu","email":"","orcid":"","institution":"Suzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-03-17 21:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6247727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6247727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78888660,"identity":"1eb90ed0-213f-41c8-89de-9ba772be0dcf","added_by":"auto","created_at":"2025-03-20 10:06:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeological location and sampling sites of the study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/0d1f95843af9298ef1d35450.png"},{"id":78886856,"identity":"821b3981-6a22-4a12-be20-c2b22b5bcfc6","added_by":"auto","created_at":"2025-03-20 09:50:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":316508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of heavy metals in groundwater.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/c8d62ab1ab1d9ada8198a974.png"},{"id":78886852,"identity":"35765ce4-aaf1-40de-a5ab-e8268fd3b254","added_by":"auto","created_at":"2025-03-20 09:50:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56432,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of heavy metals in groundwater in the study area.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/3631fc8f4a626b1ca63867aa.png"},{"id":78886854,"identity":"5c18a583-ea53-463f-9501-e435155dfa2d","added_by":"auto","created_at":"2025-03-20 09:50:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104010,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePMF model identifies the contribution (a) of each pollution source to the pesticides (b) and average proportion.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/bf41ba6f6522f80a4610c2b6.png"},{"id":78886857,"identity":"142316d3-6c18-4e18-adb3-567c215a3d80","added_by":"auto","created_at":"2025-03-20 09:50:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":124484,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-carcinogenic health risks of heavy metals in groundwater for adults and children.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/3df67fb410093196b63b891c.png"},{"id":78886861,"identity":"0cf51d51-cf88-472b-8394-00ed6d77fc99","added_by":"auto","created_at":"2025-03-20 09:50:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarcinogenic Health Risks of Heavy Metals in Groundwater for Adults and Children\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/92ee8eb4fbc52d318242d5af.png"},{"id":78889634,"identity":"d56506b4-c5b5-41e7-b97d-41414f88b3eb","added_by":"auto","created_at":"2025-03-20 10:22:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2004276,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6247727/v1/d9f111f3-fa53-459f-b6f0-d215a6f2d17a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial distribution, sources and health risk assessment of heavy metals in shallow groundwater in a typical coal mining area in Huainan, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGroundwater is a significant source of freshwater, with a wide range of applications. It is used for various purposes, including human consumption, industrial processes, and agricultural activities (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wen et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is estimated that more than half of the world's cities use groundwater as a source of drinking water (Bricker et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, as industrialization progresses, human demand for energy is increasing, and the problem of groundwater pollution has become a central research topic (Zhu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Common groundwater pollution problems include eutrophication (Z. Li \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), organic pollution (Pan et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and heavy metal pollution (Sridhar and Parimalarenganayaki \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Groundwater contamination can be attributed to two primary causes: natural and anthropogenic. The former is predominantly associated with elevated background pollutants within the aquifer medium, a phenomenon known as hydrothermal action (Etikala et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Heavy metals are usually found in low concentrations in unpolluted groundwater and mainly come from the weathering of rocks (Tiwari et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Anthropogenic causes are principally associated with agricultural non-point source pollution, industrial wastewater and domestic sewage (Torres-Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the groundwater contaminated by mining activities is predominantly characterised by the presence of heavy metals(Zhu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Heavy metals have garnered significant attention among the numerous pollutants due to their high toxicity, non-biodegradability, and bioaccumulative properties (Yu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Consequently, to prevent the adverse effects of heavy metals in groundwater in areas affected by coal mining activities, it is necessary to assess the sources and health risks of heavy metals in groundwater in these areas.\u003c/p\u003e \u003cp\u003eTrace elements such as heavy metals in groundwater can harm human health, impacting vital organs such as the nervous system, bones, kidneys, cardiovascular disease and cancer (Abba et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, it is imperative to ascertain the content, spatial distribution, sources and health risk assessment of heavy metals in the environment (Yadav et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The spatial distribution of common heavy metal pollution is often explored using interpolation methods, such as Kriging and Inverse Distance Weighting, which are frequently combined with the characteristics of the surface environment in the study area to initially infer the spatial distribution pattern of heavy metals (Deng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) utilized inverse distance weighting interpolation to investigate the spatial distribution characteristics of heavy metals in groundwater influenced by mining, and it was determined that mining and industrial activities could substantially augment the concentration of heavy metals. (Eziz et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) employed ordinary kriging interpolation to map the spatial distribution of heavy metals in groundwater in China's chili production areas. (Ravindra and Mor \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) utilized Kriging interpolation to analyze the spatial distribution of heavy metals in groundwater in Chandigarh, India, and identified significant spatial heterogeneity in the distribution of As and other heavy metals. The identification of the sources of heavy metals is often facilitated by the utilization of tools such as multivariate statistical analysis (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), source apportionment models (Yu et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), and machine learning (Zhu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).(Yan et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) employed correlation analysis and principal component analysis to identify the sources of heavy metals in the soil of Tengzhou City, China. (Huang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) utilized absolute principal component-multiple linear regression (APCS-MLR) to ascertain the predominant influence of natural and agricultural sources of heavy metals in soil in Sanya City. In a separate study, (Sheng et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) et al. utilized PMF and APCS-MLR models to identify the sources of heavy metals in groundwater in typical arid oasis areas in Northwest China. Their findings indicated that industrial and agricultural activities were the primary sources.\u003c/p\u003e \u003cp\u003eHuainan, a city with a long history of coal mining, exemplifies the importance of coal resources in driving socio-economic development. However, coal extraction has also been linked to significant environmental challenges (Qiu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, understanding the spatial distribution of heavy metals in groundwater and conducting health risk assessments are crucial for effectively managing and controlling heavy metal pollution (Sheng et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The objectives of this study are threefold: firstly, to investigate the spatial distribution characteristics of heavy metal elements in shallow groundwater in the study area; secondly, to identify the main sources of heavy metals using multivariate statistical analysis; and thirdly, to assess the human health risk posed by heavy metals in groundwater in the study area. The study's results will support the protection of shallow groundwater in coal mining areas and will help to understand the distribution and sources of heavy metals in coal mining areas in the plains.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study area is located in Panji District, Huainan City, at 116\u0026deg;21\u0026prime;~117\u0026deg;11\u0026prime;E longitude and 32\u0026deg;32\u0026prime;~33\u0026deg;06\u0026prime;N latitude. Huainan City is located at the southern end of the Yellow-Huai-Huai Plain, bordering the Huaihe River, and covers an area of 590 square kilometers. The stratigraphy of Panji District belongs to the North China Stratigraphic Zone, and after long-term geological action, the ground surface is covered by the topsoil layer of the Quaternary system with a thickness of 1201\u0026thinsp;\u0026minus;\u0026thinsp;564 meters. The thickness is between 1201 and 564 metres. Due to the thick topsoil layer and the multi-layered quicksand layer, the water content is high.\u003c/p\u003e \u003cp\u003eThe district is located at the southern end of the Huanghuai Plain, with a high topography in the northwest and a low topography in the southeast, with a gentle slope and a slope gradient of one in five thousand, and an elevation of 18\u0026ndash;22 meters above sea level, with the highest point being Gulugang in Hetong Township at an elevation of 23.86 meters above sea level, and the lowest point being Tangyuohu in Gaohuang Township at an elevation of 16.9 meters above sea level. Due to the change of river course and the flooding and siltation of Yellow and Huai sediments, the district's terrain is mostly river valley silt plains and irregular Tu Fu Gangtou. Panji District is a subtropical monsoon climate zone. Influenced by the monsoon, winter and summer are long, spring and autumn are short, and the four seasons are distinct. The average annual temperature is 15.1\u0026deg;C, the highest year 16.1\u0026deg;C, the lowest year 14.3\u0026deg;C; the extreme maximum temperature is 41.6\u0026deg;C, the extreme minimum temperature is minus 22.2\u0026deg;C. The average number of sunshine hours is 2298, with 2603.9 hours in the highest year and 1891.3 hours in the lowest year. The average annual rainfall is 905.6 mm, ranging from 1558 mm in the highest year to 347 mm in the lowest year. The average annual frost-free period is 215.5 days.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Sample collection and analysis\u003c/h3\u003e\n\u003cp\u003eForty shallow groundwater samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were collected in July 2024 around the Zhuzhuang Mine in Panji District, Huainan City, China. The coordinates of the sampling points were recorded on site. The samples were pumped for about 10 minutes before sampling and immediately returned to the laboratory, where they were filtered through a 0.22 acetate filter membrane, and nitric acid was added after filtration to make the pH of the samples less than 2. Heavy metals were measured using an inductively coupled plasma mass spectrometer (ICP-MS, Shimadzu, ICP-MS 2030LF). Quality assurance and quality control (QA/QC) samples were prepared to ensure the accuracy of the measurements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2.3 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eSampling point distributions and the spatial distribution of each heavy metal were mapped using Arcgis 10.2 (Esri, Redland, CA). Principal component analysis was performed using IBM SPSS 16.0 (IBM, USA). Positive matrix factorization (PMF) was used to quantitative recognition the contribution of mixture sources. PMF were using EPA PMF 5.0 for windows. The Rstudio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org/\u003c/span\u003e\u003cspan address=\"http://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for statistical analysis and correlation heatmap creation.\u003c/p\u003e\n\u003ch3\u003e2.4 Health risk assessment\u003c/h3\u003e\n\u003cp\u003eThe human health risk assessment was based on the model recommended by the US Environmental Protection Agency (Wu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Heavy metals in drinking water affect human health mainly through direct consumption(Sun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The assessment subjects were divided into adults and children according to the population distribution (Sun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The calculation procedure was as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{ADD}_{ingestion}=\\frac{{C}_{w}\\times\\:IR\\times\\:EF\\times\\:ED}{BW\\times\\:AT}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere ADDingestion is the average daily direct intake dose; Cw is the content of heavy metals in water samples (\u0026micro;g/L); IR is the amount of water consumed per day; EF is the frequency of exposure; ED is the duration of exposure; BW is body weight; and AD is the average time (day).\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:HQ=ADD/RfD$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:HI=\\sum\\:HQ$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:CR=\\sum\\:ADD\\times\\:SF$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere RfD is the reference dose, SF represents the slope factor for heavy metals, and CR is the total carcinogenic risk via direct digestion.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comprehensive characteristics of heavy metals\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the statistical results of heavy metals in the groundwater of the study area. The average concentrations of these heavy metals were in the order of Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;U\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Pb. The highest average concentration was found for Mn (90.76 \u0026micro;g/L), with a quarter of the samples exceeding the Chinese maximum drinking water quality standard (GB 5749\u0026thinsp;\u0026minus;\u0026thinsp;2022) for Mn\u0026thinsp;\u0026lt;\u0026thinsp;100 \u0026micro;g/L. Similarly, Ni was the next highest mean concentration (15.20 \u0026micro;g/L), with a quarter of the samples exceeding the maximum Chinese drinking water quality standard (GB 5749\u0026thinsp;\u0026minus;\u0026thinsp;2022) for Ni. Similarly, Ni was the next highest average concentration (15.20 \u0026micro;g/L), with a quarter of the samples exceeding the maximum Chinese drinking water quality standard (GB 5749\u0026thinsp;\u0026minus;\u0026thinsp;2022) (Ni\u0026thinsp;\u0026lt;\u0026thinsp;20 \u0026micro;g/L). Except for Mn and Ni, which had mean values greater than 10 \u0026micro;g/L, the mean concentrations of the other heavy metals were less than 10 \u0026micro;g/L. Mn is found in high concentrations in groundwater in the Huaibei Plain region of China, and many studies have suggested that the high levels of Mn are naturally occurring (Feng and Yu \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Meng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). of Mn, Zn and Ni are relatively large, suggesting that these heavy metals are exceedingly unevenly distributed (Eziz et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\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\u003eDescriptive statistics of heavy metal concentrations in groundwater (\u0026micro;g/L).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e836.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAC\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\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\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e CV\u0026thinsp;=\u0026thinsp;Coefficient of variation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e MAC\u0026thinsp;=\u0026thinsp;maximum allowable concentration in drinking-water according to the China National Standard (GB 5749\u0026ndash;2022) and World Health Organization (WHO)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3.2 Spatial variation and characteristics of trace metals\u003c/h3\u003e\n\u003cp\u003eInverse distance weights were used to analyze the spatial distribution characteristics of heavy metal elements in groundwater in the study area (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the spatial distributions of V, Ni, Cd, Pb and U were relatively similar, and the distributions showed typical point-source pollution characteristics, and most of these high-value areas were located in the vicinity of urban and rural residential areas and mining waste (fly ash) piles. The spatial distributions of Zn and Cr were highly similar in characteristics, and the hotspot areas were located in the vicinity of villages. Therefore, it is initially inferred that heavy metals in groundwater in the study area are mainly affected by mining activities and agricultural activities, and the point source characteristics are extremely obvious (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Generally, there is a strong relationship between heavy metal content in groundwater and geological background and anthropogenic activities in the study area (Chorol and Gupta \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3.3 Multivariate Statistical Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3.1 Correlation between Heavy Metals\u003c/h2\u003e \u003cp\u003eCorrelation analysis is often used to investigate the relationship between water quality indicators in groundwater, and generally, positive correlations have been found between water quality parameters with similar sources or similar transport and transformation processes. The results of the correlation analysis of heavy metals in groundwater in the study area are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Significant positive correlations ( p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were found between V, U, Pb and Ni, suggesting that these heavy metals may have similar origins; Ni and Pb are typical heavy metals originating from industrial activities, and therefore V, U, Pb and Ni may originate from industrial activities (Zhai et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The correlation coefficient between Cr and Zn is 0.96, and their spatial distributions are very similar, suggesting that Cr and Zn originate from the same or similar migratory transformation processes. The correlation coefficients of Cu with Cd, Zn and Cr are 0.51, 0.42 and 0.43, respectively, and these heavy metals are usually associated with anthropogenic activities, agriculture, and industrial pollution (Meng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ni and Cd showed a significant negative correlation with a coefficient of -0.33, indicating that Ni and Cd come from different pollution sources (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3.2 Principal Component Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) is a highly effective method of exploring the sources of heavy metals, and PCA has been widely applied to identify the sources of heavy metals in different environmental media (Yu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The results of KMO and Bartlett's Sphericity test were 0.0.615 and 0.00, respectively, which responded to the fact that these parameters can be used for principal component analysis(Chen et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).The results of principal component analysis of heavy metals in groundwater in the study area are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The analysis identified three principal components, collectively accounting for 73.391% of the total variance. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, PC1 accounted for 31.77% of the total variance, with Cr, Cd, Zn, and Cu exhibiting higher loading values. Cr is typically considered to be more intensely disturbed by human activities, and fertilizers, pesticides, and herbicides used in the agricultural production process contain trace amounts of Cr (Meng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zn may originate from the domestic waste, and the rural domestic waste's recovery rate is not high. Zn may originate from domestic waste, and the current recycling rate of rural domestic waste is not very high, with a significant amount of rubbish and domestic sewage being disposed of unorganized (X. Li et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\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\u003eResults of principal component analysis of heavy metals in groundwater\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePCs\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\u003eExtract the sum of squares of the load\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eSquare sum of rotational load\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\u003ePercentage of variance/%\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\u003ePercentage of variance/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCumulative contribution %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eeigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercentage of variance/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\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\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e31.77\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\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e59.81\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\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e73.39\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\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.63\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90.01\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.25\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\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\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.23\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.64\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.00\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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eHeavymetal factor loadings ingroundwater\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\" colname=\"c1\"\u003e \u003cp\u003eHeavy metals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.08\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\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\u003eAdditionally, Cu may leach into groundwater through agricultural activities, domestic waste, and domestic wastewater (Aithani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, PC1 can be identified as an agricultural face source of pollution. PC2 explained a total of 28.04% of the overall variables with high loading values for V, Ni, Pb and U (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although Ni was considered more of geological origin (Lin et al., 2 016), one-fourth of the groundwater in the study area had Ni content greater than MAC. (Lin et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) explored the source of Ni in groundwater in the Huaibei Coalfield and concluded that mining activities are the main cause of Ni exceedance. Ni and V have been reported in some industrial wastewater from alloy manufacturing, electroplating and smelting (Parveen et al. n.d.). Pb enters the environment through automobile exhaust emissions and lead oxide in tires after tire wear (Parveen et al. n.d.). Thus, PC2 can be identified as an industrial source. PC3 explained 13.58% of the overall variable and showed a significant positive loading with Mn. The presence of Mn in groundwater in the plains of Anhui Province, China, is attributed to natural sources (Feng and Yu \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e), thus confirming that PC3 is a natural source.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3.3 Source apportionment using PMF\u003c/h2\u003e \u003cp\u003eIn the random seed model, three source factors were identified by systematically evaluating different factor numbers to determine the optimal factor count. This ensures that Q(true)\u0026thinsp;\u0026asymp;\u0026thinsp;Q(robust) and establishes a strong correlation between prediction and observation (H. Zhang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The contribution percentages of each factor and their corresponding factor characteristics are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In Factor 1(F1), as analyzed by the PMF model, Cd, Ni, Cu, and Zn exhibit higher contributions. In this study area, the presence of Cd and Ni in groundwater is predominantly associated with mining activities. Prior research has indicated that the concentration of Cd in coal gangue mountains and coal chemical industrial parks within mining regions tends to be relatively elevated (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, F1 can be identified as a mining activity. Mn is the main parameter in Facter 2 (F2), indicating that the groundwater in the study area is in a reducing environment, the background value is high or the industrial wastewater has an influence on it (Q. Zhang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Mn content in coal gangue and mining waste is significantly high, and during long-term accumulation, it will gradually leach into the environment (Jiang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Combined with the spatial distribution characteristics of Mn (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it can be inferred that the pollution is predominantly spot-like in nature, with 75% of the groundwater exhibiting low concentrations of Mn. In the analysis of the source of Mn in the groundwater of the mining area in northern Anhui, China, Mn is primarily attributed to geological origins (Feng and Yu \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Meng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, F2 is considered a natural source. Factor 3 (F3) was characterized by U and V. V and U are considered to be the signature elements of electroplating wastewater and industrial wastewater, so F3 is identified as an industrial source.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Health risk assessment\u003c/h2\u003e \u003cp\u003eAs posited by (Parveen et al. n.d.), exposure to heavy metals can occur via three primary routes: ingestion of contaminated food or water, inhalation of airborne particles containing heavy metals, and dermal exposure. However, ingesting heavy metals through drinking water is considered the most significant route (Alidadi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The results of the non-carcinogenic risk assessment are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe magnitude of non-carcinogenic risk posed by heavy metals in the groundwater of the study area for both adults and children was in the order of U\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Pb, and the HQ values were all less than 1 (Alidadi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). HQ values greater than 1 indicate a higher probability of non-carcinogenic health risk. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the HI values for adults and children were less than 1, indicating that the non-carcinogenic risk from heavy metals in the groundwater in the study area is within the safe range. Children were exposed to higher non-carcinogenic health risks compared to adults, with the non-carcinogenic risk for children being 1.5 times higher than that for adults.\u003c/p\u003e \u003cp\u003eThe carcinogenic potential of Ni, Pb, Cr and Cd is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Carcinogenic risk (CR) values greater than 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;4 indicate a high carcinogenic risk, with CR values between 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;6 and 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;4 being considered acceptable thresholds recommended by the USEPA, and CRs less than 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;6 being negligible levels (Sun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The mean CR for Ni for adults and children was greater than 1.0 \u0026times; 10\u0026thinsp;\u0026minus;\u0026thinsp;4, indicating that Ni poses a non-negligible carcinogenic risk. For Cr, the carcinogenic risk for both adults and children exceeded the safety threshold in only one water sample, and the CR for Pb and Cd were less than the safety threshold. Both carcinogenic and non-carcinogenic risks were more significant in children than in adults. This phenomenon may be attributed to the heightened sensitivity of children to heavy metals during their growth and development, coupled with their comparatively lower body weight, which renders them more susceptible to carcinogenic and non-carcinogenic risks (Sun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The findings underscore the inadequacy of shallow groundwater in the study area for consumption as drinking water. The presence of carcinogenic risk in the shallow groundwater of the study area has been attributed to anthropogenic disturbances (industrial and agricultural activities). Therefore, the shallow groundwater should be purified before use or consumption as municipal water (Sun et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study analyzed the content and spatial distribution characteristics of Mn, Ni, U, Zn, V, Cu, Cr, Cd and Pb. The sources of heavy metals were identified using correlation and principal component analysis. Finally, the health risk was calculated by combining this with a health risk assessment model. The conclusions that can be drawn from this study are as follows:\u003c/p\u003e \u003cp\u003e(1) The average concentrations of the heavy metals under scrutiny were found to be Mn\u0026thinsp;\u0026gt;\u0026thinsp;Ni\u0026thinsp;\u0026gt;\u0026thinsp;U\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cd\u0026thinsp;\u0026gt;\u0026thinsp;Pb. It was determined that one-quarter of the shallow groundwater samples had concentrations of Mn and Ni that exceeded the drinking water quality standards. The spatial distribution characteristics demonstrated that the groundwater with high concentrations of heavy metals was predominantly located in close proximity to urban and rural residential areas, as well as the dumping sites of mining waste.\u003c/p\u003e \u003cp\u003e(2) Principal component analysis (PCA) extracted three principal components (PCs), which cumulatively explained 73.391% of the total variance. PC1 accounted for 31.77% of the total variance, with Cr, Cd, Zn and Cu exhibiting higher loading values. Consequently, PC1 was identified as a indicator of pollution from agricultural surface sources. PC2 accounted for 28.04% of the total variance, with V, Ni, Pb and U exhibiting higher loading values. The PC2 were indicative of an industrial source. PC3 explained 13.58% of the overall variables and showed a significant positive loading with Mn. In combination with the results of previous studies, PC3 was considered as a natural background source. The PMF model indicated that mining activities, industrial sources and local geogenic processes were the main factors affecting groundwater quality, with contributions of 42.76%, 440.78% and 16.46%, respectively.\u003c/p\u003e \u003cp\u003e(3) The health risk assessment results demonstrated that Ni's carcinogenicity risk to adults and children was relatively high, and the health risk of point source pollution should also be considered.The carcinogenicity risk of Cr to adults and children exceeded the safety thresholds for only one sample, and the carcinogenicity risks of Pb and Cd were less than the safety thresholds. The carcinogenic and non-carcinogenic risks of heavy metals in the study groundwater were found to be higher for children than for adults. It is therefore recommended that shallow groundwater in the study area be consumed only after the necessary purification and that municipal water supplies be used as drinking water sources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Key Research and Development Project (2024ZD1004204), the Scientific Research Project of Anhui Colleges and Universities (2023AH052224 \u0026nbsp;and 2023AH052232),\u0026nbsp;Natural Science Research Project of Anhui Educational Committee (No. 2022AH050797), Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (No.EC2023012), Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters (No. 2022-MGDP-01), Independent Research Fund of the State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (No. SKLMRDPC19ZZ06), the Coal Research Centre for Comprehensive Prevention and Control of Mine Water Hazard (134092220042204), the Key Scientific Research Project of Suzhou University (2022yzd01), and the Outstanding Academic and Technical Backbone of Suzhou University (2020XJGG11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there is no conflict.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbba, S. 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Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a self-organizing map (SOM). \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, \u003cem\u003e257\u003c/em\u003e, 120664. https://doi.org/10.1016/j.jclepro.2020.120664\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heavy metals, Soruce identification, Groundwater, Health risk assessment","lastPublishedDoi":"10.21203/rs.3.rs-6247727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6247727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe availability of uncontaminated groundwater is of pivotal significance for the sustainable sustenance of human development. High concentrations of heavy metals in groundwater can pose substantial risks to human health. This study explores the spatial distribution patterns, sources of pollution, and health risk assessment of heavy metals (Mn, Ni, U, Zn, V, Cu, Cr, Cd and Pb) in the shallow groundwater of the Huainan coal mining area in China. The concentrations of Mn and Ni were found to be relatively high. The spatial distribution characteristics of the heavy metals were analyzed using inverse distance weighting, revealing that the spatial distribution of V, Ni, Cd, Pb and U was similar, suggesting characteristics of typical point source pollution. The PMF model indicated that mining activities, industrial sources and local geogenic processes were the main factors affecting groundwater quality, with contributions of 42.76%, 440.78% and 16.46%, respectively. The health risk assessment results demonstrate that the non-carcinogenic risk of each heavy metal is within the safety threshold; however, the carcinogenic risk posed by Ni should not be overlooked. It is observed that the carcinogenic risk and non-carcinogenic risk values for children exceed those for adults. Consequently, groundwater in the study area must undergo specific purification measures before utilization. The findings of this study offer a scientific foundation for ensuring the quality of groundwater and the safety of drinking water in plain areas affected by coal mining.\u003c/p\u003e","manuscriptTitle":"Spatial distribution, sources and health risk assessment of heavy metals in shallow groundwater in a typical coal mining area in Huainan, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 09:50:46","doi":"10.21203/rs.3.rs-6247727/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf2da5d5-fa7f-4df0-8410-9cc456bd0028","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-20T09:50:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 09:50:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6247727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6247727","identity":"rs-6247727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}