Hydrochemical characteristics analysis and human health assessment in the upper reaches of Zhang Wei River alluvial plain

Hydrochemical characteristics analysis and human health assessment in the upper reaches of Zhang Wei River alluvial plain | 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 Hydrochemical characteristics analysis and human health assessment in the upper reaches of Zhang Wei River alluvial plain Yang Meng, Zhaoji Zhang, Yuanjing Zhang, Yaci Liu, Mengqing Jiao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590270/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Groundwater is the critical resource for agricultural, industrial, and urban-rural water supplies in arid and semi-arid regions, especially in North China Plain. In this study, 64 groundwater samples were collected to assess the potential for groundwater development and utilization in the upper reaches of the Zhang Wei river. The hydrochemical analysis revealed that the groundwater is primarily composed of HCO3-Ca and HCO3-Na·Ca. The hydrochemical type is influenced by natural water-rock interactions, including evaporite dissolution, silicate weathering, and ion exchange processes. Isotope data for hydrogen and oxygen, characterized by positive deviations from the global meteoric water line, underscore the significant impact of evaporation in the region. An entropy weight index method was employed for water quality evaluation, revealing that over 89% of the samples complied with Class II standards for household use. Furthermore, more than 85% of the area’s groundwater exhibited Total Dissolved Solids (TDS) concentrations below 1,000 mg/L, indicating a predominance of soft water. However, toxic elements such as fluorine (F), iodine (I), and chromium (Cr) were found to exceed drinking water standards, posing a health risk. Particularly, the intake level of fluorine was above the permissible value, potentially causing non-carcinogenic risks to children and infants. In conclusion, while the overall groundwater quality is favorable for the region, the presence of geochemically derived toxic substances necessitates careful consideration of its suitability for drinking purposes. Hydrochemistry Water–rock interaction Hydrogen and oxygen isotopes Human health evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The subterranean aquifer reservoirs play a pivotal role in the socioeconomic endeavors across numerous regions worldwide. On a global scale, 65% of underground water is allocated for potable consumption, 20% for livestock and irrigation, and 15% for industrial manufacturing purposes (Chebet et al., 2020 ; Kadam et al., 2021 ; Wang and Ma, 2022 ; Fan et al., 2022 ; Liu et al., 2023 ). The North China Plain stands as one of the largest alluvial plains in China, renowned for its dense population (Xiao et al., 2022a ; Hao et al., 2023 ). Over the past several decades, burgeoning developmental strides have significantly augmented both population density and economic activities in this locale, consequently escalating the demand for water resources (Aji et al., 2008 ; Zhang et al., 2012 ; Li et al., 2016 ). Prolonged exploitation of groundwater has engendered an array of geological environmental concerns, encompassing land subsidence, deterioration in groundwater quality, as well as diminishment in river and spring discharge, potentially culminating in seasonal or permanent disappearance (Pietersen et al., 2021 ; Bhatt et al., 2024 ; Khan et al., 2024 ; Zhang et al., 2024 ). Henceforth, to foster the wholesome and sustainable utilization of groundwater resources, it is imperative to devise policy-driven long-term strategies and implement corresponding mitigation measures, with the aim of attaining commendable economic and ecological benefits on a global scale. The geochemical composition of groundwater is typically influenced by numerous natural factors, including the quality of recharge water, aquifer lithology, hydrodynamic properties, soil-water-rock interactions, residence time, and the intensity of evaporation (Wang et al., 2022 ; Balderer and Piatti, 2023 ). Furthermore, human activities also significantly influence the groundwater's chemical profile (Xiao et al., 2022a ). Hence, distinguishing between geological factors and anthropogenic influences on the hydrogeochemical composition is paramount. This differentiation serves as a prerequisite for gaining profound insights into groundwater geochemistry and water quality (Wang et al., 2024a ; Wang et al., 2024b ). The isotopic composition of hydrogen and oxygen serves as a potent tracer for water bodies and finds widespread application in the field of hydrogeochemistry (Carucci et al., 2012 ; Niu et al., 2020 ; Gupta et al., 2023 ). Within the hydrological cycle, the influence of isotopic fractionation results in an enrichment of lighter isotopes in water vapor, while heavier isotopes tend to remain in the liquid phase. Given the discernible isotopic discrepancies among waters sourced from various origins, latitudes, and elevations, the distinctive isotopic compositions of hydrogen and oxygen in water bodies assist in identifying sources and facilitate the examination of transport processes, thereby facilitating the tracking of water circulation (Joshi et al., 2018 ; Wang et al., 2021b ). Fluorine, an abundant element in the Earth's crust, predominantly enters groundwater through the dissolution of fluorine-bearing minerals and from anthropogenic contributions (Yadav et al., 2019 ; More et al., 2021 ). Typical fluorine-bearing minerals include fluorite, apatite, fluorapatite, cryolite, and topaz (Bretzler et al., 2017 ; Rashid et al., 2018 ; Dou et al., 2020 ). Fluoride is crucial for maintaining human health by helping prevent dental caries at low concentrations (Kabir et al., 2020 ). Nevertheless, prolonged exposure to high concentrations of fluoride can lead to severe health and hygiene issues, a scenario more commonly observed in rural areas of developing countries (Rehman et al., 2022 ; Xiao et al., 2022b ;Li et al., 2024 ). A plethora of research endeavors have delved into the origins and migration patterns of high-fluoride groundwater, encompassing both natural elements like topography, runoff dynamics, and aquifer constitution, and anthropogenic factors such as land usage and irrigation practices (Su et al., 2015 ; Ali et al., 2016 ; Li et al., 2019 ; Chen et al., 2023 ). Nevertheless, there remains a paucity of comprehensive investigation and application of these influential factors at the regional level. Thus, conducting a comprehensive analysis of the origin of high-fluoride groundwater in the southern region of the North China Plain carries significant scientific importance. This research endeavors to scrutinize the hydrogeochemical processes in the southern expanse of Zhang Wei River alluvial plain, through the utilization of hydrochemical and environmental isotope data. The specific objectives include (1) Unraveling the spatial distribution and evolutionary trends of groundwater hydrochemical parameters; (2) Identifying the primary controlling factors behind the hydrochemical evolution in the study area; (3) Utilizing isotopic methods to clarify the interactions of surface water and groundwater; (4) Evaluating the overall water quality and potential human health risks in the area is crucial. The findings of this study will serve as crucial references for groundwater development and utilization in Zhang Wei River alluvial plain. 2. Study area 2.1 Geography and Climate The study area, situated in the southern part of the North China Plain (refer to Fig. 1 ), it spans approximately 113° 52′ E to 115° 49′ E longitude and 36° 50′ N to 37° 47′ N latitude, covering an expanse of 12,000 km². Located at the intersection of the eastern foothills of the southern Taihang Mountains and the North China Plain, the area features a west-to-east gradient of terrain types, including mountains, hills, and terraced plains. The western Piedmont zone boasts elevations of approximately 100 meters, while the eastern and northern parts of the study area register elevations of around 30 meters. The region is characterized by a temperate, semi-humid to semi-arid continental monsoon climate. Its climatic profile is marked by relatively dry and low precipitation in spring, contrasted with hot and abundantly rainy summers. The mean annual temperature averages 14.36°C, with precipitation ranging between 400 mm and 600 mm annually. Precipitation exhibits significant seasonal variations, with uneven distribution throughout the year. The majority of rainfall occurs between June and September, with July and August experiencing the highest precipitation levels, accounting for over 55% of the annual rainfall. The mean annual water surface evaporation ranges from 1000 mm to 1800 mm. 2.2 Geology and Hydrogeology Spanning across two second-order tectonic units: the western Shanxi-Daertu Fault Uplift (Ⅱ23) and the eastern North China Fault Depression (Ⅱ24), the region exhibits intricate tectonic development with a complex and intersecting topography. In terms of geomorphology, the western segment of the research area encompasses a piedmont alluvial plain formed by the combined effects of pluvial and alluvial processes, while the eastern part consists of a central alluvial and lacustrine plain. The aquifer lithology within the study area predominantly comprises porous formations of unconsolidated rocks. The lithological composition primarily consists of sand-gravel, gravelly sand, gravel, coarse sand, and medium to fine sand. A systematic variation in lithology and thickness of the aquifer demonstrates a gradient from west to east, with a gradual decrease in grain size and thickness of the aquifer bed. The aquifer formations are distributed within the Quaternary unconsolidated rock layers, predominantly comprising alluvial deposits from the Hai River, Luan River, and ancient Yellow River. Within the study area, the Quaternary sediment thickness ranges approximately from 170 to 500 meters. The region is predominantly divided into two primary zones by the Fuyang River: west of the Fuyang River lies the freshwater zone, while to the east lies the saline water zone. Groundwater recharge sources include atmospheric precipitation, runoff recharge, lateral inflow, and irrigation return flow. Among these, atmospheric precipitation constitutes the primary recharge source, accounting for approximately 70% of the total groundwater recharge volume. Lateral inflow, notably significant in piedmont plains, typically ranges from 50 to 200 * 10 3 m 3 /(a.km), while in central plains, lateral inflow is comparatively minor, with a flow rate typically ranging from 2 to 7 * 10 3 m 3 /(a.km). The North China Plain, being a vital agricultural region for staple crops, has seen anthropogenic groundwater extraction emerge as a significant discharge method for confined aquifers in recent decades. Extensive exploitation of confined aquifers has not only led to the formation of large-scale cones of depression and other geological environmental issues within the study area but also caused significant disturbances to the hydrogeochemical evolution by altering the hydrodynamic conditions of confined aquifers. Moreover, agricultural production in many areas heavily relies on the use of fertilizers and pesticides. The toxic substances present in these products are introduced into the aquifer through irrigation water. Additionally, the high permeability of foothill areas enhances the infiltration of pollutants into the aquifer from both point and non-point sources, driving the movement of pollutants along groundwater flow paths. The dense human activities in the plain areas also contribute to the burden of surface pollutants. These elements together pose significant challenges to the hydrochemical quality of the confined groundwater in the study region. 3. Materials and methods 3.1 Sample collection and testing In June 2020, a total of 64 water samples were collected, in which 34 samples collected from phreatic aquifers and 30 samples collected from confined aquifers (Fig. 1 ). Details regarding groundwater and sampling depths are listed in Table S2. Sampling took place during the dry season, spanning from May to June 2020. To ensure that the samples reflected the true groundwater conditions, each well was pumped for 15 minutes before sampling to remove any stagnant water. In-situ measurements of groundwater temperature, pH, and electrical conductivity (EC) were conducted. Once these physicochemical parameters stabilized, water samples were collected in polyethylene bottles. Each target groundwater sample was pre-rinsed by flushing 3 to 5 times before collection. Samples were then collected according to specific detection requirements, carefully sealed, and individually labeled. All samples were stored in portable coolers and transported to the laboratory at 4℃. They were delivered to the laboratory for analysis within 48 hours of collection. Field measurements of pH and electrical conductivity (EC), along with other physical parameters, were conducted using a portable multi-parameter instrument (Multi 350i/SET, Munich, Germany). Total dissolved solids (TDS) were determined through gravimetric analysis. Bicarbonate ions (HCO- 3) were quantified via acid-base titration. Major cations, including calcium (Ca 2+ ), magnesium (Mg 2+ ), sodium (Na + ), and potassium (K + ), were analyzed using inductively coupled plasma mass spectrometry (ICAP6300, Thermo Fisher, Los Angeles, CA, USA). Ammonium (NH + 4) and anions, such as chloride (Cl − ), sulfate (SO2- 4 ) , nitrate (NO- 3), and nitrite (NO- 2), were measured using gas chromatography-mass spectrometry (GC-MSQP2010plus, Yi Dian, Shanghai, China). Isotopic analyses of δ 2 H and δ 18 O were performed with an isotope ratio mass spectrometer (DLT-100, LGR Company, San Jose, CA, USA). The ion analyses were carried out at the Water Environment Detection Center of the Hebei Institute of Geological Environmental Monitoring, while the isotope analyses were conducted at the Laboratory of Groundwater Science and Engineering, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences. The stable isotopes of hydrogen and oxygen ( 2 H and 18 O) were determined using a liquid water isotope analyzer (Finnigan TMMAT253, Germany), with δ 18 O and δ 2 H values reported relative to the Vienna Standard Mean Ocean Water (VSMOW) standard. The analytical uncertainties were 0.2‰ for δ 18 O and 1.0‰ for δ 2 H. 3.2 Entropy-weighted water quality index Compared to other water quality assessment methods, the advantage of the entropy weight index method lies in its incorporation of the entropy concept, which minimizes the subjective influence on the weights of the evaluation factors. The EWQI evaluation entails four primary stages: identification of evaluation indicators, normalization, weight assignment to indicators, and calculation of entropy-weighted water quality indices. (Gorgij and Moayeri, 2023 ; Gao et al., 2024 ). Firstly, select evaluation indicators and establish an initial eigenvalue matrix as follows: $$\left. {X=\left[ {\begin{array}{*{20}{c}} {{x_{11}}}&{{x_{12}}}&{}&{{x_{1n}}} \\ {{x_{21}}}&{{x_{22}}}&{}&{{x_{2n}}} \\ \vdots &{}& \ddots & \vdots \\ {{x_{m1}}}&{{x_{m2}}}& \cdots &{{x_{mn}}} \end{array}} \right.} \right]$$ 2.1 In this context, X represents the initial feature value matrix, where X ij denotes the value of the j th evaluation factor for the i th water quality sample. Next, the eigenvalue matrix should be normalized. Given the diverse units and substantial dimensional variations across the indicators in matrix X, normalizing the initial matrix becomes imperative to yield a standardized matrix Y. This normalization process involves deducting the ratio of each indicator's actual value to its range from the maximum value of each indicator, thereby constituting the elements of the new matrix Y. The computation of matrix Y adheres to the equations specified: $$\left. {Y=\left[ {\begin{array}{*{20}{c}} {{y_{11}}}&{{y_{12}}}&{}&{{y_{1n}}} \\ {{y_{21}}}&{{y_{22}}}&{}&{{y_{2n}}} \\ \vdots &{}& \ddots & \vdots \\ {{y_{m1}}}&{{y_{m2}}}& \cdots &{{y_{mn}}} \end{array}} \right.} \right]$$ 2.2 $${y_{ij}}=\left\{ {\begin{array}{*{20}{c}} {\frac{{{x_{ij}} - min\{ {x_{1j}},{x_{2j}}, \cdots ,x\} }}{{max\{ {x_{1j}},{x_{2j}}, \cdots ,x\} - min\{ {x_{1j}},{x_{2j}}, \cdots ,x\} }}} \\ {\frac{{max\{ {x_{1j}},{x_{2j}}, \cdots ,x\} - {x_{ij}}}}{{max\{ {x_{1j}},{x_{2j}}, \cdots ,x\} - min\{ {x_{1j}},{x_{2j}}, \cdots ,x\} }}} \end{array}} \right.$$ 2.3 Within the context of the standardized matrix Y, the elements are denoted by y ij . The term max{x 1j , x 2j , …, x nj } signifies the maximum value within a specific column of matrix Y, while min{x 1j , x 2j , …, x nj } indicates the minimum value within the same column of the matrix Y. Next, the weights for each hydrochemical indicator must be established. Due to the varying importance of different indicators in assessing groundwater quality, it is essential to assign specific weights to each indicator during the evaluation process. To enhance the objectivity and rationality of the evaluation, we utilize the entropy weight method to allocate weights to each indicator. This allocation can be performed in accordance with equations ( 2.4 , 2.5 , 2.6 ): Here, p ij means the proportion of the hydrochemical parameter for each sample, whereas ei represents the entropy information associated with each index, ω j means the weight assigned to a particular hydrochemical index. Next, compute the EWQI value. Prior to determining the EWQI value, it is necessary to quantitatively grade each hydrochemical index (q j ), as shown in Eq. ( 2.7 ). Calculate EWQI by combining the values of ω j and q j as outlined below: In the equation, x j represents the measured values of various parameters in the samples, meanwhile, s j means the maximum permissible limit set forth by either the World Health Organization or Chinese regulatory guidelines for the chemical parameter j. Based on the EWQI value, groundwater quality can be categorized into five distinct classifications, namely extremely excellent, excellent, good, poor, and extremely poor. The corresponding numerical ranges are 0–50, 50–100, 100–150, 150–200, and > 200. 3.3 Human Health Risk Assessment With the progress of industrialization, the influence of groundwater contaminants on human health has become increasingly significant. Therefore, it is crucial to accurately and reasonably assess the health risks posed by groundwater contaminants. This study employs the human health risk assessment model recommended by the United States Environmental Protection Agency (USEPA, 1989 ), with a focus on two primary exposure pathways: dermal contact and direct ingestion. The aim is to quantify the non-carcinogenic risks associated with different exposure routes and conduct a comprehensive evaluation of groundwater quality in the study area. Considering that fluoride ions (F − ) exceed allowable limits in multiple locations and are categorized as non-carcinogenic, this research specifically emphasizes the potential non-carcinogenic risks associated with fluoride exposure. The formula for evaluating human health risks through direct ingestion is as follows: (Adimalla et al., 2019 ; Yousefi et al., 2018 ; Alam et al., 2024 ): $$CD{I_{oral}}=({C_i} \times IR \times EF \times ED) \div (BW \times AT)$$ 3.1 $$H{Q_{oral}}=CD{I_{oral}} \div Rf{D_{oral}}$$ 3.2 $$H{I_{oral}}=H{Q_{oral,1}}+H{Q_{oral,2}}+ \cdots +H{Q_{oral,i}}$$ 3.3 Where CDI oral is the chronic daily intake dose via the drinking water intake pathway [mg/(kg*day)]. C i represents the concentration of the target contaminants in groundwater (mg/L). IR refers to the ingestion rate of drinking water (L/day). EF denotes exposure frequency (days/year). ED indicates exposure duration (years). BW is the average body weight (kg). AT expresses the average time(days). HQ oral is the hazard quotient of non-carcinogenic risk due to the drinking water intake pathway. RfD oral denotes the reference dose of a specific pollutant [mg/(kg*day)] through the drinking water intake pathway. HI oral represents the overall non-carcinogenic health risks of multiple contaminants (1, 2, …, i) by drinking water intake pathway. The formula for evaluating human health through dermal intake is as follows: $$CD{I_{dermal}}=({C_i} \times K \times {S_a} \times T \times EF \times ED \times EV \times CF) \div (BW \times AT)$$ 3.4 $${S_a}=239 \times {H^{0.417}} \times B{W^{0.517}}$$ 3.5 $$H{Q_{dermal}}=CD{I_{dermal}} \div Rf{D_{dermal}}$$ 3.6 $${\text{Rf}}{{\text{D}}_{dermal}}={\text{Rf}}{{\text{D}}_{{\text{oral}}}} \div AB{{\text{S}}_{{\text{gi}}}}$$ 3.7 Where CDI dermal represents the daily exposure dose via dermal contact [mg/(kg*day)]. K is the skin permeability coefficient (cm/h). S a , T, EV, CF, and H correspond to the skin surface area, contact duration (h/d), the exposure frequency of daily dermal contact (time/day, EV = 1 day), and the unit conversion factor (L/cm3), the average body height (cm). RfD dermal denotes the reference dose for a specific contaminant [mg/(kg*day)] through the dermal contact. ABS gi indicates the gastrointestinal absorption factor. All parameters utilized in this study are listed in Table S1 . (HI total ) The overall risk of pollutants ingested through drinking water and dermal to human health is as follows: $$H{I_{total}}=H{I_{oral}}+H{I_{dermal}}$$ 3.8 4. Results and discussion 4.1 Analysis of hydrochemical characteristics of groundwater Hydrochemical parameters for groundwater samples in the study area were listed in Table 1 . pH values range from 7.11 to 9.70, averaging 7.93, indicating predominantly neutral to alkaline groundwater, consistent with Chinese drinking water guidelines (6.5–8.5) (GAQS, 2017). EC values vary from 403 to 873 µ S/cm, with an average of 566.10 µ S/cm. TDS concentrations range between 154 and 1766 mg/L, with an average of 676.91 mg/L. According to the TDS classification by the United States Geological Survey (USGS, 2000 ), the majority (85.93%) with salinity levels below 1000 milligrams per liter fall into the freshwater category, while 14.06% fall into the brackish water category. There are three main ionic forms for nitrogen: NO- 3, NO- 2, and NH + 4. The NO- 3concentration ranges from 0.108 mg/L and 24.20 mg/L, with an average of 3.33 mg/L.NO- 2 concentrations vary between 0 and 0.08 mg/L, averaging 0.01 mg/L. NH + 4 concentrations lie between 0.02 mg/L and 0.06 mg/L, with an average of 0.02 mg/L. Analysis indicates that approximately 4.68% of samples surpass the Chinese drinking water standard for NO- 3 (20 mg/L). However, NO- 2 and NH + 4 concentrations remain within acceptable limits per Chinese drinking water standards (GAQS, 2017). Thus, certain areas in the study region are contaminated with nitrate, while the overall environmental pollution situation regarding nitrite and ammonia nitrogen is satisfactory. The order of main cation concentrations in groundwater samples: Ca 2+ > Na + > Mg 2+ > K + . Ca 2+ is the predominant cation, with concentrations ranging from 1.92 mg/L and 182 mg/L, an average of 62.01 mg/L. Na + ranks second, with concentrations from 6.82 mg/L and 509 mg/L, an average of 149.79 mg/L. Approximately 23.44% of groundwater samples exceed the World Health Organization water quality standard for Na + (WHO, 2017 ). Mg 2+ concentrations range from 5.01 mg/L and 189 mg/L, an average of 32.42 mg/L. The high sodium ion content in the area, particularly in samples with high concentrations, mainly originates from the confined aquifers. This phenomenon is primarily attributed to the leaching action of shallow groundwater. (Hao et al., 2020 ) The order of major anion concentrations is as follows: HCO- 3 > SO2- 4 > Cl − > NO- 3. The highest concentration of anions is found in HCO- 3, with an average of 312.92 mg/L and spanning from 113 mg/L to 946 mg/L. SO2- 4 concentrations vary between 24.8 mg/L and 443 mg/L, averaging 133.59 mg/L. Cl − concentrations range from 8.51 mg/L to 539 mg/L, with an average of 127.98 mg/L. Additionally, analysis of F − and Cr 6+ revealed that 17.19% of samples exceeded the standard for fluoride ions, hexavalent chromium ions are of particular concern, with an average concentration of 0.68 mg/L. The maximum concentration detected is 0.037 mg/L, which is 3.7 times the permissible limit, and 14.06% of the samples exceed this standard. Table 1 Statistical overview of various groundwater parameters in the study region Index Unit Min Max Mean SD* Guideline % of the sample exceeding the Guideline pH 7.11 9.7 7.93 0.48 6.5–8.5** 7.81% EC uS/cm 403 873 566.10 144.28 / / TH mg/L 42 1077 288.09 205 450** 23.44% TDS mg/L 154 1766 676.91 326.36 1000** 14.06% Ca 2+ mg/L 1.92 182 62.01 44.38 300** / Mg 2+ mg/L 5.01 189 32.42 32.39 100** 3.13% Na + mg/L 6.82 509 149.79 109.73 200/ 23.44%/ K + mg/L 0.47 5.67 1.59 1.04 / / C1 − mg/L 8.51 539 127.98 109.82 250** 10.94% SO2- 4 mg/L 24.8 443 133.59 84.82 250** 10.94% HCO- 3 mg/L 113 946 312.92 154.4 250 / NO- 3 mg/L 0.108 24.2 3.33 5.37 20** 4.69% NO- 2 mg/L 0 0.08 0.01 0.02 0.02*** 4.69% NH + 4 mg/L 0.02 0.06 0.02 0.13 0.2*** 4.69% I − mg/L 0.002 0.325 0.09 0.11 0.1*** 7.81% F − mg/L 0.119 3.41 0.68 0.63 1*** 17.19% Cr 6+ mg/L 0.004 0.037 0.01 0.01 0.005*** 14.06% * Standard Deviation. ** WHO Drinking Water Standards (WHO, 2017 ). *** China Groundwater Quality Standards (GAQS, 2017). 4.2 Hydrogeochemical type The Piper diagram summarizes the chemical composition of groundwater (Fig. 2 ). It can be observed that all samples are dominated by Na + and K + as the main cations, with approximately 83.33% of the water samples classified as sodium-potassium-type groundwater, and 20.31% as calcium-type water. Most samples show HCO- 3 as the dominant anion, though some samples exhibit Cl − predominance. Specifically, HCO- 3 and Cl − account for 89.06% and 9.37% of the overall samples, respectively. Additionally, the hydrochemical facies of groundwater were assessed utilizing the Piper diagram (Piper, 1944 ). Within the diamond-shaped field, hydrochemical types include HCO 3 -Ca, mixed HCO 3 -Na·Ca, and Cl-Na waters. The majority of water samples (39.06%) belong to the HCO 3 -Ca type, indicating good water quality. 31.25% of water samples are classified as mixed HCO 3 -Na·Ca type, while 21.87% are saline Cl-Na type waters. It's apparent that confined groundwater predominantly exhibits the Cl-Na type, depicting a shift from mixed Cl-Mg·Ca type water to Cl-Na type water, indicating progressive salinity in deeper groundwater. Conversely, the general trend for shallow groundwater is transitioning from HCO 3 -Ca type water to HCO 3 -Na type water. Groundwater chemistry is primarily influenced by three natural processes: atmospheric precipitation, evaporation, and interactions with geological formations. The Gibbs diagram is extensively used to illustrate groundwater ion characteristics, identify the principal sources of ions, and ascertain the key factors affecting groundwater chemistry (Gibbs, 1970 ). In this study, the Gibbs diagram (Fig. 3 ) was utilized, revealing that most samples are predominantly influenced by rock weathering. This implies that interactions with geological formations are the predominant natural drivers shaping groundwater chemistry in the study area (Xiao et al., 2022b ). Combining the local hydrological conditions, it is observed that groundwater in the aquifers exhibits low flow velocities and relatively long residence times, providing ample time for water-rock interactions. Additionally, considering prior research findings, it is clear that ion exchange and mineral dissolution processes play a significant role in shaping hydrochemistry and groundwater types (Pei et al., 2023 ; Yang et al., 2023 ). To delve deeper into the rock control within water-rock interactions, employing end-member diagrams can elucidate the impact of various rock types on the ion composition of groundwater. As shown in Fig. 4 , it is evident that all samples are situated in the transition zone from silicates to carbonates. This indicates a significant involvement of both silicates and carbonates in water-rock interactions, suggesting that groundwater contains a substantial amount of components from both types of rocks/minerals. The saturation indices of the selected minerals are illustrated in Fig. 5 . Typically, when the saturation index (SI) is less than zero, it signifies that the mineral is undergoing dissolution into the groundwater. An SI value equal to zero denotes a state of saturation. Conversely, an SI value exceeding zero signifies the active precipitation of a mineral. The analysis reveals that the saturation indices (SI) values for most groundwater samples indicate that the SI values for calcite, dolomite, and aragonite are above 0, implying ongoing precipitation of these minerals. This observation aligns with the conclusions drawn from the end-member analysis. This further elucidates that carbonates in the study region are not predominantly derived from the dissolution of minerals like calcite, dolomite, and aragonite. In contrast, the SI values for halite, gypsum, and anhydrite are all below zero, suggesting the ongoing dissolution of these minerals into the groundwater. Additionally, the SI values of fluorite and potassium salts are also less than 0, indicating ongoing dissolution of these minerals and their entry into groundwater, which adequately explains the elevated levels of F − and K + in groundwater samples. 4.3 Isotopic characteristics Evaporation significantly influences the groundwater chemistry in the study area. Deuterium and oxygen-18 isotopes are crucial for understanding the impact of evaporation on groundwater's chemical composition (Carucci et al., 2012 ; Wang et al., 2021a ). The relationship between δ 2 H and δ 18 O in groundwater and surface water is depicted in Fig. 6 . All groundwater samples are positioned below the Global Meteoric Water Line (GMWL), highlighting a strong evaporative effect on shallow groundwater in the region. By analyzing shallow groundwater sample data, a linear regression established the Local Meteoric Water Line (LMWL) for the area, resulting in the equation: Y = 5.88X-14.04. To investigate the recharge sources of shallow groundwater in the study area, we systematically analyzed the data using the Global Meteoric Water Line (GMWL) equation (δ 2 H = 8.00*δ 18 O + 10.00) as outlined by the International Atomic Energy Agency. The δ18O values for groundwater in the area ranged from − 11.53‰ to -3.98‰, averaging − 8.18‰, while the δ 2 H values varied from − 74.82‰ to -40.93‰, with a mean of -60.31‰. As shown in Fig. 6 , all groundwater samples are positioned below the global meteoric water line. Through linear regression analysis of the δ 18 O and δ 2 H values of groundwater, we derived the equation for the evaporation line as δ 2 H = 5.88*δ 18 O − 14.04. In comparison to the regional precipitation line, the slope and intercept are notably smaller, indicating a pronounced influence of non-equilibrium evaporation on the groundwater replenishment process from atmospheric precipitation. This suggests that intense evaporation significantly affects the enrichment and transport of δ 18 O and δ 2 H in groundwater, in alignment with the findings derived from the analysis of groundwater hydrochemistry within the study area. The hydrogen and oxygen isotope values of most surface water samples exceed the regional precipitation line, but the same values in the Shahe segment falls below the Global Meteoric Water Line (GMWL). This phenomenon emanates from the fact that the sampling site is situated within the western Piedmont alluvial plain of Zhangwei, leading to the supply of mountain springs or fissure water to the river surface water. In comparison to other surface water samples, this supply results in a more pronounced enrichment of heavy isotopes, consequently, the hydrogen and oxygen isotope values are observed to be lower than the Global Meteoric Water Line (GMWL). 4.4 Water quality evaluation The entropy-weighted water quality index (EWQI) is widely employed for an in-depth evaluation of groundwater quality. In this investigation, cations (Ca 2+ , Mg 2+ , Na + , K + ), anions (Cl − , SO2 − 4, HCO − 3, F − , and NO − 3), in addition to pH, EC, TH, and TDS, were chosen as assessment parameters. The outcomes of water quality evaluation yielded EWQI values ranging from 4 to 284. The assessment findings of EWQI depict a spectrum of groundwater quality from excellent (Grade 1) to very poor (Grade 5). There are 57 groundwater samples classified as Grade 1 and Grade 2 (EWQI values < 100), accounting for 89.06% of the total samples. Additionally, 9.38% of groundwater samples (6 samples) have EWQI values ranging from 50 to 100, while only one sample has an EWQI value exceeding 150. In this study, over 89% of the groundwater samples meet the drinking water standards. Among them, groundwater samples with poorer quality (S28 and S29) exhibit higher TDS levels (S28: 1446 mg/L; S29: 1489 mg/L) and are enriched with F − (S28: 1.73 mg/L; S29: 1.1 mg/L). Combining data analysis reveals that detection points with high TDS and elevated F − ions are predominantly distributed in the deep groundwater zones. The exceedance of these two parameters is the primary driving factor behind the deterioration of water quality in the region. This discovery directly affirms the preceding analysis and further substantiates that the interaction between deep-seated groundwater and geological formations is the primary factor contributing to the aberrant groundwater quality observed in the study locale. 4.5 Risks assessment for human health Health risk assessment necessitates the application of a quantitative model to systematically evaluate the potential health hazards stemming from exposure to contaminated groundwater. In this research, fluoride ions were selected for non-carcinogenic health risk assessment due to the potential health issues, such as dental and skeletal fluorosis, that can arise from prolonged ingestion of groundwater with fluoride levels exceeding acceptable limits. Therefore, we employed a methodology involving the computation of the Hazard Quotient (HQ) values through skin contact and oral ingestion pathways across different demographic groups, including adults, children, and infants, to evaluate non-carcinogenic health risks. The cumulative noncarcinogenic risk values for adult females, adult males, children, and infants are presented in Table 2 . Statistical analysis of health risk assessment regarding fluoride ions, as depicted in Table 2 , reveals significant differences in non-carcinogenic effects between the two groups of groundwater. In terms of non-carcinogenic risks from consuming shallow groundwater, infants exhibit an average risk value of 1.21 and a peak value of 4.04. For deep groundwater, these values rise to an average of 2.05 and a maximum of 8.06. A comparative analysis of the two datasets indicates that pollutants in deep groundwater exhibit higher extreme values than those in shallow groundwater, suggesting a more prevalent contamination scenario in deep groundwater, hence the higher average values. Furthermore, a detailed examination reveals that the hazard indices for confined water are significantly larger compared to shallow water, with the hazard index for infants approximately twice that of adults, indicating a gradual reduction in health risks with increasing age. In summary, fluoride ions in the study area pose certain health risks, with children consuming highly contaminated groundwater more susceptible to health complications. Table 2 Statistics of Human Health Risk Assessment Results MIN MAX AVERAGE SD Phreatic groundwater Infant 0.28 4.04 1.21 0.80 Children 0.17 2.50 0.75 0.50 Females 0.12 1.80 0.54 0.36 Males 0.15 2.14 0.64 0.42 MIN MAX AVERAGE SD Confined groundwater Infant 0.37 8.06 2.05 1.89 Children 0.23 4.99 1.27 1.17 Females 0.16 3.58 0.91 0.84 Males 0.19 4.26 1.08 1.00 5. Conclusions In the North China Plain region, long-standing issues such as groundwater overexploitation, random discharge of wastewater, and extensive use of fertilizers and pesticides have posed significant threats to groundwater quality. This study offers a comprehensive examination of the factors affecting groundwater quality in the southern region of the North China Plain. Through the introduction of environmental isotope techniques, an analysis of groundwater recharge conditions is conducted, and efforts have been made to adopt water quality assessment methods to evaluate the suitability of groundwater and its potential health risks to humans. The study concludes the following: According to the evaluation results of the Environmental Water Quality Index (EWQI), there are a total of 57 groundwater samples with water quality grades of 1 and 2 (EWQI value < 100), accounting for 89.06% of the total number of samples. Overall, the groundwater quality tends toward favorable conditions, predominantly comprising fresh water (85.94%) or moderately hard fresh water (10.93%). Analysis of the human health risk assessment results indicates that groundwater poses relatively minor potential human health risks compared to pressurized water sources. Under the influence of various pollutants, the health risks to infants are twice that of adults, with fluoride ions (F − ) and iodine ions (I − ) primarily constituting the threats to human health. The hydrochemical makeup of groundwater is predominantly shaped by natural hydrogeological processes, prominently featuring the dissolution of rock salt, gypsum, anhydrite, and silicates, alongside ion exchange reactions, which constitute the principal formative reactions for both phreatic and confined water chemical components. Additionally, the chemical attributes of phreatic water are subject to influence from both evaporation and anthropogenic inputs, albeit within a constrained range and magnitude. In the exploitation of confined water for drinking purposes, special consideration should be given to hazardous compounds, especially concerns revolving around elevated fluoride levels. Given the intensive exploitation of confined aquifers within the study area driven by socio-economic advancements, there is a pressing need for vigilance concerning human-induced alterations to pressurized water systems in the locality. Declarations Competing interests The authors declare no competing interests. Author Contribution Yang Meng: Investigation, Data analysis, Writing – original draft, preparation. Zhaoji Zhang: Conceptualization, Validation, Methodology, Yuanjing zhang: Methodology, Writing-Reviewing. Yaci Liu: Methodology, Formal analysis. Mengqing Jiao: Data analysis, Methodology, Data curation. Yasong Li: Formal analysis, Writing - review and editing. All authors have read and agreed to the submitted version of the manuscript. Acknowledgments This research was financially supported by the Basic research business fee project of central level public welfare research institutes (Grant No. SK202309) And the Open Fund of Key Laboratory (Grant No. WSRCR-2023-01). 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Journal of Jilin University(Earth Science Edition) 42, 1456–1461. https://doi.org/10.13278/j.cnki.jjuese.2012.05.014. (in Chinese) Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Revision requested 31 Jan, 2025 Reviews received at journal 19 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers invited by journal 01 Oct, 2024 Editor assigned by journal 17 Jun, 2024 Submission checks completed at journal 17 Jun, 2024 First submitted to journal 16 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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2","display":"","copyAsset":false,"role":"figure","size":9672540,"visible":true,"origin":"","legend":"\u003cp\u003ePiper three-line diagram of ground-water in the study area\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/cdf9446e6c08ea9d8ff2e099.jpg"},{"id":60353338,"identity":"fbafd592-d4ec-4781-a57c-104e97f45b7c","added_by":"auto","created_at":"2024-07-15 23:40:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8329478,"visible":true,"origin":"","legend":"\u003cp\u003eControl mechanism of Gibbs groundwater chemistry Gibbs\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/1714401cea0e904560bc96aa.jpg"},{"id":60355025,"identity":"ced3dbba-d8e3-4221-ae5c-24664ebb29e0","added_by":"auto","created_at":"2024-07-15 23:56:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1888463,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of (a) HCO3/Na and Ca/Na, and (b) Mg/Na and Ca/Na\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/0bdfee72c1384393f512f8cf.jpg"},{"id":60353938,"identity":"bb5c6479-a3f1-4365-a2e6-3aa81220f830","added_by":"auto","created_at":"2024-07-15 23:48:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6502928,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of groundwater mineral saturation indicators\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/edde9fafb7b67163b17dcbc3.jpg"},{"id":60353936,"identity":"89c3d57b-0f37-4dfa-94d9-eb5ea27b7cec","added_by":"auto","created_at":"2024-07-15 23:48:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6478586,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the Relationship between Surface Water and Groundwater δ D - δ 18O in the Study Area\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/3e5f3ce4b1bb1a685d056aea.jpg"},{"id":60353332,"identity":"a1060569-ebf5-484c-9636-95aaf729474a","added_by":"auto","created_at":"2024-07-15 23:40:31","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2332156,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of groundwater EWQI and TDS\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/ed5eb4fdaddc6059f091f822.jpg"},{"id":79120416,"identity":"88b2678c-7b72-42a9-8be1-375d1a0715d7","added_by":"auto","created_at":"2025-03-24 16:07:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36697479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/c97615c9-8f79-4898-bc45-0e204e8fd9f7.pdf"},{"id":60353331,"identity":"a58f99be-b4f4-4654-bf53-a017a0db5c4f","added_by":"auto","created_at":"2024-07-15 23:40:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40307,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4590270/v1/e3b254bfb11ace54b0915f1d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydrochemical characteristics analysis and human health assessment in the upper reaches of Zhang Wei River alluvial plain","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe subterranean aquifer reservoirs play a pivotal role in the socioeconomic endeavors across numerous regions worldwide. On a global scale, 65% of underground water is allocated for potable consumption, 20% for livestock and irrigation, and 15% for industrial manufacturing purposes (Chebet et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kadam et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang and Ma, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The North China Plain stands as one of the largest alluvial plains in China, renowned for its dense population (Xiao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Hao et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Over the past several decades, burgeoning developmental strides have significantly augmented both population density and economic activities in this locale, consequently escalating the demand for water resources (Aji et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Prolonged exploitation of groundwater has engendered an array of geological environmental concerns, encompassing land subsidence, deterioration in groundwater quality, as well as diminishment in river and spring discharge, potentially culminating in seasonal or permanent disappearance (Pietersen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bhatt et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Henceforth, to foster the wholesome and sustainable utilization of groundwater resources, it is imperative to devise policy-driven long-term strategies and implement corresponding mitigation measures, with the aim of attaining commendable economic and ecological benefits on a global scale.\u003c/p\u003e \u003cp\u003eThe geochemical composition of groundwater is typically influenced by numerous natural factors, including the quality of recharge water, aquifer lithology, hydrodynamic properties, soil-water-rock interactions, residence time, and the intensity of evaporation (Wang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Balderer and Piatti, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, human activities also significantly influence the groundwater's chemical profile (Xiao et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). Hence, distinguishing between geological factors and anthropogenic influences on the hydrogeochemical composition is paramount. This differentiation serves as a prerequisite for gaining profound insights into groundwater geochemistry and water quality (Wang et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). The isotopic composition of hydrogen and oxygen serves as a potent tracer for water bodies and finds widespread application in the field of hydrogeochemistry (Carucci et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Niu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gupta et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Within the hydrological cycle, the influence of isotopic fractionation results in an enrichment of lighter isotopes in water vapor, while heavier isotopes tend to remain in the liquid phase. Given the discernible isotopic discrepancies among waters sourced from various origins, latitudes, and elevations, the distinctive isotopic compositions of hydrogen and oxygen in water bodies assist in identifying sources and facilitate the examination of transport processes, thereby facilitating the tracking of water circulation (Joshi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFluorine, an abundant element in the Earth's crust, predominantly enters groundwater through the dissolution of fluorine-bearing minerals and from anthropogenic contributions (Yadav et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; More et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Typical fluorine-bearing minerals include fluorite, apatite, fluorapatite, cryolite, and topaz (Bretzler et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rashid et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dou et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Fluoride is crucial for maintaining human health by helping prevent dental caries at low concentrations (Kabir et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nevertheless, prolonged exposure to high concentrations of fluoride can lead to severe health and hygiene issues, a scenario more commonly observed in rural areas of developing countries (Rehman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xiao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e;Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A plethora of research endeavors have delved into the origins and migration patterns of high-fluoride groundwater, encompassing both natural elements like topography, runoff dynamics, and aquifer constitution, and anthropogenic factors such as land usage and irrigation practices (Su et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ali et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, there remains a paucity of comprehensive investigation and application of these influential factors at the regional level. Thus, conducting a comprehensive analysis of the origin of high-fluoride groundwater in the southern region of the North China Plain carries significant scientific importance.\u003c/p\u003e \u003cp\u003eThis research endeavors to scrutinize the hydrogeochemical processes in the southern expanse of Zhang Wei River alluvial plain, through the utilization of hydrochemical and environmental isotope data. The specific objectives include (1) Unraveling the spatial distribution and evolutionary trends of groundwater hydrochemical parameters; (2) Identifying the primary controlling factors behind the hydrochemical evolution in the study area; (3) Utilizing isotopic methods to clarify the interactions of surface water and groundwater; (4) Evaluating the overall water quality and potential human health risks in the area is crucial. The findings of this study will serve as crucial references for groundwater development and utilization in Zhang Wei River alluvial plain.\u003c/p\u003e"},{"header":"2. Study area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geography and Climate\u003c/h2\u003e \u003cp\u003eThe study area, situated in the southern part of the North China Plain (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), it spans approximately 113\u0026deg; 52\u0026prime; E to 115\u0026deg; 49\u0026prime; E longitude and 36\u0026deg; 50\u0026prime; N to 37\u0026deg; 47\u0026prime; N latitude, covering an expanse of 12,000 km\u0026sup2;. Located at the intersection of the eastern foothills of the southern Taihang Mountains and the North China Plain, the area features a west-to-east gradient of terrain types, including mountains, hills, and terraced plains. The western Piedmont zone boasts elevations of approximately 100 meters, while the eastern and northern parts of the study area register elevations of around 30 meters. The region is characterized by a temperate, semi-humid to semi-arid continental monsoon climate. Its climatic profile is marked by relatively dry and low precipitation in spring, contrasted with hot and abundantly rainy summers. The mean annual temperature averages 14.36\u0026deg;C, with precipitation ranging between 400 mm and 600 mm annually. Precipitation exhibits significant seasonal variations, with uneven distribution throughout the year. The majority of rainfall occurs between June and September, with July and August experiencing the highest precipitation levels, accounting for over 55% of the annual rainfall. The mean annual water surface evaporation ranges from 1000 mm to 1800 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Geology and Hydrogeology\u003c/h2\u003e \u003cp\u003eSpanning across two second-order tectonic units: the western Shanxi-Daertu Fault Uplift (Ⅱ23) and the eastern North China Fault Depression (Ⅱ24), the region exhibits intricate tectonic development with a complex and intersecting topography. In terms of geomorphology, the western segment of the research area encompasses a piedmont alluvial plain formed by the combined effects of pluvial and alluvial processes, while the eastern part consists of a central alluvial and lacustrine plain. The aquifer lithology within the study area predominantly comprises porous formations of unconsolidated rocks. The lithological composition primarily consists of sand-gravel, gravelly sand, gravel, coarse sand, and medium to fine sand. A systematic variation in lithology and thickness of the aquifer demonstrates a gradient from west to east, with a gradual decrease in grain size and thickness of the aquifer bed. The aquifer formations are distributed within the Quaternary unconsolidated rock layers, predominantly comprising alluvial deposits from the Hai River, Luan River, and ancient Yellow River. Within the study area, the Quaternary sediment thickness ranges approximately from 170 to 500 meters. The region is predominantly divided into two primary zones by the Fuyang River: west of the Fuyang River lies the freshwater zone, while to the east lies the saline water zone. Groundwater recharge sources include atmospheric precipitation, runoff recharge, lateral inflow, and irrigation return flow. Among these, atmospheric precipitation constitutes the primary recharge source, accounting for approximately 70% of the total groundwater recharge volume. Lateral inflow, notably significant in piedmont plains, typically ranges from 50 to 200 * 10\u003csup\u003e3\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e/(a.km), while in central plains, lateral inflow is comparatively minor, with a flow rate typically ranging from 2 to 7 * 10\u003csup\u003e3\u003c/sup\u003e m\u003csup\u003e3\u003c/sup\u003e/(a.km).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe North China Plain, being a vital agricultural region for staple crops, has seen anthropogenic groundwater extraction emerge as a significant discharge method for confined aquifers in recent decades. Extensive exploitation of confined aquifers has not only led to the formation of large-scale cones of depression and other geological environmental issues within the study area but also caused significant disturbances to the hydrogeochemical evolution by altering the hydrodynamic conditions of confined aquifers. Moreover, agricultural production in many areas heavily relies on the use of fertilizers and pesticides. The toxic substances present in these products are introduced into the aquifer through irrigation water. Additionally, the high permeability of foothill areas enhances the infiltration of pollutants into the aquifer from both point and non-point sources, driving the movement of pollutants along groundwater flow paths. The dense human activities in the plain areas also contribute to the burden of surface pollutants. These elements together pose significant challenges to the hydrochemical quality of the confined groundwater in the study region.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample collection and testing\u003c/h2\u003e \u003cp\u003eIn June 2020, a total of 64 water samples were collected, in which 34 samples collected from phreatic aquifers and 30 samples collected from confined aquifers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Details regarding groundwater and sampling depths are listed in Table S2. Sampling took place during the dry season, spanning from May to June 2020. To ensure that the samples reflected the true groundwater conditions, each well was pumped for 15 minutes before sampling to remove any stagnant water. In-situ measurements of groundwater temperature, pH, and electrical conductivity (EC) were conducted. Once these physicochemical parameters stabilized, water samples were collected in polyethylene bottles. Each target groundwater sample was pre-rinsed by flushing 3 to 5 times before collection. Samples were then collected according to specific detection requirements, carefully sealed, and individually labeled. All samples were stored in portable coolers and transported to the laboratory at 4℃. They were delivered to the laboratory for analysis within 48 hours of collection.\u003c/p\u003e \u003cp\u003eField measurements of pH and electrical conductivity (EC), along with other physical parameters, were conducted using a portable multi-parameter instrument (Multi 350i/SET, Munich, Germany). Total dissolved solids (TDS) were determined through gravimetric analysis. Bicarbonate ions (HCO- 3) were quantified via acid-base titration. Major cations, including calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e), sodium (Na\u003csup\u003e+\u003c/sup\u003e), and potassium (K\u003csup\u003e+\u003c/sup\u003e), were analyzed using inductively coupled plasma mass spectrometry (ICAP6300, Thermo Fisher, Los Angeles, CA, USA). Ammonium (NH\u0026thinsp;+\u0026thinsp;4) and anions, such as chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e), sulfate (SO2- 4\u003csup\u003e)\u003c/sup\u003e, nitrate (NO- 3), and nitrite (NO- 2), were measured using gas chromatography-mass spectrometry (GC-MSQP2010plus, Yi Dian, Shanghai, China). Isotopic analyses of δ\u003csup\u003e2\u003c/sup\u003eH and δ\u003csup\u003e18\u003c/sup\u003eO were performed with an isotope ratio mass spectrometer (DLT-100, LGR Company, San Jose, CA, USA). The ion analyses were carried out at the Water Environment Detection Center of the Hebei Institute of Geological Environmental Monitoring, while the isotope analyses were conducted at the Laboratory of Groundwater Science and Engineering, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences. The stable isotopes of hydrogen and oxygen (\u003csup\u003e2\u003c/sup\u003eH and \u003csup\u003e18\u003c/sup\u003eO) were determined using a liquid water isotope analyzer (Finnigan TMMAT253, Germany), with δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH values reported relative to the Vienna Standard Mean Ocean Water (VSMOW) standard. The analytical uncertainties were 0.2\u0026permil; for δ\u003csup\u003e18\u003c/sup\u003eO and 1.0\u0026permil; for δ\u003csup\u003e2\u003c/sup\u003eH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Entropy-weighted water quality index\u003c/h2\u003e \u003cp\u003eCompared to other water quality assessment methods, the advantage of the entropy weight index method lies in its incorporation of the entropy concept, which minimizes the subjective influence on the weights of the evaluation factors. The EWQI evaluation entails four primary stages: identification of evaluation indicators, normalization, weight assignment to indicators, and calculation of entropy-weighted water quality indices. (Gorgij and Moayeri, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirstly, select evaluation indicators and establish an initial eigenvalue matrix as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\left. {X=\\left[ {\\begin{array}{*{20}{c}} {{x_{11}}}\u0026amp;{{x_{12}}}\u0026amp;{}\u0026amp;{{x_{1n}}} \\\\ {{x_{21}}}\u0026amp;{{x_{22}}}\u0026amp;{}\u0026amp;{{x_{2n}}} \\\\ \\vdots \u0026amp;{}\u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{x_{m1}}}\u0026amp;{{x_{m2}}}\u0026amp; \\cdots \u0026amp;{{x_{mn}}} \\end{array}} \\right.} \\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this context, X represents the initial feature value matrix, where X\u003csub\u003eij\u003c/sub\u003e denotes the value of the j\u003csup\u003eth\u003c/sup\u003e evaluation factor for the i\u003csup\u003eth\u003c/sup\u003e water quality sample.\u003c/p\u003e \u003cp\u003eNext, the eigenvalue matrix should be normalized. Given the diverse units and substantial dimensional variations across the indicators in matrix X, normalizing the initial matrix becomes imperative to yield a standardized matrix Y. This normalization process involves deducting the ratio of each indicator's actual value to its range from the maximum value of each indicator, thereby constituting the elements of the new matrix Y. The computation of matrix Y adheres to the equations specified:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\left. {Y=\\left[ {\\begin{array}{*{20}{c}} {{y_{11}}}\u0026amp;{{y_{12}}}\u0026amp;{}\u0026amp;{{y_{1n}}} \\\\ {{y_{21}}}\u0026amp;{{y_{22}}}\u0026amp;{}\u0026amp;{{y_{2n}}} \\\\ \\vdots \u0026amp;{}\u0026amp; \\ddots \u0026amp; \\vdots \\\\ {{y_{m1}}}\u0026amp;{{y_{m2}}}\u0026amp; \\cdots \u0026amp;{{y_{mn}}} \\end{array}} \\right.} \\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${y_{ij}}=\\left\\{ {\\begin{array}{*{20}{c}} {\\frac{{{x_{ij}} - min\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} }}{{max\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} - min\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} }}} \\\\ {\\frac{{max\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} - {x_{ij}}}}{{max\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} - min\\{ {x_{1j}},{x_{2j}}, \\cdots ,x\\} }}} \\end{array}} \\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2.3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWithin the context of the standardized matrix Y, the elements are denoted by y\u003csub\u003eij\u003c/sub\u003e. The term max{x\u003csub\u003e1j\u003c/sub\u003e, x\u003csub\u003e2j\u003c/sub\u003e, \u0026hellip;, x\u003csub\u003enj\u003c/sub\u003e} signifies the maximum value within a specific column of matrix Y, while min{x\u003csub\u003e1j\u003c/sub\u003e, x\u003csub\u003e2j\u003c/sub\u003e, \u0026hellip;, x\u003csub\u003enj\u003c/sub\u003e} indicates the minimum value within the same column of the matrix Y.\u003c/p\u003e \u003cp\u003eNext, the weights for each hydrochemical indicator must be established. Due to the varying importance of different indicators in assessing groundwater quality, it is essential to assign specific weights to each indicator during the evaluation process. To enhance the objectivity and rationality of the evaluation, we utilize the entropy weight method to allocate weights to each indicator. This allocation can be performed in accordance with equations (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e, \u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e, \u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e2.6\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"412\" height=\"170\"\u003e\u003c/p\u003e\u003cp\u003eHere, p\u003csub\u003eij\u003c/sub\u003e means the proportion of the hydrochemical parameter for each sample, whereas ei represents the entropy information associated with each index, ω\u003csub\u003ej\u003c/sub\u003e means the weight assigned to a particular hydrochemical index.\u003c/p\u003e \u003cp\u003eNext, compute the EWQI value. Prior to determining the EWQI value, it is necessary to quantitatively grade each hydrochemical index (q\u003csub\u003ej\u003c/sub\u003e), as shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e2.7\u003c/span\u003e). Calculate EWQI by combining the values of ω\u003csub\u003ej\u003c/sub\u003e and q\u003csub\u003ej\u003c/sub\u003e as outlined below:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXwAAAByCAIAAADf+P1ZAAAOlklEQVR4Ae2dPW7jPhPGdQfdwAH2BjqBAjh1CqdjlwXcen0AI50BIdg+6QXXQg6QuNcBBLiVD8AD8H2T2f+E4ZdlS1Fk63GzFDUaDn+0nvDTGyl8QAAEQKBHAlGPZaEoEAABEFAQHXwJQAAEeiUA0ekVNwoDARCA6OA7AAIg0CsBiE6vuFEYCIAARAffARAAgV4JQHR6xY3CQAAEIDr4DoAACPRKAKLTK24UBgIgANHBdwAEQKBXAhCdXnGjMBAAAYgOvgMgAAK9EoDo9IobhYEACEB08B0AARDolQBEp1fcKAwEQACig+8ACIBArwQgOv9wZ1kW/fcpikIpxTlxHJdl2WuzoDAQuFwCEJ3Pts3zXNeXuq5vbm4eHx+llJ9GSIEACLQjANH55CelTNM0yzKllJRyuVxWVfV5GykQAIEuCEB0vlDMsixN0/1+D8X5wgUXINAdAYjOF5ZFUcRxfHNzgz7OFy64AIHuCEB0vrAsyzKOY5pI/nIDFyAAAh0RgOh8gqzr+u7uLkkSIcRnLlIgAAKdEoDo/MNJilNVVZZlk8mkrutOOcMZCIDAPwIQnXcQVVXd3d2R0JRl+evXL9/GnNVqBT3C2wMCbQhAdFSe57PZjDfj1HWdJMliseCcNnzxLAiAgEFg1KJT1/VkMqF9yLQ9R8/RNwrSzp00TTHHbHyBcAkCxxIYtegcC+v19dU37DrWFexBYLQEIDpHNP3z8/OxYy7a5ZymqfEgH+wyuk6+/COihCkIDJsARKdp+5RluVqtmlp/2PFgzRCdoihogYwMWHd8+UcVCmMQsAlEUZdvektvXYZiV/WScrIsY3U4ql50tIJ7OtT3YVdFUZAk+fKPKgvGIGATaKkRtkOlVBufEB0n0s9MKeXDw8N+v7+/v7cXy6WURn5ZlovF4vP5j5/I0Hs6xpI8X3KCnjUudYdIg0BzAj51mM/ntIQynU6d3rbb7X+/9fLv3/V6rVv6POs2zvTZiE5VVbPZLIqiLMuEENxTcNaq20whROBsBO0q5D0++uo7hWH0dHgMRXd5hOXL77Yu8DY2Ak5pWK/XeZ4rpXa7XRRFTt0xJGY6nW63W52e07Nu4Eufh+jQkajVaiWlLMsySRK702HUUAhh6DRdfscRB9pbuNlslsslD6M4HogOo0CiZwI+XSDFoWDW67XTzJCYq6srO3jng7aZkXMGokOTHSwWRVFw2qjMD14GRkNh0eEjpkZPh/N/sFIo+twJNBGFPM8PmuV5Pp/PbRoHH7QfeZ8PcuYOKlN/G43Z1pZxOrtCDTP1oo/q6RjyxJecIM/GpV4c0iDQkEATUcjz3Dm80ouwx1Z0t4l/3c+/p+ysoeWIjw9FlWWZsVGY8u0jUb0Nr46d0zF0k/tBvvyhNQfiOSMCTURhOp3udrtwpZxjq5PXsM6gpyOEuL29lVLmeb5cLu2Z2jCvb73bZPVKCKGvXimleJmcZ5EpSF/+t1YBzi+YwEHRyfPcmDC2afjGVpcsOjS7EcdxnufZx0fnYnQQ9Fs/nubNgVEUGR00385jX/6P1wUBnCOBsOjsdjvnTI1RU9/Y6pJFhxFIKWezmX36CUeiGBESIGAQCOiOb9DU0EPAs+HBuDyD4RVH7FssP+FIFPvUEzRWol5Jnud1XW82G90AaRA4OwI+adDzA12ezsdW57F6pZTSxynGevkJR6J83xuasZYfn9VqFUVRn1sQfVEhHwRaEtD1hTcEGqu0NJdMe3b0HTq+sZXh86gIz6mn46zYyUeiDG/02108drNniA17XILAGRFooxF2NVt6O1fRCR+JsjEdzKEJab0b1dWo7WDRMACBURE4V9FRSoWPRJ3QivSfXgkh7KMMJ3jDIyAAAk4CZyw6zvq0zKTleWNbTUufeBwEQEAnANHRabynoTsmEVyDQKcEIDrvOIuPD4P1HbZgAyRAAAROJgDReUdnLIHpR0yZrH28i28hAQIg0JwAROedlRBiMplUVUX/8V6apvoyVnOasAQBEDhIAKLzvvPw+fm5qqr7+3vaMWX8T3tDPt51sIFhAAJDIwDRadQiON7VCBOMQKABAYhOA0hKYaNgI0wwAoEGBCA6hyF1eLzrcGGwAIFLJwDROdzCxtrW4QdgAQIg4CcA0fGy6fx4l7ck3ACBMRGA6IRau/PjXaHCcA8ExkEAojOOdkYtQWAwBCA6g2kKBAIC4yAA0RlHO6OWIDAYAhCdwTQFAgGBcRCA6IyjnVFLEBgMAYjOYJoCgYDAOAhAdMbRzqglCAyGAERnME2BQEBgHAQgOuNoZ9QSBAZDAKIzmKZAICAwDgIQnXG0M2oJAoMhANEZTFMgEBAYBwGIzjjaGbUEgcEQgOgMpikQCAiMgwBEZxztjFqCwGAIQHQG0xQIBATGQQCiM452Ri1BYDAEIDqDaQoEAgLjIADRCbVzWZZJkry9vSVJkmVZyBT3QAAEmhGA6Bzg9Pz8/PfvX/wvNAcw4TYINCYA0Qmhquv6/v6+ruuiKNDTCZHCPRBoTACiE0JVfHyUUqvVqq7rkCnugQAINCMA0QlxIq2p6/ru7u7x8dFpKqXcbDZSSufd/jOHFk//BFDiwAlAdNo20AD/m/O6rn0S2ba2eB4EWhOA6LRCyOOvVl6+4eHBBvYNdYXLMyMA0Tm9waSUf/78Gc7ASq/JkGPT40R6hAT6EJ0syyLXpyiKsizjOKabaZre3t6yYZZlRVHwLX639/v9YrGgp+I4zvOcm00IoT/O+UclpJRpmrIfIxHHcVmW5LAoisVicZTzPo0Xi0VRFH2WiLIGSyCKunzTW3rrMpQA8aIo9Ne1qqrZbEZvb1mWk8nk6emJHs+ybDKZ8FLRYrHQ16rJz2q1Ig3K8zyKIv3VyrJMLygQUuBWXdeTjw+HQcZ62EopIYRedMDhj9wqikII8SNFo9BBEWipEc66tPHZk+hkWZamKfdWlFKbzYZe6bIsf/36RQJU13WSJGz5/w3BLy8vXGfq+OhdG3rz2Z4uO3nTqAume6Ywnp6eKFQpJesmRzioRFmWs9lMZz6o8BBMPwR86jCfz6kXP51OfZHQH3Uy2+12hpnPs2FmX/YkOkII6rBIKZ+fn/U4dNHZbDbX19f0qksp9SUY6n3YgiKE4J4R2XTV+yDdsUuk4Ekfeail10gpVVVVkiQUGGnlYrHwvf9SStqCyE7Ksmw/cNPBsmckxkbAKQ3r9Zr+eO92uyiKnLqz3W6jKNput0opShvonJ4NG+dlH6Kja0H28dFDKYqCXs6yLF9fX8XHRynFfQoy9o2bdNHp/DUjvdDHdxw5Hcsyxl90N89zY7LJHgayH0rQViDu+vl6KDS+i6Ioy7KDg7uwLBoB4PIiCfh0QR8urNdrp9l6vb66umIsURR11dnpQ3R4Ppj6aUZPhCZxqqp6eHjY7/dpmmZZVpYlz/IopWhy1x7sGN0fexDHyGjkZcwK06WvL0PP0iy4EbNSirVSL0IppQsfzzRT/E7x4serqrq7u9tsNsvl0tknop4XzWcFJI8d6lrPmUiMioBTTQwC9BfRyFRKUT4LjdOVM9N2ZeT0ITrcebHHEUopUoqXl5eyLPnlfHx81F88en/sl5bkjBSBnzVq2PKyruubmxt7GOUUHT0GPd3w/dcFywibvLE+NpkkblioURAuL4lAE1HI89w5vFJKXV1dkYfpdMrqo/Np4l+3p/S3i47x1bf37wohrq+vafqGjH///v36+qrHSn/kDdGh95C7P4E3Vnd1bPrp6cnu5vh6OnoMdV1fX1/ziClJEudYjOMJ93R0jaOKO6Nib0opg7x+C+mREGgiCj5BIUQ0GqCZHRtaE/+Op+ysbnOMxXJyrs/XGJMycRzz33OOhN4fI9+Y5QmPrU4bXvH4iCPhhK4CeqYugqwL3NdjS+ME6cE5Hd2DUfGAz8lkwjGwGRLjIXBQFPI8X6/XPiB0l9a59Gkgtj/ony31xPf2dKSUQgh+D5VStLWPc4w/2oGpCnrTqOZSStofyG9UVVWTycToCun1PCEdXnJ2hspKRFNUq9WKxsa+iWGKyh512qtXQojb21spZZ7ny+Uy7JDcYiL5hEa/sEfCorDb7ebzua/K2+2WJ5Jpfsfu74T9+zx/o+jQmMg5d8vqQD9Yw9M3gZ+tIaEhb3EcLxaL/X5PtdJ3PBu9IV+1D+brXQ/duK7rzWZDg5ckSYy5HtLQOI6fnp4oHUWRsVhu6KzuPJAmmLQoZq8AOn3qY72AZ9y6bAIBXWBNcRKYz+f6XM90OjX6RAHPToec+Y2iw2V0lZBSvr29sUJ15db249v4J6VcrVakmD4b25ud8/r6aqiVbePL8ZVr+wz31Hz+kX9hBHzSoOc7uzxG74b37DAf3QNnNkmcjejQdrsoivT+xcvLC/U7mlS1uY1+hsvuqfGY7uBOGV+J9my6z9LOdw7rlFK2zyYrXLZ/5FweAUMdaEOg8cWmxSnas8PDKLokS84kPobPo6Cdh+js9/uqqqSULy8vNzc3RCFJEv2QxFHV7sQ4MM0c8H/yzy3TbDrV3RhFOn3SidlAJLg1HgJtNMKm1NLbeYiOXe0h5Jz28xHfoQW2z9NiGwJVxHDxBCA63iY2VrXJjqZs6dwG7dbh0ZbX0ccNKSVtuTaOWYWfCt8N+MSPeIXR4e4PEoDoHA3fWOF+fHwM7/rjAoQQcRw3FCl+Kpxw+iQx6mHGPRwb7oKAkwBEx4HFuQLNdoboDO2H0OkcPxSH2wuJoRGA6LhbxF6BZjtDdDgfCRAAgSYEIDpuSrQCTV0eXlykjdQQHTcy5IJAMwIQHQcnXoGG6DjoIAsE2hGA6Dj42SvQuhFtHeTjY/otpEEABA4SgOh8IgqsQH8aIQUCINCOAETnCz/nCvQXC1yAAAi0IwDRaccPT4MACBxJAKJzJDCYgwAItCMA0WnHD0+DAAgcSQCicyQwmIMACLQjANFpxw9PgwAIHEkAonMkMJiDAAi0I/A/p1BL/qaNNeUAAAAASUVORK5CYII=\" width=\"380\" height=\"114\"\u003e\u003c/p\u003e \u003cp\u003eIn the equation, x\u003csub\u003ej\u003c/sub\u003e represents the measured values of various parameters in the samples, meanwhile, s\u003csub\u003ej\u003c/sub\u003e means the maximum permissible limit set forth by either the World Health Organization or Chinese regulatory guidelines for the chemical parameter j. Based on the EWQI value, groundwater quality can be categorized into five distinct classifications, namely extremely excellent, excellent, good, poor, and extremely poor. The corresponding numerical ranges are 0\u0026ndash;50, 50\u0026ndash;100, 100\u0026ndash;150, 150\u0026ndash;200, and \u0026gt;\u0026thinsp;200.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Human Health Risk Assessment\u003c/h2\u003e \u003cp\u003eWith the progress of industrialization, the influence of groundwater contaminants on human health has become increasingly significant. Therefore, it is crucial to accurately and reasonably assess the health risks posed by groundwater contaminants. This study employs the human health risk assessment model recommended by the United States Environmental Protection Agency (USEPA, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), with a focus on two primary exposure pathways: dermal contact and direct ingestion. The aim is to quantify the non-carcinogenic risks associated with different exposure routes and conduct a comprehensive evaluation of groundwater quality in the study area. Considering that fluoride ions (F\u003csup\u003e\u0026minus;\u003c/sup\u003e) exceed allowable limits in multiple locations and are categorized as non-carcinogenic, this research specifically emphasizes the potential non-carcinogenic risks associated with fluoride exposure.\u003c/p\u003e \u003cp\u003eThe formula for evaluating human health risks through direct ingestion is as follows: (Adimalla et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yousefi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e):\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$CD{I_{oral}}=({C_i} \\times IR \\times EF \\times ED) \\div (BW \\times AT)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$H{Q_{oral}}=CD{I_{oral}} \\div Rf{D_{oral}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$$H{I_{oral}}=H{Q_{oral,1}}+H{Q_{oral,2}}+ \\cdots +H{Q_{oral,i}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere CDI\u003csub\u003eoral\u003c/sub\u003e is the chronic daily intake dose via the drinking water intake pathway [mg/(kg*day)]. C\u003csub\u003ei\u003c/sub\u003e represents the concentration of the target contaminants in groundwater (mg/L). IR refers to the ingestion rate of drinking water (L/day). EF denotes exposure frequency (days/year). ED indicates exposure duration (years). BW is the average body weight (kg). AT expresses the average time(days). HQ\u003csub\u003eoral\u003c/sub\u003e is the hazard quotient of non-carcinogenic risk due to the drinking water intake pathway. RfD\u003csub\u003eoral\u003c/sub\u003e denotes the reference dose of a specific pollutant [mg/(kg*day)] through the drinking water intake pathway. HI\u003csub\u003eoral\u003c/sub\u003e represents the overall non-carcinogenic health risks of multiple contaminants (1, 2, \u0026hellip;, i) by drinking water intake pathway.\u003c/p\u003e \u003cp\u003eThe formula for evaluating human health through dermal intake is as follows:\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$CD{I_{dermal}}=({C_i} \\times K \\times {S_a} \\times T \\times EF \\times ED \\times EV \\times CF) \\div (BW \\times AT)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$${S_a}=239 \\times {H^{0.417}} \\times B{W^{0.517}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$H{Q_{dermal}}=CD{I_{dermal}} \\div Rf{D_{dermal}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ15\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ15\" name=\"EquationSource\"\u003e\n$${\\text{Rf}}{{\\text{D}}_{dermal}}={\\text{Rf}}{{\\text{D}}_{{\\text{oral}}}} \\div AB{{\\text{S}}_{{\\text{gi}}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere CDI\u003csub\u003edermal\u003c/sub\u003e represents the daily exposure dose via dermal contact [mg/(kg*day)]. K is the skin permeability coefficient (cm/h). S\u003csub\u003ea\u003c/sub\u003e, T, EV, CF, and H correspond to the skin surface area, contact duration (h/d), the exposure frequency of daily dermal contact (time/day, EV\u0026thinsp;=\u0026thinsp;1 day), and the unit conversion factor (L/cm3), the average body height (cm). RfD\u003csub\u003edermal\u003c/sub\u003e denotes the reference dose for a specific contaminant [mg/(kg*day)] through the dermal contact. ABS\u003csub\u003egi\u003c/sub\u003e indicates the gastrointestinal absorption factor. All parameters utilized in this study are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e(HI\u003csub\u003etotal\u003c/sub\u003e) The overall risk of pollutants ingested through drinking water and dermal to human health is as follows:\u003cdiv id=\"Equ16\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ16\" name=\"EquationSource\"\u003e\n$$H{I_{total}}=H{I_{oral}}+H{I_{dermal}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3.8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis of hydrochemical characteristics of groundwater\u003c/h2\u003e \u003cp\u003eHydrochemical parameters for groundwater samples in the study area were listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. pH values range from 7.11 to 9.70, averaging 7.93, indicating predominantly neutral to alkaline groundwater, consistent with Chinese drinking water guidelines (6.5\u0026ndash;8.5) (GAQS, 2017). EC values vary from 403 to 873 \u0026micro; S/cm, with an average of 566.10 \u0026micro; S/cm. TDS concentrations range between 154 and 1766 mg/L, with an average of 676.91 mg/L. According to the TDS classification by the United States Geological Survey (USGS, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the majority (85.93%) with salinity levels below 1000 milligrams per liter fall into the freshwater category, while 14.06% fall into the brackish water category.\u003c/p\u003e \u003cp\u003eThere are three main ionic forms for nitrogen: NO- 3, NO- 2, and NH\u0026thinsp;+\u0026thinsp;4. The NO- 3concentration ranges from 0.108 mg/L and 24.20 mg/L, with an average of 3.33 mg/L.NO- 2 concentrations vary between 0 and 0.08 mg/L, averaging 0.01 mg/L. NH\u0026thinsp;+\u0026thinsp;4 concentrations lie between 0.02 mg/L and 0.06 mg/L, with an average of 0.02 mg/L. Analysis indicates that approximately 4.68% of samples surpass the Chinese drinking water standard for NO- 3 (20 mg/L). However, NO- 2 and NH\u0026thinsp;+\u0026thinsp;4 concentrations remain within acceptable limits per Chinese drinking water standards (GAQS, 2017). Thus, certain areas in the study region are contaminated with nitrate, while the overall environmental pollution situation regarding nitrite and ammonia nitrogen is satisfactory.\u003c/p\u003e \u003cp\u003eThe order of main cation concentrations in groundwater samples: Ca\u003csup\u003e2+\u003c/sup\u003e \u0026gt; Na\u003csup\u003e+\u003c/sup\u003e \u0026gt; Mg\u003csup\u003e2+\u003c/sup\u003e \u0026gt; K\u003csup\u003e+\u003c/sup\u003e. Ca\u003csup\u003e2+\u003c/sup\u003e is the predominant cation, with concentrations ranging from 1.92 mg/L and 182 mg/L, an average of 62.01 mg/L. Na\u003csup\u003e+\u003c/sup\u003e ranks second, with concentrations from 6.82 mg/L and 509 mg/L, an average of 149.79 mg/L. Approximately 23.44% of groundwater samples exceed the World Health Organization water quality standard for Na\u003csup\u003e+\u003c/sup\u003e (WHO, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Mg\u003csup\u003e2+\u003c/sup\u003e concentrations range from 5.01 mg/L and 189 mg/L, an average of 32.42 mg/L. The high sodium ion content in the area, particularly in samples with high concentrations, mainly originates from the confined aquifers. This phenomenon is primarily attributed to the leaching action of shallow groundwater. (Hao et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) The order of major anion concentrations is as follows: HCO- 3\u0026thinsp;\u0026gt;\u0026thinsp;SO2- 4\u0026thinsp;\u0026gt;\u0026thinsp;Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e \u0026gt; NO- 3. The highest concentration of anions is found in HCO- 3, with an average of 312.92 mg/L and spanning from 113 mg/L to 946 mg/L. SO2- 4 concentrations vary between 24.8 mg/L and 443 mg/L, averaging 133.59 mg/L. Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations range from 8.51 mg/L to 539 mg/L, with an average of 127.98 mg/L. Additionally, analysis of F\u003csup\u003e\u0026minus;\u003c/sup\u003e and Cr\u003csup\u003e6+\u003c/sup\u003e revealed that 17.19% of samples exceeded the standard for fluoride ions, hexavalent chromium ions are of particular concern, with an average concentration of 0.68 mg/L. The maximum concentration detected is 0.037 mg/L, which is 3.7 times the permissible limit, and 14.06% of the samples exceed this standard.\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\u003eStatistical overview of various groundwater parameters in the study region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGuideline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% of the sample exceeding the Guideline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u0026ndash;8.5**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003euS/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e566.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e288.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e450**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e676.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e326.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1000**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e149.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.44%/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC1\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO2- 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e133.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCO- 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO- 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO- 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\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\u003e4.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH\u0026thinsp;+\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003csup\u003e6+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Standard Deviation.\u003c/p\u003e \u003cp\u003e** WHO Drinking Water Standards (WHO, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e*** China Groundwater Quality Standards (GAQS, 2017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Hydrogeochemical type\u003c/h2\u003e \u003cp\u003eThe Piper diagram summarizes the chemical composition of groundwater (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It can be observed that all samples are dominated by Na\u003csup\u003e+\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e as the main cations, with approximately 83.33% of the water samples classified as sodium-potassium-type groundwater, and 20.31% as calcium-type water. Most samples show HCO- 3 as the dominant anion, though some samples exhibit Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e predominance. Specifically, HCO- 3 and Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e account for 89.06% and 9.37% of the overall samples, respectively. Additionally, the hydrochemical facies of groundwater were assessed utilizing the Piper diagram (Piper, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1944\u003c/span\u003e). Within the diamond-shaped field, hydrochemical types include HCO\u003csub\u003e3\u003c/sub\u003e-Ca, mixed HCO\u003csub\u003e3\u003c/sub\u003e-Na\u0026middot;Ca, and Cl-Na waters. The majority of water samples (39.06%) belong to the HCO\u003csub\u003e3\u003c/sub\u003e-Ca type, indicating good water quality. 31.25% of water samples are classified as mixed HCO\u003csub\u003e3\u003c/sub\u003e-Na\u0026middot;Ca type, while 21.87% are saline Cl-Na type waters. It's apparent that confined groundwater predominantly exhibits the Cl-Na type, depicting a shift from mixed Cl-Mg\u0026middot;Ca type water to Cl-Na type water, indicating progressive salinity in deeper groundwater. Conversely, the general trend for shallow groundwater is transitioning from HCO\u003csub\u003e3\u003c/sub\u003e-Ca type water to HCO\u003csub\u003e3\u003c/sub\u003e-Na type water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGroundwater chemistry is primarily influenced by three natural processes: atmospheric precipitation, evaporation, and interactions with geological formations. The Gibbs diagram is extensively used to illustrate groundwater ion characteristics, identify the principal sources of ions, and ascertain the key factors affecting groundwater chemistry (Gibbs, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). In this study, the Gibbs diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was utilized, revealing that most samples are predominantly influenced by rock weathering. This implies that interactions with geological formations are the predominant natural drivers shaping groundwater chemistry in the study area (Xiao et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Combining the local hydrological conditions, it is observed that groundwater in the aquifers exhibits low flow velocities and relatively long residence times, providing ample time for water-rock interactions. Additionally, considering prior research findings, it is clear that ion exchange and mineral dissolution processes play a significant role in shaping hydrochemistry and groundwater types (Pei et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To delve deeper into the rock control within water-rock interactions, employing end-member diagrams can elucidate the impact of various rock types on the ion composition of groundwater. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it is evident that all samples are situated in the transition zone from silicates to carbonates. This indicates a significant involvement of both silicates and carbonates in water-rock interactions, suggesting that groundwater contains a substantial amount of components from both types of rocks/minerals.\u003c/p\u003e \u003cp\u003eThe saturation indices of the selected minerals are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Typically, when the saturation index (SI) is less than zero, it signifies that the mineral is undergoing dissolution into the groundwater. An SI value equal to zero denotes a state of saturation. Conversely, an SI value exceeding zero signifies the active precipitation of a mineral. The analysis reveals that the saturation indices (SI) values for most groundwater samples indicate that the SI values for calcite, dolomite, and aragonite are above 0, implying ongoing precipitation of these minerals. This observation aligns with the conclusions drawn from the end-member analysis. This further elucidates that carbonates in the study region are not predominantly derived from the dissolution of minerals like calcite, dolomite, and aragonite. In contrast, the SI values for halite, gypsum, and anhydrite are all below zero, suggesting the ongoing dissolution of these minerals into the groundwater. Additionally, the SI values of fluorite and potassium salts are also less than 0, indicating ongoing dissolution of these minerals and their entry into groundwater, which adequately explains the elevated levels of F\u003csup\u003e\u0026minus;\u003c/sup\u003e and K\u003csup\u003e+\u003c/sup\u003e in groundwater samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Isotopic characteristics\u003c/h2\u003e \u003cp\u003eEvaporation significantly influences the groundwater chemistry in the study area. Deuterium and oxygen-18 isotopes are crucial for understanding the impact of evaporation on groundwater's chemical composition (Carucci et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The relationship between δ\u003csup\u003e2\u003c/sup\u003eH and δ\u003csup\u003e18\u003c/sup\u003eO in groundwater and surface water is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. All groundwater samples are positioned below the Global Meteoric Water Line (GMWL), highlighting a strong evaporative effect on shallow groundwater in the region. By analyzing shallow groundwater sample data, a linear regression established the Local Meteoric Water Line (LMWL) for the area, resulting in the equation: Y\u0026thinsp;=\u0026thinsp;5.88X-14.04.\u003c/p\u003e \u003cp\u003eTo investigate the recharge sources of shallow groundwater in the study area, we systematically analyzed the data using the Global Meteoric Water Line (GMWL) equation (δ\u003csup\u003e2\u003c/sup\u003eH =\u0026thinsp;8.00*δ\u003csup\u003e18\u003c/sup\u003eO + 10.00) as outlined by the International Atomic Energy Agency. The δ18O values for groundwater in the area ranged from \u0026minus;\u0026thinsp;11.53\u0026permil; to -3.98\u0026permil;, averaging \u0026minus;\u0026thinsp;8.18\u0026permil;, while the δ\u003csup\u003e2\u003c/sup\u003eH values varied from \u0026minus;\u0026thinsp;74.82\u0026permil; to -40.93\u0026permil;, with a mean of -60.31\u0026permil;. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, all groundwater samples are positioned below the global meteoric water line. Through linear regression analysis of the δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH values of groundwater, we derived the equation for the evaporation line as δ\u003csup\u003e2\u003c/sup\u003eH = 5.88*δ\u003csup\u003e18\u003c/sup\u003eO \u0026minus;\u0026thinsp;14.04. In comparison to the regional precipitation line, the slope and intercept are notably smaller, indicating a pronounced influence of non-equilibrium evaporation on the groundwater replenishment process from atmospheric precipitation. This suggests that intense evaporation significantly affects the enrichment and transport of δ\u003csup\u003e18\u003c/sup\u003eO and δ\u003csup\u003e2\u003c/sup\u003eH in groundwater, in alignment with the findings derived from the analysis of groundwater hydrochemistry within the study area.\u003c/p\u003e \u003cp\u003eThe hydrogen and oxygen isotope values of most surface water samples exceed the regional precipitation line, but the same values in the Shahe segment falls below the Global Meteoric Water Line (GMWL). This phenomenon emanates from the fact that the sampling site is situated within the western Piedmont alluvial plain of Zhangwei, leading to the supply of mountain springs or fissure water to the river surface water. In comparison to other surface water samples, this supply results in a more pronounced enrichment of heavy isotopes, consequently, the hydrogen and oxygen isotope values are observed to be lower than the Global Meteoric Water Line (GMWL).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Water quality evaluation\u003c/h2\u003e \u003cp\u003eThe entropy-weighted water quality index (EWQI) is widely employed for an in-depth evaluation of groundwater quality. In this investigation, cations (Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e), anions (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO2\u0026thinsp;\u0026minus;\u0026thinsp;4, HCO\u0026thinsp;\u0026minus;\u0026thinsp;3, F\u003csup\u003e\u0026minus;\u003c/sup\u003e, and NO\u0026thinsp;\u0026minus;\u0026thinsp;3), in addition to pH, EC, TH, and TDS, were chosen as assessment parameters. The outcomes of water quality evaluation yielded EWQI values ranging from 4 to 284.\u003c/p\u003e \u003cp\u003eThe assessment findings of EWQI depict a spectrum of groundwater quality from excellent (Grade 1) to very poor (Grade 5). There are 57 groundwater samples classified as Grade 1 and Grade 2 (EWQI values\u0026thinsp;\u0026lt;\u0026thinsp;100), accounting for 89.06% of the total samples. Additionally, 9.38% of groundwater samples (6 samples) have EWQI values ranging from 50 to 100, while only one sample has an EWQI value exceeding 150. In this study, over 89% of the groundwater samples meet the drinking water standards. Among them, groundwater samples with poorer quality (S28 and S29) exhibit higher TDS levels (S28: 1446 mg/L; S29: 1489 mg/L) and are enriched with F\u003csup\u003e\u0026minus;\u003c/sup\u003e (S28: 1.73 mg/L; S29: 1.1 mg/L). Combining data analysis reveals that detection points with high TDS and elevated F\u003csup\u003e\u0026minus;\u003c/sup\u003e ions are predominantly distributed in the deep groundwater zones. The exceedance of these two parameters is the primary driving factor behind the deterioration of water quality in the region. This discovery directly affirms the preceding analysis and further substantiates that the interaction between deep-seated groundwater and geological formations is the primary factor contributing to the aberrant groundwater quality observed in the study locale.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Risks assessment for human health\u003c/h2\u003e \u003cp\u003eHealth risk assessment necessitates the application of a quantitative model to systematically evaluate the potential health hazards stemming from exposure to contaminated groundwater. In this research, fluoride ions were selected for non-carcinogenic health risk assessment due to the potential health issues, such as dental and skeletal fluorosis, that can arise from prolonged ingestion of groundwater with fluoride levels exceeding acceptable limits. Therefore, we employed a methodology involving the computation of the Hazard Quotient (HQ) values through skin contact and oral ingestion pathways across different demographic groups, including adults, children, and infants, to evaluate non-carcinogenic health risks. The cumulative noncarcinogenic risk values for adult females, adult males, children, and infants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eStatistical analysis of health risk assessment regarding fluoride ions, as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reveals significant differences in non-carcinogenic effects between the two groups of groundwater. In terms of non-carcinogenic risks from consuming shallow groundwater, infants exhibit an average risk value of 1.21 and a peak value of 4.04. For deep groundwater, these values rise to an average of 2.05 and a maximum of 8.06. A comparative analysis of the two datasets indicates that pollutants in deep groundwater exhibit higher extreme values than those in shallow groundwater, suggesting a more prevalent contamination scenario in deep groundwater, hence the higher average values. Furthermore, a detailed examination reveals that the hazard indices for confined water are significantly larger compared to shallow water, with the hazard index for infants approximately twice that of adults, indicating a gradual reduction in health risks with increasing age. In summary, fluoride ions in the study area pose certain health risks, with children consuming highly contaminated groundwater more susceptible to health complications.\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\u003eStatistics of Human Health Risk Assessment Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003ePhreatic groundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVERAGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eConfined groundwater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn the North China Plain region, long-standing issues such as groundwater overexploitation, random discharge of wastewater, and extensive use of fertilizers and pesticides have posed significant threats to groundwater quality. This study offers a comprehensive examination of the factors affecting groundwater quality in the southern region of the North China Plain. Through the introduction of environmental isotope techniques, an analysis of groundwater recharge conditions is conducted, and efforts have been made to adopt water quality assessment methods to evaluate the suitability of groundwater and its potential health risks to humans. The study concludes the following:\u003c/p\u003e \u003cp\u003eAccording to the evaluation results of the Environmental Water Quality Index (EWQI), there are a total of 57 groundwater samples with water quality grades of 1 and 2 (EWQI value\u0026thinsp;\u0026lt;\u0026thinsp;100), accounting for 89.06% of the total number of samples. Overall, the groundwater quality tends toward favorable conditions, predominantly comprising fresh water (85.94%) or moderately hard fresh water (10.93%). Analysis of the human health risk assessment results indicates that groundwater poses relatively minor potential human health risks compared to pressurized water sources. Under the influence of various pollutants, the health risks to infants are twice that of adults, with fluoride ions (F\u003csup\u003e\u0026minus;\u003c/sup\u003e) and iodine ions (I\u003csup\u003e\u0026minus;\u003c/sup\u003e) primarily constituting the threats to human health.\u003c/p\u003e \u003cp\u003eThe hydrochemical makeup of groundwater is predominantly shaped by natural hydrogeological processes, prominently featuring the dissolution of rock salt, gypsum, anhydrite, and silicates, alongside ion exchange reactions, which constitute the principal formative reactions for both phreatic and confined water chemical components. Additionally, the chemical attributes of phreatic water are subject to influence from both evaporation and anthropogenic inputs, albeit within a constrained range and magnitude. In the exploitation of confined water for drinking purposes, special consideration should be given to hazardous compounds, especially concerns revolving around elevated fluoride levels. Given the intensive exploitation of confined aquifers within the study area driven by socio-economic advancements, there is a pressing need for vigilance concerning human-induced alterations to pressurized water systems in the locality.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYang Meng: Investigation, Data analysis, Writing \u0026ndash; original draft, preparation. Zhaoji Zhang: Conceptualization, Validation, Methodology, Yuanjing zhang: Methodology, Writing-Reviewing. Yaci Liu: Methodology, Formal analysis. Mengqing Jiao: Data analysis, Methodology, Data curation. Yasong Li: Formal analysis, Writing - review and editing. All authors have read and agreed to the submitted version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was financially supported by the Basic research business fee project of central level public welfare research institutes (Grant No. SK202309) And the Open Fund of Key Laboratory (Grant No. WSRCR-2023-01). In particular, we are grateful to the editor and anonymous reviewers for providing numerous comments and suggestions, which helped improve this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdimalla, N., Li, P., and Qian, H., 2019. Evaluation of groundwater contamination for fluoride and nitrate in semi-arid region of Nirmal Province, South India: a special emphasis on human health risk assessment (HHRA). Hum Ecol. 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(in Chinese)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hydrochemistry, Water–rock interaction, Hydrogen and oxygen isotopes, Human health evaluation","lastPublishedDoi":"10.21203/rs.3.rs-4590270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGroundwater is the critical resource for agricultural, industrial, and urban-rural water supplies in arid and semi-arid regions, especially in North China Plain. In this study, 64 groundwater samples were collected to assess the potential for groundwater development and utilization in the upper reaches of the Zhang Wei river. The hydrochemical analysis revealed that the groundwater is primarily composed of HCO3-Ca and HCO3-Na\u0026middot;Ca. The hydrochemical type is influenced by natural water-rock interactions, including evaporite dissolution, silicate weathering, and ion exchange processes. Isotope data for hydrogen and oxygen, characterized by positive deviations from the global meteoric water line, underscore the significant impact of evaporation in the region. An entropy weight index method was employed for water quality evaluation, revealing that over 89% of the samples complied with Class II standards for household use. Furthermore, more than 85% of the area\u0026rsquo;s groundwater exhibited Total Dissolved Solids (TDS) concentrations below 1,000 mg/L, indicating a predominance of soft water. However, toxic elements such as fluorine (F), iodine (I), and chromium (Cr) were found to exceed drinking water standards, posing a health risk. Particularly, the intake level of fluorine was above the permissible value, potentially causing non-carcinogenic risks to children and infants. In conclusion, while the overall groundwater quality is favorable for the region, the presence of geochemically derived toxic substances necessitates careful consideration of its suitability for drinking purposes.\u003c/p\u003e","manuscriptTitle":"Hydrochemical characteristics analysis and human health assessment in the upper reaches of Zhang Wei River alluvial plain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 23:40:26","doi":"10.21203/rs.3.rs-4590270/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-31T15:54:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-19T05:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169698557472738932667484053472107192029","date":"2024-10-01T13:39:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-01T13:29:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-17T12:46:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-17T06:29:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2024-06-16T15:23:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b4893b42-b93f-4f18-bef1-43e1ab745e0a","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T16:00:01+00:00","versionOfRecord":{"articleIdentity":"rs-4590270","link":"https://doi.org/10.1007/s12665-025-12156-w","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2025-03-18 15:57:11","publishedOnDateReadable":"March 18th, 2025"},"versionCreatedAt":"2024-07-15 23:40:26","video":"","vorDoi":"10.1007/s12665-025-12156-w","vorDoiUrl":"https://doi.org/10.1007/s12665-025-12156-w","workflowStages":[]},"version":"v1","identity":"rs-4590270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4590270","identity":"rs-4590270","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}