Hydrochemical Characteristics and Nitrate Health Risk Assessment in a Shallow Aquifer: Insights from a Typical Low-Mountainous Region

Abstract

Wolong Town, Pingquan City, is located in a typical low-mountainous area of northern China, where groundwater is a crucial drinking water resource, thus, investigating groundwater’s hydrochemical characteristics and assessing nitrate-related health risks are vital for protecting, developing, and utilizing water resources. In this study, 66 groundwater samples were collected and analyzed for physicochemical parameters and major ion concentrations. Results showed that the groundwater in Wolong Town was weakly alkaline (average pH = 7.6), and classified as fresh water with TDS ranging from 90.0 to 900 mg/L. The dominant hydrochemical type was identified as HCO₃⁻-Ca²⁺. Hydrochemical evolution was jointly regulated by natural water-rock interaction, anthropogenic nitrogen input, and environmental redox differentiation. Among these, water-rock interaction was the primary driver, where the hydrochemical composition was mainly shaped by the dissolution of halite, calcite, dolomite, and gypsum, coupled with cation exchange. Nitrate was the primary groundwater pollutant, with concentrations varying from 0.94 to 259 mg/L; elevation, soil type, and population density were key drivers influencing nitrate distribution. Health risk assessment indicated that nitrate posed significantly higher non-carcinogenic risks to infants and children than to adults, and long-term consumption of groundwater with excessive nitrate might induce adverse health effects. This study enhances understanding of shallow groundwater’s hydrochemical evolution and nitrate contamination-related health risks, thereby providing theoretical support for the sustainable development, utilization, and quality protection of groundwater resources in semi-arid low-mountainous areas.

1. Introduction

In arid and semi-arid regions, groundwater serves as an irreplaceable water resource for human survival [1,2], with its quality and sustainability directly tied to public health and safety, as well as the long-term development of regional economies [3]. Compared with surface water, groundwater exhibits greater stability in both quantity and quality, and is inherently less susceptible to anthropogenic disturbances [4]. However, against the backdrop of intensifying human activities and the continuous expansion of industrial and agricultural production, critical issues such as groundwater overexploitation and contamination have become increasingly pronounced. These anthropogenic pressures pose a severe threat to the sustainable utilization of groundwater resources [5,6]. Research on groundwater hydrochemistry plays a pivotal role in deciphering the mechanisms governing groundwater formation and evolution, evaluating its quality status, and clarifying its interactive relationships with the surrounding environment—ultimately providing a scientific basis for ensuring the rationality, safety, and sustainability of water resource development. The formation and evolution of groundwater hydrochemical characteristics are synergistically driven by geological processes and anthropogenic activities [7,8]. Under natural conditions, water-rock interaction is a core factor shaping groundwater hydrochemical properties [9,10], chemical processes including mineral dissolution and ion exchange directly regulate the chemical composition and physical properties of groundwater. With the accelerated pace of urbanization, anthropogenic activities have further disrupted the natural ion balance of groundwater, significantly altering its hydrogeochemical processes and chemical characteristics [11]. Concurrently, this disruption has led to elevated concentrations of toxic and hazardous substances in groundwater, among which nitrate pollution has emerged as a globally widespread environmental challenge [12,13].

In China, groundwater nitrate pollution has likewise garnered widespread academic and societal attention. Empirical studies have demonstrated that since the turn of the 21st century, the national average concentration of nitrate in China’s groundwater has risen by 29% [14]—a trend that signals a gradual deterioration of groundwater quality. Furthermore, results from national-scale groundwater monitoring programs reveal that nitrate was detected in 96% of groundwater samples, with 28% of these samples exceeding the maximum contaminant level (MCL) for nitrate established by the World Health Organization (WHO) [15]. This high detection rate and exceedance ratio underscore the pervasiveness and severity of nitrate pollution in China’s groundwater systems. Groundwater nitrate contamination primarily originates from multiple anthropogenic sources, which can be categorized into point-source and non-point-source pollution. Point-source pollution includes discharges from dry latrines, inefficient wastewater treatment systems, and intensive livestock farming operations, while non-point-source pollution arises from the application of chemical fertilizers and pesticides, manure spreading in agricultural fields, and atmospheric nitrogen deposition [16,17]. Among these various pollution pathways, excessive nitrogen application in agricultural activities—such as overuse of nitrogen-based fertilizers to boost crop yields—has been identified as the dominant contributor to regional groundwater nitrate pollution [12]. This is particularly concerning in agriculturally intensive regions, where repeated and unregulated nitrogen input creates a continuous risk of nitrate leaching into shallow aquifers.

Groundwater nitrate contamination poses substantial and multifaceted risks to human health. Biologically, approximately 5% of ingested nitrate is converted by bacteria in the digestive system into nitrite, which further forms N-nitrosamines and N-nitrosamides—compounds known to induce birth defects and carcinogenesis [15,18]. Epidemiological studies have further corroborated these health hazards, reporting that direct consumption of groundwater with elevated nitrate concentrations is associated with distinct adverse outcomes: it increases the incidence of methemoglobinemia (commonly referred to as “blue baby syndrome,” which primarily affects infants) and elevates the long-term risk of developing esophageal, lymphatic, and gastric cancers in adult populations [19]. To quantitatively evaluate the magnitude of such health threats, the Human Health Risk Assessment (HHRA) model has been widely adopted as a standardized tool in groundwater pollution research [20]. For instance, Yu et al. [21] applied this model to assess nitrate-related health risks in rural groundwater systems of Yantai, China. Their findings indicated that high nitrate levels in the study area likely stemmed from the synergistic effects of agricultural fertilizer application and regional geological conditions, with calculated risk values exceeding safety thresholds for both adolescent and adult populations. Similarly, Zhai et al. [22] utilized the HHRA model to estimate potential nitrate health risks in the Songnen Plain (northeastern China), highlighting significant disparities in risk exposure across demographic groups—specifically, minors and rural residents were identified as high-vulnerability populations requiring prioritized attention from academia and policymakers. In this context, systematic characterization of groundwater nitrate concentrations and quantitative assessment of their associated health risks are not only critical for advancing scientific understanding of pollution impacts but also serve to provide evidence-based support for decision-makers in formulating targeted groundwater resource protection and risk mitigation strategies [23,24,25].

Wolong Town, Pingquan City, lies in northeastern Hebei Province. Geographically, it is part of the low mountain and hilly region in the eastern segment of the Yanshan Mountains [26,27]. Given the town’s scarce surface water resources, groundwater acts as the primary water supply for domestic use, agricultural irrigation, and industrial production. Compared with plain areas [28,29], the shallow mountainous region encompassing Wolong Town is defined by rolling terrain and extensively developed bedrock fractures. Its hydrochemical evolution is governed by “water-rock interaction”, with pollution risks primarily stemming from “point source pollution”—such as mining wastewater and sewage from small-scale livestock farms—characterized by a limited contamination range but high pollutant concentrations. A review of existing literature reveals that prior research in this region has primarily focused on discrete topics, including surface water-groundwater interaction [30], spatiotemporal variations in groundwater quality, mining-induced heavy metal pollution, and associated health risk assessments [31,32,33]. However, critical research gaps remain: few studies have systematically integrated investigations of groundwater hydrochemical characteristics, their evolutionary mechanisms, nitrate pollution patterns, and comprehensive health risk assessments. This lack of integration has impeded a holistic understanding of how groundwater systems respond to natural and anthropogenic pressures. To address these gaps, this study employs an integrated methodological framework, combining traditional hydrochemical analysis, the GeoDetector tool, and a multi-population health risk assessment model. By establishing a logical research chain—“pollution source identification → driving factor quantification → health risk evaluation → management strategy formulation”—this study aims to fill the existing knowledge gap and provide comprehensive insights into the groundwater environment of Wolong Town.

2. Materials and Methods

2.1. Study Area

Wolong Town, administered by Pingquan City, is geographically situated between 118°32′27″ E–118°50′07″ E longitude and 40°59′48″ N–41°10′50″ N latitude, with a total area of 230.23 km², as shown in Figure 1. Climatically, the town features a temperate continental monsoon climate, characterized by distinct seasonal variations. Its annual precipitation ranges from 300 mm to 800 mm, while the annual potential evaporation reaches approximately 1800 mm. Tectonically, the town lies at the junction of the Yanshan Fold Belt (North China Platform) and the Inner Mongolia Axis-a geological setting that exerts a fundamental control on its geological and hydrogeological conditions. Topographically, Wolong Town exhibits a “west-high, east-low” slope, with elevations ranging from 700 m to 920 m (maximum relative relief of 220 m), and belongs to a low-mountain landform. The Pu River, the largest river in the area, flows from north to south through several villages, with a total length of 5.8 km. Regarding mineral resources, proven underground deposits include iron, gold, and silica, among which iron ore is the primary exploited resource—boasting geological reserves of 280 million tons and recoverable reserves of 150 million tons. Iron ore mining activities are concentrated in the vicinity of Niangniaomiao Village in the western part of the town.

Groundwater in the study area is predominantly classified into two types: unconsolidated rock pore water and bedrock fissure water, forming a hydrogeological pattern characterized by “water-rich valleys and water-scarce highlands.” The sandy gravel aquifer in the Puhe River Valley, with a thickness of 5–15 m, exhibits the highest water yield potential among all aquifer types. In contrast, the Mesozoic volcanic rock fissure aquifer in the central part of the town has uneven permeability, which is primarily controlled by the degree of rock weathering. Karst fissure water is only locally developed in the Paleozoic limestone formations in the northern region, with a limited spatial distribution. Groundwater flow in the study area follows two main directions: east-to-west and north-to-south. Atmospheric precipitation infiltration and lateral infiltration from the Puhe River dominate the recharge sources. Discharge pathways primarily include artificial pumping (for domestic, agricultural, and industrial use) and natural spring overflow. Monitoring wells in the area typically have a depth of 15–30 m, and the depth of the groundwater level ranges from 1.2 to 6.0 m.

2.2. Sample Collection and Laboratory Analysis

A total of 66 shallow groundwater samples (as shown in Figure 2) were collected from domestic and agricultural wells in Wolong Town between May to September 2022. All sample collection procedures strictly adhered to the Technical Specifications for Environmental Monitoring of Groundwater (HJ 164–2020) to guarantee data reliability. Prior to sampling, each well was pumped for 15–20 min to flush stagnant water in the wellbore and tubing, ensuring that the collected samples accurately reflected the in situ groundwater chemistry. Polyethylene sampling bottles (pre-rinsed with deionized water) were then thoroughly rinsed 3 times with the groundwater to be sampled to minimize cross-contamination. Samples were preserved differently based on the target parameters to prevent chemical transformation during storage. For the determination of cations (Na⁺, K⁺, Ca²⁺, Mg²⁺), concentrated nitric acid (analytical grade) was added to acidify the samples to pH 1–2, after which the samples were sealed in polyethylene bottles. For anions (Cl⁻, SO₄²⁻, HCO₃⁻, CO₃²⁻, NO₃⁻, NO₂⁻), samples were stored in polyethylene bottles in their original state without any chemical addition, to avoid altering the anion balance. For ammonia nitrogen (NH₃–N) analysis, concentrated sulfuric acid (analytical grade) was added to adjust the sample pH to <2, followed by sealing in polyethylene bottles to inhibit microbial decomposition of nitrogenous compounds. All preserved samples were immediately stored in a portable refrigerator at 4 °C and transported to the Laboratory of the Fourth Geological Brigade, Hebei Bureau of Geology and Mineral Exploration and Development on the same day of collection, ensuring that no more than 24 h elapsed between sampling and analysis—consistent with the quality control requirements for groundwater chemical analysis.

All groundwater chemical analyses were performed in strict accordance with the analytical protocols specified in China’s Groundwater Quality Standard (GB/T 14848-2017) [34] to ensure consistency with national technical norms. pH was determined using a calibrated pH meter (PHS-25, Shanghai Precision Scientific Instrument Co., Ltd., Shanghai, China). TDS was measured via the standard gravimetric method (WZB-175, Shanghai Precision Scientific Instrument Co., Ltd., Shanghai, China). Cations (Na⁺, K⁺, Ca²⁺, Mg²⁺) were analyzed using inductively coupled plasma optical emission spectroscopy (iCAP 6300, Thermo Fisher, Waltham, MA, USA). Cl⁻, SO₄²⁻, HCO₃⁻ and CO₃²⁻ were determined by Titration. Nitrogen species (NH₃-N, NO₃⁻, NO₂⁻) were measured using a UV-visible spectrophotometer (UV-1240, Shimadzu, Kyoto, Japan). Quality control was ensured by analyzing blank samples, parallel samples, and certified reference materials; the relative standard deviation (RSD) of parallel samples was <5%, meeting analytical requirements.

2.3. Data Analysis

SPSS 19.0 software was used to conduct descriptive statistical analysis (e.g., mean, standard deviation, coefficient of variation) on groundwater hydrochemical parameters, and to explore correlations between key ions. PCA was also performed using SPSS 19.0 to reduce the dimensionality of 13 hydrochemical indicators. Prior to analysis, data were standardized to eliminate the influence of different units. The Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test of Sphericity were used to verify the suitability of PCA, and varimax rotation was applied to enhance the interpretability of extracted principal components. Origin 2023 software was used to generate graphical tools for deciphering the dominant hydrogeochemical processes controlling groundwater evolution, including Piper Trilinear Diagram, Gibbs Diagrams, Ion Ratio Plots and Chlor-Alkaline Indices (CAI). The GeoDetector tool was applied to quantify the explanatory power of potential factors on the spatial heterogeneity of groundwater nitrate concentrations. Six influencing factors were selected based on regional characteristics. The explanatory power was represented by the q-value (range: 0–1), where a higher q-value indicates a stronger control of the factor on nitrate spatial distribution. Additionally, the interactive detection module of GeoDetector was used to analyze the combined effects of different factor pairs (e.g., population density × soil type) on nitrate concentrations. ArcGIS 10.7 software was used to generate Hydrogeological map, sampling site Map, spatial distribution maps of groundwater hydrochemical parameters (e.g., TDS, NO₃⁻, NH₃-N) and nitrate health risk indices. The inverse distance weighting (IDW) interpolation method was adopted for spatial interpolation, as it is well-suited for datasets with unevenly distributed sampling points (e.g., dense points in the central valley, sparse points in high-elevation areas). This approach enabled the clear visualization of spatial patterns and hotspots of groundwater chemical properties and pollution risks.

2.4. Health Risk Assessment

The US Environmental Protection Agency (USEPA) HHRA model [35,36] was used to evaluate the non-carcinogenic health risks of nitrate via two exposure pathways: drinking water ingestion and dermal contact.

2.4.1. Core Formulas

The non-carcinogenic risk index (HI) is defined as the ratio of chronic daily intake (I_CD) to the reference dose (D_Rf):

$$\mathit{HI}={\displaystyle \frac{I_{\mathit{CD}}}{D_{\mathit{Rf}}}}$$

(1)

where

HI < 1: Risk is within the acceptable range; no significant adverse health effects are expected;

HI ≥ 1: Risk exceeds the acceptable threshold; adverse health effects may occur with long-term exposure.

Total I_CD is the sum of ingestion I_CD (I_CDI) and dermal contact I_CD (I_CDD):

$$I_{\mathit{CD}}=I_{\mathit{CDI}}+I_{\mathit{CDD}}$$

(2)

where

$$I_{\mathit{CDI}}={\displaystyle \frac{C\times \mathit{IR}\times \mathit{ABS}\times \mathit{EF}\times \mathit{ED}}{\mathit{BW}\times \mathit{AT}}}$$

(3)

$$I_{\mathit{CDD}}={\displaystyle \frac{C\times \mathit{SA}\times \mathit{Kp}\times \mathit{EV}\times \mathit{EF}\times \mathit{ED}\times \mathit{CF}}{\mathit{BW}\times \mathit{AT}}}$$

(4)

In the formulas, the definitions of each parameter are as follows:

C: Measured concentration of nitrate in groundwater (mg/L);

IR: Daily drinking water intake rate (L/d);

ABS: Gastrointestinal absorption coefficient, whose value is correlated with pollutant concentration;

EF: Exposure frequency, referring to the annual cumulative exposure duration (d/a);

ED: Exposure duration, representing the number of years during which the substance is ingested by the human body (a);

BW: Average body weight of residents (kg);

AT: Average exposure time, denoting the average duration over which exposure occurs (d);

SA: Skin contact surface area (cm²);

K_p: Skin permeability coefficient of the pollutant (cm/h);

EV: Daily bathing frequency (times/d);

CF: Conversion factor (L/cm³).

2.4.2. Parameter Definition and Values

Parameters were determined based on USEPA guidelines [37] and regional characteristics (

Table 1. Parameters of Health Risk Assessment Model [22,37,38].

Parameter	Unit	Infants	Children	Adult Males	Adult Females
DRf of nitrate (DRf)	mg/(kg·d)	1.6	1.6	1.6	1.6
Daily water intake (IR)	L/d	0.64	1.5	2.0	2.0
Gastrointestinal absorption (ABS)	-	0.5	0.5	0.5	0.5
Exposure frequency (EF)	d/a	365	365	365	365
Exposure duration (ED)	a	1	12	30	30
Body weight (BW)	kg	10	30	69.55	60.4
Average contact time (AT)	d	365 × ED	365 × ED	365 × ED	365 × ED
Skin surface area (SA)	cm2	5000	12,000	16,000	15,000
Skin permeability (KP)	cm/h	0.001	0.001	0.001	0.001
Bathing frequency (EV)	Times/d	1	1	1	1
Conversion factor (CF)	L/cm3	0.001	0.001	0.001	0.001

). The D_Rf for nitrate was 1.6 mg/(kg·d) [20].

3. Results and Discussion

3.1. Groundwater Hydrochemical Composition Analysis

3.1.1. Statistical Characteristics of Hydrochemical Parameters

To characterize the hydrochemical properties of groundwater in the study area, descriptive statistical analysis was performed on all measured hydrochemical indicators, with results summarized in

Table 2. Statistical summary of groundwater hydrochemical parameters.

Indicator	Unit	Maximum	Minimum	Average	Standard Deviation	Coefficient of Variation (%)
pH	-	8.4	7.0	7.6	0.31	4.1
TDS	mg/L	900	90.0	371	187	50.3
Na+	mg/L	105	5.68	22.6	19.6	86.6
K+	mg/L	13.7	0.60	2.33	2.10	90.0
Ca2+	mg/L	187	18.2	74.5	39.5	53.0
Mg2+	mg/L	39.9	4.24	14.6	7.45	51.1
Cl−	mg/L	147	8.15	39.5	33.4	84.5
SO42−	mg/L	236	12.1	79.4	51.4	64.8
HCO3−	mg/L	277	36.3	164	55.3	33.8
CO32−	mg/L	20.8	0.00	1.15	3.52	307
NO3−	mg/L	259	0.94	47.8	57.6	121
NH3-N	mg/L	0.39	0.00	0.07	0.08	115
NO2−	mg/L	0.90	0.00	0.04	0.12	279

and visualized in Figure 3. The analysis shows that the pH values of groundwater range from 7.0 to 8.4, with a mean value of 7.6, indicating that the groundwater is overall weakly alkaline, which is consistent with the typical pH range of groundwater in carbonate-dominated low mountain-hill regions. The TDS concentrations vary from 90.0 to 900 mg/L, with an average of 371 mg/L. According to the classification standard for groundwater salinity (TDS < 1000 mg/L = fresh water), the groundwater in the study area is categorized as fresh water, suitable for most domestic and agricultural uses. As illustrated in the hydrochemical component statistics (Table 2) and boxplots (Figure 3), the order of mean mass concentrations of cations in groundwater is Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, while the order of anions is HCO₃⁻ > SO₄²⁻ > NO₃⁻ > Cl⁻. Among major cations: Ca²⁺ and Na⁺ are the dominant species, with average concentrations of 74.5 mg/L and 22.6 mg/L, respectively. The high Ca²⁺ concentration is likely attributed to the dissolution of carbonate minerals (e.g., calcite, dolomite) in the underlying strata, while Na⁺ may originate from both silicate weathering (e.g., albite) and minor anthropogenic inputs. Among major anions: HCO₃⁻ is the absolute dominant anion, with an average concentration of 164 mg/L—significantly higher than that of SO₄²⁻ (79.4 mg/L) and Cl⁻ (39.5 mg/L). This dominance of HCO₃⁻ further supports the influence of carbonate dissolution on groundwater hydrochemistry, as HCO₃⁻ is the primary product of calcite and dolomite dissolution.

Spatial variability of hydrochemical parameters was evaluated using the coefficient of variation (CV), where CV < 50% indicates weak variability, 50% ≤ CV < 100% indicates strong variability, and CV ≥ 100% indicates extremely strong variability [39]. pH and HCO₃⁻ exhibit relatively low CV values (4.1% and 33.8%, respectively), reflecting their uniform spatial distribution. This is likely because pH is primarily regulated by stable carbonate buffering systems, and HCO₃⁻ is widely produced by regional carbonate weathering—processes that are spatially consistent across the study area. Most indicators, including TDS, Ca²⁺, Mg²⁺, SO₄²⁻, Na⁺, K⁺, and Cl⁻, have CV values ranging from 50% to 90%. This strong variability suggests that the hydrogeochemical processes controlling these ions (e.g., evaporation-concentration in arid valleys, lithological leaching in heterogeneous bedrock, and localized anthropogenic inputs) are spatially heterogeneous. CO₃²⁻, NO₃⁻, NH₃-N, and NO₂⁻ exhibit CV values exceeding 100%. Such extreme variability is typically associated with local point-source pollution [40]—for example, NO₃⁻ hotspots may correspond to areas with intensive agricultural fertilization or domestic sewage discharge, while NH₃-N accumulation may be linked to localized reducing environments (e.g., stagnant groundwater zones).

3.1.2. Groundwater Hydrochemical Types

To systematically classify groundwater hydrochemical types and clarify the dominant ion assembly characteristics in the study area, a Piper trilinear diagram was constructed [41], as shown in Figure 4. Samples of Bedrock fissure water are predominantly clustered in the HCO₃⁻-Ca²⁺ zone of the Piper diagram. This type is typically formed in relatively closed hydrogeological environments, where carbonate dissolution (e.g., calcite (CaCO₃) and dolomite (CaMg(CO₃)₂)) is the dominant process. The weak hydraulic connectivity of fissure aquifers limits the mixing of groundwater with external ion sources, further consolidating the dominance of HCO₃⁻ and Ca²⁺. Most pore water samples also fall into the HCO₃⁻-Ca²⁺ type, consistent with the regional carbonate weathering background. However, a subset of pore water samples shows elevated concentrations of Cl⁻ and SO₄²⁻, leading to the formation of mixed types: HCO₃⁻·SO₄²⁻-Ca²⁺ and HCO₃⁻·Cl⁻-Ca²⁺.

The distinct hydrochemical characteristics between pore water and fissure water, as well as the formation of mixed types in pore water, are primarily driven by differences in aquifer properties and human activity impacts. Pore water is distributed in the unconsolidated deposits of the Puhe River valley, featuring extensive spatial coverage and active recharge (e.g., infiltration from precipitation and river water). Compared to fissure water, pore water has stronger hydraulic conductivity and more frequent interactions with the unsaturated zone, making it more susceptible to external ion inputs. The presence of preferential flow paths (e.g., macropores in agricultural soils) and bedrock fractures in the pore water zone creates rapid migration channels for surface pollutants [42]. For instance, Cl⁻ enrichment may originate from domestic sewage discharge or livestock manure leaching, while elevated SO₄²⁻ could be attributed to agricultural fertilizer application (e.g., ammonium sulfate) or minor seepage from iron ore mining waste residues. These anthropogenic inputs alter the original anion balance of pore water, leading to the emergence of mixed hydrochemical types.

3.2. Correlation Analysis and Principal Component Analysis

3.2.1. Correlation Analysis

In hydrogeochemical research, correlation analysis serves as a widely employed methodological approach. Specifically, correlation analysis of hydrochemical components enables the revelation of correlations among different hydrochemical constituents in aqueous systems, while also facilitating the identification of similarities and disparities in their provenances [43,44]. In this study, 11 components including TDS, pH, Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, CO₃²⁻, NO₃^- were selected for correlation analysis, as shown in Figure 5. The results indicate that TDS was significantly positively correlated with Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and NO₃⁻ (p < 0.01, r = 0.57–0.90), indicating these ions are the primary contributors to groundwater mineralization. pH was negatively correlated with most cations (r = −0.12–0.35), suggesting slightly acidic environments may promote the dissolution of carbonate and silicate minerals (releasing Ca²⁺ and Mg²⁺).

Ca²⁺ and HCO₃⁻ (r = 0.70), Mg²⁺ and HCO₃⁻ (r = 0.67) were significantly positively correlated, indicating carbonate weathering are major sources of these ions. Na⁺ and SO₄²⁻ were positively correlated (r = 0.52), potentially from albite (NaAlSi₃O₈) weathering or gypsum (CaSO₄·2H₂O) dissolution. However, the correlation between Ca²⁺ and SO₄²⁻ is weaker, suggesting that the weathering of albite (NaAlSi₃O₈) may play a dominant role. Cl⁻ exhibits strong positive correlations with Ca²⁺ (0.82) and Mg²⁺ (0.62), which may be related to the dissolution of rock salt (NaCl) and calcium/magnesium chloride minerals, or influenced by lateral recharge of chlorine-bearing groundwater.

A significant positive correlation is observed between Na⁺ and SO₄²⁻, which may originate from the dissolution of feldspar (e.g., albite) or gypsum (CaSO₄·2H₂O). In contrast, the correlation between Ca²⁺ and SO₄²⁻ is relatively weak, implying that the weathering of albite (NaAlSi₃O₈) might exert a dominant control. Chloride ions (Cl⁻) demonstrate strong positive correlations with Ca²⁺ (correlation coefficient: 0.82) and Mg²⁺ (correlation coefficient: 0.62), which may be caused by leaching processes—such as the weathering of silicates, carbonate rocks, halite, and other minerals.

Nitrate (NO₃⁻) exhibited a strongly positive correlation with calcium ions (Ca²⁺) (correlation coefficient, r = 0.85) and chloride ions (Cl⁻) (r = 0.73). Furthermore, NO₃⁻ showed a strong correlation with total dissolved solids (TDS) (r = 0.74), and this correlation magnitude is far higher than the natural background level. This observation suggests that nitrate in the groundwater is predominantly of anthropogenic origin (e.g., nitrogenous fertilizers, livestock manure), with the concomitant infiltration of associated Ca²⁺ and Cl⁻ into the groundwater system through soil leaching processes.

3.2.2. Principal Component Analysis

Principal Component Analysis (PCA) is a multivariate statistical dimensionality reduction technique that transforms a set of potentially correlated original variables into a smaller set of uncorrelated comprehensive variables (termed “principal components”). To enhance the interpretability of these components, varimax rotation—a orthogonal rotation method that maximizes the variance of factor loadings across variables—was applied to derive distinct factor variables [45,46]. In this study, PCA was performed using SPSS software on 13 groundwater hydrochemical indicators (including pH, TDS, major cations/anions, and nitrogen species). Prior to analysis, all data were standardized (z-score normalization) to eliminate the influence of differing units and magnitude disparities among indicators. Two statistical tests were conducted to verify the suitability of the dataset for PCA. The KMO index was calculated as 0.62. According to Kaiser’s criterion, a KMO value between 0.6 and 0.7 indicates a “marginally suitable” dataset for factor analysis, confirming that the indicators have sufficient common variance to support dimensionality reduction. Bartlett’s Test of Sphericity yielded a significance value (p-value) of <0.05 (approaching 0), which demonstrates that the hydrochemical indicators are significantly correlated, further validating the appropriateness of PCA.

Based on the eigenvalue > 1 criterion (a widely accepted threshold for principal component extraction), 4 principal factors (F1–F4) were extracted from the rotated factor loading matrix (

Table 3. Varimax-rotated component matrix for groundwater ions.

Indicator	F1	F2	F3	F4
pH	−0.269	0.631	0.443	−0.082
TDS	0.962	−0.004	0.066	0.193
Na+	0.618	0.54	−0.400	0.037
K+	0.319	0.654	−0.194	0.284
Ca2+	0.911	−0.349	0.051	0.020
Mg2+	0.807	0.110	0.353	−0.200
Cl−	0.870	−0.117	−0.098	−0.047
SO42−	0.676	0.495	−0.454	0.08
HCO3−	0.619	−0.201	0.374	0.019
CO32−	0.083	0.658	0.544	−0.255
NH3-N	−0.274	−0.259	−0.057	0.463
NO2−	−0.019	0.123	0.484	0.753
NO3−	0.725	−0.511	0.196	−0.057
Eigenvalues	5.166	2.327	1.454	1.024
Variance Contribution Rate (%)	39.737	17.901	11.185	7.879
Cumulative Contribution Rate (%)	39.737	57.637	68.823	76.702

). The cumulative variance contribution rate of these 4 factors reached 76.702%, meaning they collectively explain 76.702% of the total variance in the original 13 indicators. This high cumulative contribution rate indicates that the extracted principal factors effectively capture the majority of information embedded in the original dataset, justifying the use of PCA for simplifying the complex hydrochemical dataset while retaining key information.

F1 accounts for 39.737% of the total variance, making it the primary driver of groundwater hydrochemical characteristics. It exhibits strong positive loadings on total dissolved solids (TDS, 0.962)—a core indicator of groundwater mineralization—and major ions including Ca²⁺ (0.911), Cl⁻ (0.870), and Mg²⁺ (0.807), as well as a significant positive loading on NO₃⁻ (0.725). The high loadings of Ca²⁺, Mg²⁺, and TDS indicate that natural water-rock interaction (e.g., dissolution of calcite (CaCO₃), dolomite (CaMg(CO₃)₂), and halite (NaCl)) is a key process regulating ion enrichment [47]. The strong correlation with NO₃⁻ (a typical anthropogenic indicator) suggests the co-influence of anthropogenic nitrogen inputs, such as agricultural fertilizer leaching and domestic sewage infiltration [48]. Collectively, F1 reflects the synergistic effect of “natural mineral dissolution + anthropogenic nitrogen input,” directly determining the overall mineralization level and ion accumulation intensity of groundwater.

F2 explains 17.901% of the variance, characterized by high positive loadings on pH (0.631), K⁺ (0.654), and CO₃²⁻ (0.658), and negative loadings on Ca²⁺ (−0.349) and NO₃⁻ (−0.511). The positive loadings of pH and CO₃²⁻ indicate that F2 is closely related to groundwater alkalinity, which is primarily regulated by carbonate buffering systems. The negative loading of Ca²⁺ implies cation exchange processes (e.g., Na⁺/K⁺ displacing adsorbed Ca²⁺ on mineral surfaces), while the negative correlation between NO₃⁻ and pH suggests that NO₃⁻ is more prone to accumulation in weakly acidic environments—this is because acidic conditions inhibit microbial denitrification (a process that consumes NO₃⁻) [49]. Thus, F2 primarily reflects the spatial differentiation of groundwater alkalinity and the regulatory role of cation exchange in ion balance.

F3 contributes 11.185% of the variance, with positive loadings on CO₃²⁻ (0.544) and NO₂⁻ (0.484) and a negative loading on SO₄²⁻ (−0.454). This indicates that F3 represents the coupled effect of nitrogen transformation (NO₂⁻ accumulation) and anion differentiation under weakly reducing hydrogeochemical conditions. F4 accounts for 7.879% of the variance, with strong positive correlations only with NH₃-N (0.463) and NO₂⁻ (0.753)—both intermediate products of reduced nitrogen, representing the enrichment characteristics of intermediate products of reduced nitrogen.

Synthesizing the interpretation of F1–F4, the hydrochemical evolution of groundwater in the study area is jointly regulated by three core processes: natural water-rock interaction—anthropogenic nitrogen input—environmental redox differentiation. This “triple synergy” mechanism comprehensively explains the spatial variability of groundwater hydrochemical characteristics in the study area.

3.3. Natural Controls on Groundwater Hydrochemistry

3.3.1. Gibbs Diagram Analysis

Gibbs diagrams are commonly used to distinguish the influences of atmospheric precipitation, rock weathering, and evaporation-concentration on groundwater chemistry [50,51]. By utilizing the relationships between cation concentration ratios, anion concentration ratios, and total dissolved solids (TDS), Gibbs diagrams macroscopically reflect the controlling factors of major ions. Based on the distribution of data points, they can indicate three genetic types: evaporation-concentration type, rock weathering type, and precipitation-controlled type [52,53]. As shown in Figure 6, most groundwater samples in the study area are distributed in the rock weathering zone, indicating that water-rock interaction is the main mechanism for the formation of groundwater hydrochemical types. A small number of sampling points fall in the evaporation-concentration zone, suggesting that groundwater in the study area is affected by evaporation-concentration. However, Gibbs diagrams can only analyze the impacts of natural processes on groundwater; anthropogenic processes should be evaluated through other methods.

3.3.2. Ion Ratio Analysis

The application of ion ratio relationships enables the identification of sources of major ions and facilitates further analysis of the hydrochemical evolution processes of groundwater [41,53]. The natural sources of chloride ions (Cl⁻) and sodium ions (Na⁺) in groundwater are primarily derived from the dissolution of halite (NaCl) in sedimentary rocks. Theoretically, the dissolution of halite releases equimolar amounts of Na⁺ and Cl⁻, which implies a molar ratio of γ(Na⁺)/γ(Cl⁻) = 1 [50,54]. As illustrated in Figure 7a, a weak positive correlation is observed between γ(Na⁺) and γ(Cl⁻) in the groundwater samples collected from the study area. For some samples, the γ(Na⁺)/γ(Cl⁻) ratio falls above the 1:1 reference line, indicating that the excess Na⁺ may be sourced from the weathering of silicate minerals. For other samples, the γ(Na⁺)/γ(Cl⁻) ratio lies below the 1:1 reference line, suggesting that the excess Cl⁻ could be ascribed to anthropogenic pollution of the groundwater [55].

Under natural conditions, the dissolution of carbonate minerals produces a molar ratio of γ(Ca²⁺ + Mg²⁺)/γ(HCO₃⁻) equal to 1. As illustrated in Figure 7b, the majority of samples in the study area exhibit a significant positive correlation between HCO₃⁻ and Ca²⁺ + Mg²⁺, with the γ(Ca²⁺ + Mg²⁺)/γ(HCO₃⁻) ratio lying above the 1:1 reference line. Specifically, the γ(HCO₃⁻) concentration ranges from 0 to 6 meq/L, while the γ(Ca²⁺ + Mg²⁺) concentration ranges from 0 to 12 meq/L. This observation indicates that carbonate weathering serves as the primary source of ions in groundwater. In addition to carbonate dissolution, Ca²⁺ and Mg²⁺ may also be derived from other sources (e.g., sulfate mineral dissolution) [28].

The ratio γ(Ca²⁺ + Mg²⁺)/γ(HCO₃⁻ + SO₄²⁻) can be used to identify the sources of Ca²⁺ and Mg²⁺. As shown in Figure 7c, most samples are distributed near the 1:1 line, indicating that Ca²⁺ and Mg²⁺ are mainly derived from the dissolution of calcite, dolomite, and gypsum, and there is a synergistic effect between carbonate and sulfate weathering [56]. This is also confirmed by Figure 7d: all γ(Ca²⁺)/γ(SO₄²⁻) ratios of groundwater samples lie above the 1:1 line, which suggests that gypsum dissolution is not the only source of Ca²⁺, and Ca²⁺ is simultaneously dominated by carbonate dissolution.

3.3.3. Chlor-Alkaline Index (CAI)

The Chloride-Alkalinity Index (CAI) is a critical method for investigating cation exchange processes [57]. When both CAI-I and CAI-II values are greater than 0, it indicates that Na⁺ and K⁺ in the aqueous system exchange with Ca²⁺ and Mg²⁺ adsorbed on the surfaces of rock and soil phases, releasing the latter into the water and resulting in a reverse ion exchange reaction. Conversely, if both values are less than 0, it signifies that Ca²⁺ and Mg²⁺ in groundwater displace Na⁺ and K⁺ in the aqueous medium, undergoing a forward ion exchange reaction [58]. The larger the absolute value of the CAI, the stronger the ion exchange intensity. As depicted in Figure 8, 40.6% of the groundwater samples fall within the range where both CAI values are greater than 0, implying an elevated Na⁺ content in groundwater. This elevation induces a reverse cation exchange adsorption effect, which displaces Ca²⁺ and Mg²⁺ in the aqueous medium. Notably, the sampling points where this reverse cation exchange adsorption occurs are primarily concentrated in the central region with intensive anthropogenic activities. This observation indicates that anthropogenic activities contribute to the increase in groundwater Na⁺ concentration, thereby triggering the reverse cation exchange adsorption effect.

3.4. Anthropogenic Controls on Groundwater Hydrochemistry

In the study area, the characteristics of water samples dominated by nitrate underscore the significant impact of anthropogenic factors on groundwater chemical properties [59]. To further elucidate how anthropogenic activities influence groundwater chemical characteristics, this study focuses on analyzing nitrate (NO₃⁻), chloride (Cl⁻), and sulfate (SO₄²⁻)—components sensitive to anthropogenic disturbances. As a stable constituent in natural aquatic systems, Cl⁻ concentration varies solely through mixing with other chloride-containing sources, rendering it an effective tracer for sewage or fecal pollution, as well as dilution effects [60,61]. Similarly, SO₄²⁻ and NO₃⁻ can act as indicators for industrial emissions and agricultural/livestock activities, respectively [51,62]. As illustrated in Figure 9a, the molar ratios of n(NO₃⁻/Ca²⁺) and n(SO₄²⁻/Ca²⁺) in most groundwater samples are consistent with the impacts of agricultural wastewater and domestic sewage, while a small subset of samples is additionally affected by industrial activities. Figure 9b further confirms that groundwater quality in this region is influenced by both agricultural wastewater and domestic sewage. As shown in Figure 9c, in addition to the natural soil nitrogen content, the nitrate content in the study area’s groundwater is significantly affected by inputs from domestic sewage and manure. This finding confirms that agricultural activities, domestic sewage discharge, and manure emissions are the primary driving factors of groundwater pollution.

3.5. Factors for Controlling Nitrate and Health Risk Assessment

3.5.1. Spatial Distribution of Nitrate

Groundwater monitoring results showed that the NO₃⁻ concentration ranged from 0.94 to 259 mg/L, with an average of 47.8 mg/L. Among the samples, 37.8% exceeded the limit specified in the National Standard for Drinking Water Quality (GB 5749–2002) [63]—the limit for nitrate nitrogen (NO₃⁻-N) is 10 mg/L, which is equivalent to a nitrate (NO₃⁻) concentration limit of 44.3 mg/L. The maximum concentration at a single site exceeded the limit by 5.85 times, indicating that some groundwater in the study area is no longer suitable for direct drinking. In terms of spatial distribution (Figure 10), groundwater with high NO₃⁻ concentrations is mainly concentrated in the central river valley area, where population density is high and human activities are frequent; concentrations are lower in the high-elevation areas in the east and west. Previous studies have shown that excessive nitrate in groundwater is common in areas with intensive human activities, especially agricultural regions [64]. The concentrations of NO₂⁻ and NH₃-N in the study area’s groundwater are relatively low, and neither exceeds the limits specified in GB 5749–2002. Notably, the spatial distribution trend of NH₃-N concentration is the exact opposite of that of NO₃⁻: it is lower in the central river valley area and higher in the eastern area. This discrepancy may be attributed to the varying redox conditions of groundwater. Under oxidizing conditions, NH₃-N is oxidized to NO₃⁻; under reducing conditions; however, the concentration of NH₃-N tends to be higher.

3.5.2. Controlling Factors of Nitrate Evolution

GeoDetector, a statistical tool, is designed to clarify the underlying causes of spatial heterogeneity in survey data [65]. Asnakew Mulualem Tegegne et al. [66] applied GeoDetector to identify the primary factors influencing groundwater degradation in the Gunaibey Basin, Ethiopia. In this study, six factors were selected as potential controlling factors for the evolution of nitrate content in groundwater: elevation (DEM), soil type (ST), rock type (RT), soil nitrogen content (SN), population density (PD), and land use type (LU), as shown in Figure 11. The results showed that the significance levels (p-values) of all influencing factors were less than 0.01, indicating statistical significance. The relative explanatory power (q-values) of each factor is presented in Figure 10, ranked in descending order as follows: elevation > soil type > population density > rock type > land use type > soil nitrogen content. Among these, elevation, soil type, and population density were the primary driving factors, followed by rock type and land use type, while soil nitrogen content had a relatively minor impact on the evolution of nitrate content. The interactive detector was used to explore the combined explanatory impact of various factors on nitrate content, as shown in Figure 12. Notably, population density interacted with soil type, rock type, and soil nitrogen content, exhibiting high interaction intensities with q-values of 0.587, 0.552, and 0.554, respectively. This suggests that the intensification of human activities—such as excessive fertilization, irrigation practices, and the discharge of sewage and manure [67,68]—in combination with soil type and rock type, constitutes a significant driving force for nitrate evolution. Although soil nitrogen content alone has little impact on groundwater nitrate levels, when combined with the effects of human activities, it can exert a considerable influence.

3.5.3. Health Risk Assessment of Nitrate

Based on the nitrate concentrations in the groundwater of the study area, the non-carcinogenic health risk index (HI) was calculated for four population groups: infants, children, adult males, and adult females, as shown in Figure 13. According to the risk assessment criteria, an HI value less than 1 indicates that the potential health risk associated with groundwater exposure is at an acceptable level, whereas an HI value greater than 1 signifies an unacceptable level of risk [37]. The calculation results revealed the following: for infants, HI values ranged from 0.019 to 5.26 with a mean of 0.971, and among the 66 groundwater samples, 21 exhibited HI values exceeding 1, corresponding to an exceedance rate of 31.82%; for children, HI values varied from 0.015 to 4.11 with an average of 0.759, and seventeen out of the 66 samples had HI values above 1, resulting in an exceedance rate of 25.76%; for adult males, HI values spanned from 0.009 to 2.37 with a mean of 0.436, and eight samples (12.12% of the total) showed HI values exceeding the threshold of 1; for adult females, HI values ranged from 0.010 to 2.72 with an average of 0.502, and nine samples exceeded the HI = 1 threshold, representing an exceedance rate of 13.64%, with detailed information presented in Figure 12. From the perspective of exposure pathways, the exposure risk via drinking water is 2 to 3 orders of magnitude higher than that via dermal contact, rendering drinking water the primary exposure pathway. The contribution of dermal contact to overall risk is relatively negligible, generally not exceeding 0.06. In terms of population-specific distribution, significant disparities in risk levels were observed across different groups in the study area. The hierarchy of health risks follows the order: infants > children > adult females > adult males, which is consistent with findings from previous studies [21,22,69].

Regarding the spatial distribution of HI values (Figure 14), groundwater in the central valley region of the study area exhibits high nitrate concentrations, with risk coefficients exceeding the acceptable threshold. This indicates that this portion of groundwater is unsuitable for direct human consumption, as long-term unprocessed consumption would pose substantial health risks. In contrast, groundwater in the high-altitude areas of the east and west has lower nitrate concentrations, and the associated health risks fall within the acceptable range, making these areas suitable as drinking water sources. Therefore, it is recommended to mitigate human health risks by optimizing water source selection for centralized water supply or enhancing water treatment processes [38,70].

4. Conclusions

The shallow groundwater in Wolong Town is weakly alkaline (pH: 7.0–8.4) and fresh (TDS: 90.0–900 mg/L), with cation concentrations following the order Ca²⁺ > Na⁺ > Mg²⁺ > K⁺ and anion concentrations following HCO₃⁻ > SO₄²⁻ > NO₃⁻ > Cl⁻. The dominant hydrochemical type is HCO₃⁻-Ca²⁺. The hydrochemical evolution of groundwater in the study area is jointly regulated by natural water-rock interaction—anthropogenic nitrogen input—environmental redox differentiation. Water-rock interaction is the primary process controlling groundwater hydrochemistry, with contributions from calcite, dolomite, halite, and gypsum dissolution, as well as cation exchange. Anthropogenic activities (agricultural fertilization, domestic sewage) further alter hydrochemical composition, particularly by increasing nitrate concentrations.

Overall, 37.8% of groundwater samples exceeded the GB 5749–2002 nitrate limit, with high-nitrate areas concentrated in the central valley (intensive human activities). Agricultural nitrogen input, domestic sewage, and livestock manure are the primary sources of nitrate pollution. Elevation, soil type, and population density are the key controlling factors of nitrate spatial distribution. The non-carcinogenic health risks of nitrate are highest for infants and children (31.82% and 25.76% of samples exceed HI = 1, respectively), with drinking water ingestion being the dominant exposure pathway. Long-term consumption of high-nitrate groundwater may pose adverse health effects.

To mitigate health risks, we recommend: optimizing centralized water supply (prioritizing high-elevation low-nitrate groundwater); enhancing water treatment (e.g., biological denitrification) for high-nitrate groundwater; reducing agricultural nitrogen input (promoting precision fertilization) in the central valley. Future research in this study area could focus on refined identification of nitrate sources using multi-tracer techniques; exploration of nitrate migration and transformation mechanisms in complex aquifer systems; development of targeted nitrate pollution control and remediation strategies; integration of long-term monitoring and predictive modeling.