Journal of Groundwater Science and Engineering

Health risk assessment of Fluoride and Cadmium enrichment in rural drinking groundwater in Shanxi Province, China

Qi-fa Sun, Bing Lu, Chuan-lei Lu, Yuan Yang, Xu Xie, Lin Guo, Chen Hu, Xu Wang

2026, 14(1): 1-14. doi: 10.26599/JGSE.2026.9280067

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Excessive levels of Fluoride (F⁻) and Cadmium (Cd) in drinking groundwater may pose health risks. This study assessed the health risks associated with F⁻ and Cd contamination in rural drinking groundwater sources in Wutai County, Shanxi Province, China, to support population health protection, water resource management, and environmental decision-making. Groundwater samples were collected and analyzed, and a Human Health Risk Model (HHRA) was applied to evaluate groundwater quality. The results showed that both contents of F⁻ and Cd in groundwater exceeded the Class III limits of China's national groundwater quality standard (GB/T 14848—2024). Fluoride levels met the Class V threshold, with enrichment area mainly located in the east part of the study area. Cadmium levels reached Class IV, with elevated concentrations primarily observed in the western and northwestern regions. Correlation analysis revealed that F⁻ showed weak or no correlation with other measured substances, indicating independent sources. Health risk assessment results indicated that F⁻ poses potential health risks to rural residents, while cadmium, due to its relatively low concentrations, does not currently present a significant health risk. Among different demographic groups, the health risk levels of F⁻ exposure followed the order: Infants >children >adult females >adult males. The findings highlight that fluoride is the primary contributor to health risks associated with groundwater consumption in the study area. Strengthened monitoring and prevention of F⁻ contamination are urgently needed. This research provides a scientific basis for the prevention and control of fluoride pollution in groundwater and offers practical guidance for safeguarding drinking water safety in rural China.

Study on the strength deterioration characteristics and microscopic mechanisms of moraine soil under freeze-thaw cycles

Peng-fei Wang, Ming-li Li, Ming Chang, Jun-lin Jiang, Fan Yang, Zhi-qiang Zuo

2026, 14(1): 15-31. doi: 10.26599/JGSE.2026.9280068

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To investigate the strength degradation characteristics and microscopic damage mechanisms of moraine soil under hydro-thermo-mechanical coupling conditions, a series of X-ray Diffraction (XRD), standard triaxial testing, Scanning Electron Microscopy (SEM), and Nuclear Magnetic Resonance (NMR) experiments were conducted. The mechanical property degradation laws and evolution characteristics of the microscopic pore structure of moraine soil under Freeze-Thaw (F-T) conditions were revealed. After F-T cycles, the stress-strain curves of moraine soil showed a strain-softening trend. In the early stage of F-T cycles (0–5 cycles), the shear strength and elastic modulus exhibited damage rate of approximately 10.33% ± 0.8% and 16.60% ± 1.2%, respectively. In the later stage (10–20 cycles), the strength parameters fluctuated slightly and tended to stabilize. The number of F-T cycles was negatively exponentially correlated with cohesion, while showing only slight fluctuation in the internal friction angle, thereby extending the Mohr-Coulomb strength criterion for moraine soil under F-T cycles. The NMR experiments quantitatively characterized the evolution of the internal pore structure of moraine soil under F-T cycles. As the number of F-T cycles increased, fine and micro pores gradually expanded and merged due to the frost-heaving effect during the water-ice phase transition, forming larger pores. The proportion of large and medium pores increased to 59.55% ± 2.1% (N=20), while that of fine and micro pores decreased to 40.45% ± 2.1% (N=20). The evolution of pore structure characteristics was essentially completed in the later stage of F-T cycles (10–20 cycles). This study provides a theoretical foundation and technical support for major engineering construction and disaster prevention in the Qinghai-Xizang Plateau.

Application of the DITAPH model coupling human activities and groundwater dynamics for nitrate vulnerability assessment: A case study in Quanzhou, China

Jian-feng Li, Yuan-jing Zhang, Ya-ci Liu, Qi-chen Hao, Chun-lei Liu, Sheng-wei Cao, Zheng-hong Li

2026, 14(1): 32-48. doi: 10.26599/JGSE.2026.9280069

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To address the deficiencies in comprehensive surface contamination prevention strategies within China's nitrate-affected regions, this research innovatively proposes the DITAPH model - a systematic framework integrating groundwater nitrate vulnerability assessment and Nitrate Vulnerable Zones (NVZs) delineation through optimization of hydrogeological parameters. Based on detailed hydrogeological and hydrochemical investigations, the DITAPH model was applied in the plain areas of Quanzhou to evaluate its applicability. The model selected hydrogeological parameters (depth of groundwater, lithology of the vadose zone, topographic slope, aquifer water yield property), one climatic parameter (precipitation), and two anthropogenic parameters (land use type and population density) as assessment indicators. The results of the groundwater nitrate vulnerability assessment showed that the low, relatively low, relatively high, and high groundwater nitrate vulnerability zones in the study area accounted for 5.96%, 35.44%, 53.74% and 4.86% of the total area, respectively. Groundwater nitrate vulnerability was most strongly influenced by human activities, followed by groundwater depth and topographic slope. The high vulnerability zone is mainly affected by domestic and industrial wastewater, whereas the relatively high groundwater nitrate vulnerability zone is primarily influenced by agricultural activities. Validation of the DITAPH model revealed a significant positive correlation between the DITAPH index (DI) and nitrate concentration (ρ(NO₃⁻)). The results of the NVZs delineated by the DITAPH model are reliable and can serve as a tool for water resource management planning, guiding the development of targeted measures in the NVZs to prevent groundwater contamination.

Precision and trueness of a method for determing antimony content in groundwater using hydride generation-atomic fluorescence spectrometry

Bing-bing Liu, Lin Zhang, Ke Li

2026, 14(1): 49-58. doi: 10.26599/JGSE.2026.9280071

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At present, there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China. The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated, which limits the effective implementation of environmental monitoring. In response to this key technical gap, this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation–Atomic Fluorescence Spectrometry (HG-AFS). Ten laboratories participated in inter-laboratory collaborative tests, and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2—2004 and GB/T 6379.4—2006. The consistency and outliers of the data were tested by Mandel's h and k statistics, the Grubbs test and the Cochran test, and the outliers were removed to optimize the data, thereby significantly improving the reliability and accuracy. Based on the optimized data, parameters such as the repeatability limit (r), reproducibility limit (R), and method bias value (δ) were determined, and the trueness of the method was statistically evaluated. At the same time, precision-function relationships were established, and all results met the requirements. The results show that the lower the antimony content, the lower the repeatability limit (r) and reproducibility limit (R), indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity. Therefore, improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision. This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.

Geographical differentiation of riverine DOM composition and source apportionment: A case study of a riverine network of a mountainous stream, a Plain River, and an artificial canal

Jun-wei Zhao, Shuang-bing Huang, Zhao-xin Su, Wei-chao Huang, Yong Qian

2026, 14(1): 59-68. doi: 10.26599/JGSE.2026.9280072

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To elucidate the geographical differentiation characteristics and driving mechanisms of Dissolved Organic Matter (DOM) in typical rivers, this study conducted a multi-spectral investigation on three representative river types within Shandong Province: The mountainous Dawen River, the plain Tuhai River, and the artificial East Grand Canal. The DOM composition was analyzed using Ultraviolet-Visible (UV-Vis) absorption spectroscopy, Excitation-Emission Matrix (EEM) fluorescence spectroscopy, and parallel factor analysis (PARAFAC), while Principal Component Analysis (PCA) was employed to quantify the synergistic effects of natural processes and anthropogenic activities. Results revealed significant spatial heterogeneity in DOM composition and sources. The plain river exhibited the highest aromaticity (humic-like components: 43.3%) due to long-term agricultural non-point source inputs and urban wastewater discharge. The mountain stream, shaped by complex terrain and relatively intact ecosystems, was dominated by autochthonous DOM derived from microbial metabolism, with higher Fluorescence Index (FI = 2.12) and biological index (BIX = 1.35) than other river types. The artificial canal retained protein-like components (64.2%), largely attributed to winter hydrological stagnation and disturbances from shipping activities. Further analysis demonstrated that geographical settings (e.g., mountain terrain) and anthropogenic activities (e.g., agriculture, shipping) jointly regulated DOM composition by altering the balance between input and transformation processes. Integrated fluorescence parameters and PCA results suggested differentiated management strategies: protecting ecological integrity in mountain streams to sustain self-purification, enhancing non-point source interception in plain rivers, and mitigating shipping pollution in canals. This study systematically reveals the natural-anthropogenic coupling mechanisms driving DOM dynamics in northern China rivers, providing critical insights for precision water environment management at the watershed scale.

Vulnerability assessment in fractured aquifer using improved vulnerability index: Applied to Gabes aquifer, Southeastern Tunisia

Mounir Atoui, Belgacem Agoubi

2026, 14(1): 69-82. doi: 10.26599/JGSE.2026.9280073

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The Gabes aquifer system, located in southeastern Tunisia, is a crucial resource for supporting local socio-economic activities. Due to its dual porosity structure, is particularly vulnerable to pollution. This study aims to develop a hybrid model that combines the Fracture Aquifer Index (FAI) with the conventional GOD (Groundwater occurrence, Overall lithology, Depth to water table) method, to assess groundwater vulnerability in fractured aquifer. To develop the hybrid model, the classical GOD method was integrated with FAI to produce a single composite index. Each parameter within both GOD and FAI was scored, and a final index was calculated to delineate vulnerable areas. The results show that the study area can be classified into four vulnerability levels: Very low, low, moderate, and high, indicating that approximately 8% of the area exhibits very low vulnerability, 29% has low vulnerability, 25% falls into the moderate category, and 38% is considered highly vulnerable. The FAI-GOD model further incorporates fracture network characteristics. This refinement reduces the classification to three vulnerability classes: Low, medium, and high. The outcomes demonstrate that 46% of the area is highly vulnerable due to a dense concentration of fractures, while 17% represents an intermediate zone characterized by either shallow or deeper fractures. In contrast, 37% corresponds to areas with lightly fractured rock, where the impact on vulnerability is minimal. Multivariate statistical analysis was employed using Principal Components Analysis (PCA) and Hierarchical Cluster Analysis (HCA) on 24 samples across six variables. The first three components account for over 76% of the total variance, reinforcing the significance of fracture dynamics in classifying vulnerability levels. The FAI-GOD model removes the very-low-vulnerability class and expands the spatial extent of low- and high-vulnerability zones, reflecting the dominant influence of fracture networks on aquifer sensitivity. While both indices use a five-class system, FAI-GOD redistributes vulnerability by eliminating very-low-vulnerability areas and amplifying low/high categories, highlighting the critical role of fractures. A strong correlation (R² = 0.94) between the GOD and FAI-GOD indices, demonstrated through second-order polynomial regression, confirms the robustness of the FAI-GOD model in accurately predicting vulnerability to pollution. This model provides a useful framework for assessing the vulnerability of complex aquifers and serves as a decision-making tool for groundwater managers in similar areas.

A state-of-the-art Fuzzy Nonlinear Additive Regression (FNAR) model for groundwater level prediction

Sepideh Zeraati Neyshabouri, Abbas Khashei-Siuki, Mohammad Ghasem Akbari

2026, 14(1): 83-99. doi: 10.26599/JGSE.2026.9280074

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Groundwater modeling remains challenging due to heterogeneity and complexity of aquifer systems, necessitating endeavors to quantify Groundwater Levels (GWL) dynamics to inform policymakers and hydrogeologists. This study introduces a novel Fuzzy Nonlinear Additive Regression (FNAR) model to predict monthly GWL in an unconfined aquifer in eastern Iran, using a 19-year (1998–2017) dataset from 11 piezometric wells. Under three distinct scenarios with progressively increasing input complexity, the study utilized readily available climate data, including Precipitation (Prc), Temperature (Tave), Relative Humidity (RH), and Evapotranspiration (ETo). The dataset was split into training (70%) and validation (30%) subsets. Results showed that among three input scenarios, Scenario 3 (Sc3, incorporating all four variables) achieved the best predictive performance, with RMSE ranging from 0.305 m to 0.768 m, MAE from 0.203 m to 0.522 m, NSE from 0.661 to 0.980, and PBIAS from 0.771% to 0.981%, indicating low bias and high reliability. However, Sc2 (excluding ETo) with RMSE ranging from 0.4226 m to 0.9909 m, MAE from 0.3418 m to 0.8173 m, NSE from 0.2831 to 0.9674, and PBIAS from −0.598% to 0.968% across different months offers practical advantages in data-scarce settings. The FNAR model outperforms conventional Fuzzy Least Squares Regression (FLSR) and holds promise for GWL forecasting in data-scarce regions where physical or numerical models are impractical. Future research should focus on integrating FNAR with deep learning algorithms and real-time data assimilation expanding applications across diverse hydrogeological settings.

AI and ML in groundwater exploration and water resources management: Concepts, methods, applications, and future directions

Adla Andalu, M Gopal Naik, Sandeep Budde

2026, 14(1): 100-122. doi: 10.26599/JGSE.2026.9280070

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) into groundwater exploration and water resources management has emerged as a transformative approach to addressing global water challenges. This review explores key AI and ML concepts, methodologies, and their applications in hydrology, focusing on groundwater potential mapping, water quality prediction, and groundwater level forecasting. It discusses various data acquisition techniques, including remote sensing, geospatial analysis, and geophysical surveys, alongside preprocessing methods that are essential for enhancing model accuracy. The study highlights AI-driven solutions in water distribution, allocation optimization, and real-time resource management. Despite their advantages, the application of AI and ML in water sciences faces several challenges, including data scarcity, model reliability, and the integration of these tools with traditional water management systems. Ethical and regulatory concerns also demand careful consideration. The paper also outlines future research directions, emphasizing the need for improved data collection, interpretable models, real-time monitoring capabilities, and interdisciplinary collaboration. By leveraging AI and ML advancements, the water sector can enhance decision-making, optimize resource distribution, and support the development of sustainable water management strategies.

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2026 Vol. 14, No. 1

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