Environmental Earth Sciences最新文献

{"title":"Landslide risk using Geospatial techniques and machine learning: Shimla district of Himachal pradesh, India","authors":"Aastha Sharma,&nbsp;Haroon Sajjad,&nbsp;Md Hibjur Rahaman,&nbsp;Tamal Kanti Saha,&nbsp;Nirsobha Bhuyan,&nbsp;Md Masroor,&nbsp;Daawar Bashir Ganaie","doi":"10.1007/s12665-025-12522-8","DOIUrl":"10.1007/s12665-025-12522-8","url":null,"abstract":"<div><p>The frequency of landslide occurrences has increased due to climate change and human-induced alterations in Shimla district of Himachal Pradesh in India. Occurrence of landslides has posed great risk to ecological, physical and social systems. Thus, assessing landslide risk is crucial for devising adaptation and mitigation strategies. This study makes concerted efforts to integrate hazard, vulnerability and element-at-risk for landslide risk assessment. The effectiveness of the multilayer perceptron model was assessed using key performance metrics. The validation of the map was carried out using confusion metrics. Landslide risk analysis revealed that most of the area falls in the very high risk followed by high, low and moderate risk. Mashobra, Basantpur, Narkanda and Rampur blocks (administrative divisions) experienced very high landslide risk. Rainfall, slope, wetness index, building density and extensive road network have been attributed to very high landslide risk. Theog, Chaupal and Rohru blocks experienced high landslide risk due to high temperature variability, high population density and low literacy rate. Stability measures, effective land use and instrument installation are suggested for enhancing adaptive capacity among communities. Thus, the comprehensive framework applied in this study may be used across other geographical regions to classify risk zones and recommend effective mitigation measures.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landslide susceptibility assessment based on fuzzy set theory: Xiaowan reservoir–Lancang river","authors":"Hong Wenyu,&nbsp;Xi Wenfei,&nbsp;Yang Zhiquan,&nbsp;Gu Shixiang,&nbsp;Huang Guangcai,&nbsp;Jin Tingting,&nbsp;Zhuang Yongzai,&nbsp;Bai Shihan,&nbsp;Ma Yijie","doi":"10.1007/s12665-025-12505-9","DOIUrl":"10.1007/s12665-025-12505-9","url":null,"abstract":"<div><p>Due to the influence of complex geological structures and reservoir operations, geological disasters frequently occur in reservoir bank areas. Conducting susceptibility assessments in these areas is essential for ensuring the safe and stable operation of reservoirs.In susceptibility assessments of mountainous regions, traditional models often neglect the uncertainty inherent in dynamic environmental factors. The Interval Intuitionistic Fuzzy Set (IIFS) model, by introducing elastic interval representations, offers a more flexible means of characterizing the spatiotemporal variability and evolutionary patterns of such dynamic factors, thereby enhancing model adaptability and prediction accuracy. In this study, ascending and descending Sentinel-1 SAR data from September 2021 to September 2023 were utilized to derive ground surface deformation using time-series InSAR analysis. Key influencing factors of reservoir bank landslides in the Xiaowan Reservoir–Lancang River section—including topography, climate conditions, and geological characteristics—were incorporated into the IIFS model to conduct a comprehensive landslide susceptibility assessment. The results show that: (1) The IIFS-based model demonstrated superior performance in landslide susceptibility evaluation, achieving a ROC-AUC of 0.902, outperforming the BPNN (0.864), Random Forest (0.790), and Information Value model (0.680). Additionally, the IIFS model achieved an F1-score of 0.85, precision of 0.82, and recall of 0.88, indicating strong classification performance and balance. (2) High-susceptibility zones were primarily concentrated on the left bank of the upstream section of the Xiaowan Reservoir–Lancang River, with the extremely high susceptibility area accounting for 13.28% of the total, encompassing 21 historical landslide points. The landslide density in this zone was approximately 32% higher than that predicted by the BPNN model. (3) Sensitivity analysis with ± 5% perturbations applied to key input factors—such as DEM, annual rainfall, and InSAR deformation velocity—showed AUC fluctuations within 0.02. This indicates that the model maintains strong robustness and generalization capability when facing uncertainties in input data. Overall, the IIFS model effectively captures the uncertainty of environmental factors, enhances the prediction accuracy and spatial focus of reservoir bank landslide susceptibility, and provides scientific and practical support for geological hazard risk management.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Flood risk assessment under land use dynamics during 2000–2020 in the yellow river basin, China","authors":"Tongqing Liu,&nbsp;Shuxia Sun,&nbsp;Naixian Wang,&nbsp;Renqing Wang,&nbsp;Peiming Zheng,&nbsp;Hui Wang","doi":"10.1007/s12665-025-12526-4","DOIUrl":"10.1007/s12665-025-12526-4","url":null,"abstract":"<div><p>Floods are one of the most devastating natural disaster events globally and are strongly influenced by land use changes. This study crafted a holistic “hazard-sensitivity-vulnerability-restorability” flood risk assessment framework, integrating data from land use, natural environment, and socioeconomic factors. Using the Yellow River Basin (YRB) in China as an example from 2000 to 2020, it analyzed the spatiotemporal evolution of land use structure and flood risk, revealing their cluster pattern based on quantitative land use structure indices and flood risk index. Key findings include: (1) A gradient pattern of increasing flood risk from northwest to southeast in the YRB, with an overall trend of significant decrease followed by rebound from 2000 to 2020. (2) By 2020, land use homogenization occurred in middle-basin deserts due to grassland restoration, contrasting with down-basin fragmentation driven by built-up land expansion. (3) Global spatial correlation showed flood risk positively linked to cropland, built-up land and forest significantly but negatively associated with grassland significantly, highlighting land use trade-offs in flood mitigation. (4) Local spatial correlation revealed a “cold west (low risk - low structure index) vs. hot east (high risk – high structure index) \" differentiation, emphasizing zoning-based management needs. The study provides actionable insights for balancing flood resilience and land resource sustainability in the YRB. The proposed framework offers a transferable methodology for large river basins globally, particularly in regions facing coupled pressures of climate change and rapid urbanization.</p><h3>Graphical abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response mechanism of permeability of remolded loess to AlCl3 concentration: a new discovery","authors":"Qiming Wang,&nbsp;Panpan Xu,&nbsp;Hui Qian","doi":"10.1007/s12665-025-12543-3","DOIUrl":"10.1007/s12665-025-12543-3","url":null,"abstract":"<div><p>The unique chemical properties and hydration behavior of aluminum, combined with the abundance of silicate minerals in loess, render the seepage mechanism of aluminum solutions in loess highly complex. To explore the response mechanism of the permeability of remolded loess to AlCl₃ solutions of varying concentrations, a systematic study was conducted involving permeability tests, Zeta potential measurements, water-soil interaction analyses, and SEM observations. Results showed that the saturated hydraulic conductivity (<i>K</i>_<i>sat</i>) increased slightly (by 7.6%) under deionized water (DW) seepage due to weak water-rock interactions and pore expansion. Compared to DW, <i>K</i>_<i>sat</i> increased notably under seepage of 0.001–0.005 mol/L AlCl₃ solutions, as Al³⁺ hydrolysis facilitated the dissolution of minerals and the compression of the diffuse double layer, improving pore connectivity. However, as the concentration of Al³⁺ increased, the amount of Al(OH)₃ colloids generated rose, which slightly reduced pore space and caused a weak downward trend of final <i>K</i>_<i>sat</i>. Under seepage of a 0.01 mol/L AlCl₃ solution, the high concentration of Al³⁺ intensified hydrolysis, initially expanding pore spaces. However, as seepage progressed, the aggregation of Al(OH)₃ colloids produced due to hydrolysis caused significant pore blockage, resulting in an initial increase followed by a decrease in <i>K</i>_<i>sat</i>. Under seepage of a 0.1 mol/L AlCl₃ solution, the large amount of Al(OH)₃ colloids formed due to intense hydrolysis almost completely blocked intergranular pore spaces, Limiting seepage to just 0.5 days. These findings provide theoretical insights to support engineering applications in loess regions.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Saturation effect on the Geological Strength Index (GSI) for rock mass characterization","authors":"Hasan Karakul","doi":"10.1007/s12665-025-12525-5","DOIUrl":"10.1007/s12665-025-12525-5","url":null,"abstract":"<div><p>As a rock mass characterization system, Geological Strength Index (GSI) system is widely used in different rock engineering applications due to its use in the Hoek Brown failure criterion. However, the saturation condition, which has an effect on geomechanical properties of both rock material and discontinuities, has not been considered in the GSI system. The main purpose of this study is to examine the saturation effect in the GSI system. In order to take the saturation effect into account within the GSI system, a correction factor was proposed using the Barton failure criterion in this study. Multivariate regression analysis was also carried out to predict the correction factor for saturated conditions. The proposed equation derived by multivariate regression analysis was found statistically reliable. A modification was suggested for the process of determining the GSI value using the correction factor. Hoek Brown failure envelopes were drawn using the corrected and uncorrected GSI values to examine the effect discontinuity saturation on rock mass strength. In order to check the extent of saturation effect on rock engineering applications, stability of a rock mass slope was also examined. The results of slope stability analyses showed that the factor of safety value will be considerable higher than the actual value without using proposed corrected GSI value.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 18","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Streamflow estimation for underground dams using machine learning and hydrological modeling: a case study of Bartın Bahçecik underground dam","authors":"Tülay Ekemen Keskin,&nbsp;Emrah Şander","doi":"10.1007/s12665-025-12511-x","DOIUrl":"10.1007/s12665-025-12511-x","url":null,"abstract":"<div><p>Rapid technological advances, agricultural expansion, and population growth ratio have accelerated the depletion of limited water resources, leading many countries, including Turkey, to emphasize the construction and use of underground dams as an effective strategy for sustainable water management. In order to contribute to the sustainability of underground dams, this study takes the Bahçecik (Bartın) Underground Dam as a case study, aiming to estimate the streamflow data required for the artificial recharge of underground reservoirs using surfacewater through wells. In this context, the streamflow of the main tributary recharging the dam was estimated by jointly evaluating machine learning techniques and hydrological basin modeling results. Time Series Analysis, Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), and the similar basin area ratio methods used at the study. Time Series Analysis yielded Mean Absolute Percentage Error (MAPE) values ranging from 0.086 to 13.969%. The ANN method demonstrated superior performance in flow estimation at the E13A031 gauging station, achieving a coefficient of determination (𝑅²) of 0.802, while an 𝑅² value of 0.88 was obtained for the 2018 flow estimation of the Ovacuma Stream. These results underscore the effectiveness of integrating hydrological investigations with machine learning approaches in supporting sustainable water resource management.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 17","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI using ensemble machine learning with integrated SHapley additive explanations (SHAP)-Borda approach for estimation of the safety factor against soil liquefaction","authors":"Uğur Dağdeviren,&nbsp;Alparslan Serhat Demir,&nbsp;Caner Erden,&nbsp;Abdullah Hulusi Kökçam,&nbsp;Talas Fikret Kurnaz","doi":"10.1007/s12665-025-12466-z","DOIUrl":"10.1007/s12665-025-12466-z","url":null,"abstract":"<div><p>In most of the studies on soil liquefaction prediction based on Machine Learning (ML), the models presented are presented in a closed box structure. In the studies where the effect of the features on the model performance is analyzed with Interpretability methods, it is seen that the order of effect of the features changes for each ML algorithm. This situation makes the results of the studies conducted on the same subject inconsistent. In this study, we propose an integrated SHapley Additive exPlanations (SHAP)-Borda approach to overcome this problem. With this study, we provide decision makers with ease in explaining ML models by combining SHAP analysis results with the Borda method for the first time. In the study, ensemble ML algorithms were used for soil liquefaction prediction using data collected from the literature. The performances of the model predictions obtained by hyper parameterization were compared, and correlation results ranging from 0.91 to 0.93 were obtained. Ensemble ML algorithms that were found to be successful as a result of evaluating other performance criteria were analyzed with the SHAP-Borda approach in the study. It has been observed that with the proposed SHAP-Borda approach, the interpretability results of different ML algorithms can be brought together, and a final result can be presented, providing ease of evaluation for decision makers. The study also shows that (N₁)₆₀ and a_max are the most effective features in predicting soil liquefaction.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 17","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harvesting nature’s bounty: leveraging flood water diversion for sustainable agriculture and pisciculture in wetlands","authors":"Asish Saha,&nbsp;Shanbor Kurbah,&nbsp;Pradip Kumar Bora,&nbsp;Ranjit Das,&nbsp;Bajitborlang L. Chyne,&nbsp;Diganta Barman","doi":"10.1007/s12665-025-12512-w","DOIUrl":"10.1007/s12665-025-12512-w","url":null,"abstract":"<div><p>Flood-prone regions in the Himalayan foothills, such as Assam’s Lakhimpur district, India frequently experience monsoonal inundation, sedimentation, and dry-season water scarcity. This study presents the first of its kind nature-based floodwater management framework by utilizing seven identified wetlands for floodwater diversion, storage, and multipurpose reuse. Wetland selection was carried out using Sentinel-2 imagery; applying criteria such as ≥ 10 ha surface area, ≤ 3.5 km proximity to rivers, and location within moderate or lower flood hazard zones. Wetland storage capacities were estimated using 1 m LiDAR-derived DEM, incorporating enhancement through 2 m excavation and 2.55 m-high embankments (top width: 3.51 m; base width: 8.65 m). Hydrological modeling using HEC-HMS demonstrated high accuracy (NSE: 0.859 to 0.891), simulating peak discharges of 1012 m³/s (Ranganadi) and 120.4 m³/s (Singra). Least-Cost Path analysis was used to identify gravity-driven diversion routes, enabling the design of unlined canals (depth: 2 m; base width: 11 m), with estimated construction costs between ₹1.49–2.49 Crores. Sediment load assessment, based on CWC guidelines, revealed high sedimentation in the Ranganadi (11,085 tons/day) and minimal in Singra (176 tons/day), with site-specific check dams (10 for Ranganadi and 6 for Singra catchment) proposed for mitigation. The potential of using stored floodwater during lean seasons was evaluated through CROPWAT 2.0, showing irrigation feasibility for up to 1,980 ha of land with crops such as potato, maize, and cabbage for both catchments, and support cage pisciculture, generating over ₹34,000 per cage/year. This integrated approach offers a scalable, cost-effective model for flood mitigation, water reuse, and rural livelihood enhancement in sediment-rich Himalayan catchments.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 17","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response of sediment microbial communities to antibiotic resistance genes in an irrigation–drainage system in an integrated family farm","authors":"Ming Xu,&nbsp;Yuan Gao,&nbsp;Rui Ding,&nbsp;Yun-xiang Zhu,&nbsp;Zi-wu Fan,&nbsp;Xiao-xiao Shen","doi":"10.1007/s12665-025-12446-3","DOIUrl":"10.1007/s12665-025-12446-3","url":null,"abstract":"<div><p>Because of the extensive use of antibiotics, antibiotic resistant microorganisms are gradually becoming a threat to human health. The spatiotemporal distribution of antibiotic resistance genes in the irrigation–drainage system of integrated family farms should be further studied that is conducive to further research on the control methods of antibiotic resistance genes. Among the detected genes, <i>tetA</i>, <i>tetX</i>, <i>sul1</i>, <i>sul2</i>, <i>qnrA</i>, <i>ermC</i>, <i>intI1</i>, and <i>sul1</i> had the highest absolute abundance (1.153 × 10⁶ lecopies/g), suggesting the universality of antibiotic resistance gene transmission. Redundancy analysis reveals <i>Sphingomonas</i> and <i>Acinetobacter</i> were the dominant antibiotic resistance gene host bacteria in irrigation–drainage systems. The structural equation revealed that bacterial communities and environmental factors significantly contributed to the antibiotic resistance gene community structure. Furthermore, the normalized stochasticity ratio revealed that planting activities affect and decisively dominate sediment bacterial communities. The structural equation model showed that environmental variables were the key driving factors for the difference in ARGs distribution in different land use types (path coefficient = 0.61, R² = 0.72, <i>p</i> &lt; 0.01), which was significantly higher than <i>intI1</i> and heavy metal residues. Importantly, these physical and chemical factors indirectly affect the distribution of ARGs by changing the relationship between microorganisms to regulate the succession of microbial communities. Strategies for controlling antibiotic resistance gene pollution by regulating the total nitrogen and total phosphorus of bacterial community structures in integrated family farms may be proposed by focusing on community changes in irrigation–drainage systems.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 17","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater quality estimation for drinking and irrigation suitability in a drought-prone region of south India with health hazard computation and spatial analysis using GIS","authors":"D. Karunanidhi,&nbsp;P. Aravinthasamy,&nbsp;H. Chandra Jayasena,&nbsp;T. Subramani,&nbsp;Narsimha Adimalla","doi":"10.1007/s12665-025-12482-z","DOIUrl":"10.1007/s12665-025-12482-z","url":null,"abstract":"<div><p>Groundwater is essential in the semi-arid areas of South India, where unpredictable rainfall and limited surface water, increase reliance on underground water for drinking and farming. In the industrially active Sivakasi area, worries about groundwater pollution have grown due to rising human activities, such as fertilizer application and industrial wastes. This research seeks to assess the quality of groundwater for drinking and irrigation, concentrating on spatial differences, contamination risks, and health effects, by employing combined hydrogeochemical and geospatial methods. A total of 77 groundwater samples were analyzed for physicochemical parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-), and key contaminants (fluoride and nitrate). The Piper diagram revealed that 70% of samples belong to the mixed Ca–Mg–Cl water type. Fluoride levels exceeded World Health Organization (World Health Organization-WHO 2017) limits in 24% of the samples, rendering 140.16 km² unsuitable for drinking, while nitrate levels above 45 mg/l affected 36.4% of samples, impacting 250.93 km². A Water Quality Index (WQI) map classified 140.49 km² as having very poor water quality, and 422.29 km² as not suitable for human consumption. Conversely, irrigation assessments using USSL, Wilcox, and Doneen diagrams, along with Sodium adsorption ratio (SAR), Residual sodium carbonate (RSC), Kellys Index (KI), Sodium Percentage (Na%), and Magnesium Hazard Ration (MHR) indices, indicated that most groundwater sources were suitable for agricultural use. However, health risk assessments revealed significant non-carcinogenic risks, especially among infants, due to excessive fluoride and nitrate exposure. GIS-based mapping aids in groundwater management, ensuring drinking water safety and irrigation suitability.</p></div>","PeriodicalId":542,"journal":{"name":"Environmental Earth Sciences","volume":"84 17","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144909660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}