Groundwater for Sustainable Development最新文献

{"title":"Strategic imputation of groundwater data using machine learning: Insights from diverse aquifers in the Chao-Phraya River Basin","authors":"Yaggesh Kumar Sharma ,&nbsp;Seokhyeon Kim ,&nbsp;Amir Saman Tayerani Charmchi ,&nbsp;Doosun Kang ,&nbsp;Okke Batelaan","doi":"10.1016/j.gsd.2024.101394","DOIUrl":"10.1016/j.gsd.2024.101394","url":null,"abstract":"<div><div>Effective groundwater monitoring is essential for sustainable water management, particularly in data-sparse regions. To address inconsistencies in groundwater level data, we developed a machine learning framework for robust data imputation, tested in the Chao-Phraya River (CPR) Basin, a region facing significant groundwater challenges due to high population density and ecological importance. Our study evaluated five models—K-Nearest Neighbors (KNN), Multiple Imputation by Chained Equations (MICE), Multilayer Perceptron (MLP), Random Forest (RF), and Soft Imputation (SI) —to fill gaps in monthly groundwater level data across various locations, aquifer depths, and data loss scenarios. Results show that MICE perform well in high-density well environments, while SI excels with lower well density, maintaining Pearson correlation coefficients (R) above 0.80 and RMSE values below 6 even at 10% data loss. The Coefficient of Variation (COV) analysis also confirmed that imputed data remains stable and reliable. However, the study also reveals a significant decrease in model performance in regions with fewer wells, as indicated by increased RMSE and reduced R. Our findings indicate that machine learning models are capable of handling groundwater level observations with missing data. The well density in a region has a significant impact on these model's performance. Imputation techniques should be tailored to each aquifer's specific characteristics and surroundings in order to get accurate groundwater data.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101394"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"“Hydrogeological characterization and seawater intrusion inference in the coastal aquifer, using groundwater chemistry and remote sensing data.”","authors":"Samah M. Morsy,&nbsp;Shaimaa M. El-Hadidy","doi":"10.1016/j.gsd.2024.101399","DOIUrl":"10.1016/j.gsd.2024.101399","url":null,"abstract":"<div><div>In coastal lands groundwater is a vital water resource for supporting ecosystems. However, seawater intrusion is a dramatic global environmental problem threatening sustainable development in these lands. Thus, the coastal aquifers are vulnerable to seawater rise and increased groundwater salinity causing a set of nested groundwater management problems. Therefore, identifying the seawater front's dynamic progression and delineating the aquifer zones of optimized exploitation have significant importance in adapting to the negative effects of saltwater intrusion and ensuring sustainable development. In the present research, the approach involves an integration of remote sensing data, groundwater hydrochemical data, aquifer hydraulic parameters and subsurface well logs to clarify the hydrogeological factors that motivate the saltwater intrusion, and the geochemical evolution of groundwater that determine the seawater-freshwater limit interface in the Lower Miocene Moghra coastal aquifer, northwestern desert of Egypt. The research findings reported that: (1) The Moghra aquifer is characterized by a low to moderate relief and mild topography range from 0 to 90 m with low drainage density (&gt;2) and high lineament density of west and northwards throws in the middle zone, regulating the aquifer transmissivity values to increase in this zone (4000–6000 m²/day). (2) NE-SW, N-S and NW-SE subsurface faults enhance the hydraulic connection and recharge from the underlying aquifers. (3) The Normalized Difference Salinity Index shows a gradual increase with time from the index value of 0.2 in 1984 reaching the maximum value of unity in 2021 assuring the progression of saltwater intrusion (4) The hydrochemical investigation revealed significant evidence of saltwater infringing, and fresh to the brackish water of TDS 1000–3000 mg/l at the middle zone of the aquifer due to mixing with the water of the underlying aquifers (5) hydrochemical priority map for five quality zones to assess groundwater continuity and address sea level rise's impact on sustainable development projects.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101399"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategies for water salinity management in alluvial aquifer in a multilevel crop planning","authors":"Tayron Juliano Souza ,&nbsp;Vera Lúcia Antunes de Lima ,&nbsp;Carlos de Oliveira Galvão ,&nbsp;Nildo da Silva Dias ,&nbsp;Bárbara Barbosa Tsuyuguchi","doi":"10.1016/j.gsd.2024.101392","DOIUrl":"10.1016/j.gsd.2024.101392","url":null,"abstract":"<div><div>This article analyses the behaviour of groundwater salinity and its suitability for irrigation purposes and proposes multilevel crop planning to increase agricultural benefits based on water salinity management strategies. The study area is a 12-km-long portion of an alluvial aquifer, where the irrigated perimeter of Sumé is located, in the municipality of Sumé, in the State of Paraíba, Brazil. Three wells were selected, located in the middle portion and at the ends of the aquifer. Based on the physical-chemical data collected, the classification of groundwater for agricultural use was carried out. Then, a multilevel planning model based on water salinity management is proposed, considering the geographic scale of the aquifer, different decision-making levels (lot and of irrigated perimeter), in addition to the involvement of different actors, including organizations and small farmers, at different decision levels. The results indicated that the waters in the initial portion of the aquifer have the highest levels of salinity and that the waters in the final reach have better quality, possibly due to the shorter and longer distances from the sources of contamination, respectively. The possibility of increasing the availability of water in the aquifer and the crop yield was indicated, in addition to other benefits related to the qualification of farmers and the commercialization of products, among others. In this sense, multilevel planning is suggested as a tool that can generate benefits for farmers beyond crop yields, including social and economic aspects.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101392"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fluoride and nitrate contamination in groundwater of Naini Industrial Area, Uttar Pradesh: Assessing non-carcinogenic human health risk","authors":"Nighat Parveen ,&nbsp;Soma Giri ,&nbsp;Abhay Kumar Singh ,&nbsp;Jayant Kumar Tripathi","doi":"10.1016/j.gsd.2024.101388","DOIUrl":"10.1016/j.gsd.2024.101388","url":null,"abstract":"<div><div>Groundwater is the main source of drinking water globally; however, its quality has been deteriorated due to various geogenic and anthropogenic activities. The groundwater quality of Naini Industrial Area, Prayagraj was studied seasonally to evaluate the fluoride and nitrate contamination pertaining to human health risk assessment. The samples were collected from 60 locations in the pre-monsoon, monsoon, and post-monsoon season. The fluoride and nitrate were assessed with the help of Ion chromatography. The NO₃⁻ concentration exceeded the Indian drinking water quality standards in 27% of the groundwater samples. The NO₃⁻ contamination is predominantly associated with agricultural practices, while F⁻ can be linked to natural geological sources. The non-carcinogenic human health risk assessment was quantified by calculating the Hazard Quotient (HQ) and Hazard Index (HI) were calculated as per USEPA methodology for male, female and child population. The findings indicate that the child population is particularly susceptible to health risks associated with the ingestion of F⁻ and NO₃⁻ through the drinking water pathway. Across all the sampled sites, the Hazard Index (HI) values varied from 0.10 to 12.3 for males, 0.09 to 10.6 for females, and 0.16 to 19.7 for children suggesting substantial risk to the local populace at more than half of the locations which is largely related to nitrate contamination. Thus, the study suggests that groundwater at many locations is unsuitable for drinking without treatment pertaining to the probable health risk they pose to consumers advocating upgraded water management plan for the residents.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101388"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater level prediction using modified recurrent neural network combined with meta-heuristic optimization algorithm","authors":"Eui Hoon Lee","doi":"10.1016/j.gsd.2024.101398","DOIUrl":"10.1016/j.gsd.2024.101398","url":null,"abstract":"<div><div>Groundwater is an important resource for water supply; it fluctuates depending on various factors, and the prediction of groundwater level is very important for water resources. Among various models for predicting groundwater levels, deep learning models have been applied to various water resources fields. Recurrent neural network (RNN) is a deep learning model for sequential data, and optimizers of RNN are important operators for calculating weights. However, existing optimizers of RNN have disadvantages such as convergence of local optimum and absence of weights storage. To improve RNN, new optimizers that combine existing optimizers with a meta-heuristic optimization algorithm were applied to a modified recurrent neural network (MRNN). To verify the accuracy of the MRNN, the groundwater level in Icheon was predicted and compared with the prediction results of RNN. The average temperature, daily precipitation, relative humidity, duration of sunshine, ground temperature, water level of nearby stream, and soil wetness were used as input data for the groundwater level prediction. Correlation analysis and normalization were applied as data preprocessing methods. The accuracy of each model was compared according to the value of mean square error (MSE). Prediction accuracy of MRNN was improved by an average of 43.35 % compared to RNN.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101398"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel predictive framework for water quality assessment based on socio-economic indicators and water leaving reflectance","authors":"Hao Chen ,&nbsp;Ali P. Yunus","doi":"10.1016/j.gsd.2025.101405","DOIUrl":"10.1016/j.gsd.2025.101405","url":null,"abstract":"<div><div>Accurate prediction of water quality at both spatial and temporal scales for large water bodies remains a daunting task with significant implications for human well-being and sustainable development (aligned with SDG 6 - clean water and sanitation). Traditional data-driven models on water quality prediction relied on some degree of field subsistence, which are neither cost-effective nor time-efficient. Socio-economic indicators have been concurrently used as predictor variable for water quality; however, such datasets typically available at coarse temporal resolutions, limiting their applicability for time-sensitive analyses. In this study, we integrated machine learning (ML) models with socio-economic indicators and remote sensing reflectance (R_RS) to address the challenge of temporality in predicting Biochemical Oxygen Demand (BOD) and Total Coliform Bacteria (TCB) levels across 228 lake systems in the Indian subcontinent. Pearson correlation analysis revealed limited direct correlations (&lt;0.5) between BOD, TCB, and the input variables. However, a stepwise omission and commission analysis demonstrated that incorporating R_RS into the socio-economic model significantly enhanced predictive performance of the ML models. This approach achieved high classification accuracy for BOD and TCB, with Area Under the Curve (AUC) scores of 0.84 and 0.96, respectively, highlighting strong potential for temporal water quality assessment. Among the supervised learning methods tested, the random forest model outperformed all others in terms of accuracy and robustness. This study presents an integrated framework capable of predicting BOD and TCB with both high temporal and spatial resolution, and offers valuable insights for the effective and efficient management of aquatic ecosystems, enabling timely interventions and informed decision-making.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101405"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater quality and its impact due to hydraulic fracturing activities around oil and gas drilling sites: A comprehensive study on distribution, correlation, ecological and health risk assessment of heavy metals","authors":"Babu Mallesh Dasari ,&nbsp;Keshav Krishna Aradhi ,&nbsp;Dasaram Banothu","doi":"10.1016/j.gsd.2024.101395","DOIUrl":"10.1016/j.gsd.2024.101395","url":null,"abstract":"<div><div>Given the propensity of oilfield drilling activities to induce groundwater pollution, particularly in shallow aquifers, a critical evaluation of contamination risk becomes imperative for effective groundwater management and conservation. The distribution of twenty-two physicochemical parameters including heavy metal contamination in water is assessed using heavy metal pollution index (HPI), metal index (MI), and water quality index (WQI), revealing a high level of contamination. HPI values for the PRM season range from 63.3 to 4335.4 (mean: 1166.7) and for the POM season from 5.2 to 47.3 (mean: 23.3). The MI values during the PRM season ranged from 1.1 to 75.7 (mean: 10.1), while POM values ranged from 0.5 to 4.4 (mean: 1.1). The WQI for PRM ranged from 21.4 to 1093.7 (mean: 184.9) and from 18.1 to 614.2 (mean: 82.4) during the POM period. Irrigation quality indices determine groundwater suitability of groundwater for agricultural purposes. Employing multivariate statistical approaches, this study delineates the contributions of both natural and anthropogenic activities to alterations in groundwater hydrochemistry. Hazard Index (HI) values exceeded the USEPA's safe limits in 99% of PRM samples for children and 100% for adults, while 27.3% of POM samples for children and all POM samples for adults also surpassed safe levels. Carcinogenic Risk (CR) assessment indicated arsenic, chromium, mercury, nickel, and lead concentrations exceeding the USEPA's threshold of 1.0 x 10⁻⁶, suggesting significant carcinogenic risks for both adults and children. The study uses Monte-Carlo simulation to examine human health risk assessment parameters, and advocates for strategic planning, water resource management, and treatment schemes to mitigate identified health risks and towards providing safe drinking water.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101395"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region","authors":"Zenhom El-Said Salem ,&nbsp;Nesma A. Arafa ,&nbsp;Abdelaziz L. Abdeldayem ,&nbsp;Youssef M. Youssef","doi":"10.1016/j.gsd.2024.101403","DOIUrl":"10.1016/j.gsd.2024.101403","url":null,"abstract":"<div><div>Mega-delta aquifers face increasing salinization risks from overexploitation and erratic climate change globally. This study integrates the GALDIT framework with machine learning (ML) models, namely Support Vector Machine (SVM), Generalized Linear Model (GLM), and eXtreme Gradient Boosting (XGBoost), to enhance delta aquifer vulnerability (DAV) assessment to seawater intrusion (SWI). The Nile Delta, the largest freshwater mega-delta aquifer, serves as a case study. Grid search hyperparameter optimization was applied to refine these models using the GALDIT factors (groundwater occurrence, aquifer hydraulic conductivity, groundwater height above sea level, distance from the shoreline, impact of existing seawater intrusion, and aquifer thickness) and adjust conditioned vulnerability index (CVI) based on Total Dissolved Salts (TDS) as input variables. Statistical metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Coefficient of Determination (R²), Pearson Correlation Coefficient (r), Nash–Sutcliffe Efficiency (NSE), Root Mean Square Error to Standard Deviation of Observations (RSR), and Index of Scatter (IOS), show that the XGBoost model significantly outperforms SVM and GLM, with exceptional results: R² = 0.9622, RMSE = 0.0430, r = 0.9815, MAE = 0.0206, MSE = 0.0018, NSE = 0.9618, RSR = 0.0005, and IOS = 0.2935. The GALDIT_XGBoost map identified previously undetected high-vulnerability areas west of Alexandria and localized pockets within southern Port Said along the Mediterranean coast. The moderate vulnerability zone expanded, especially in northern Ismailia, compared to the basic GALDIT. Piper diagrams confirmed SWI risks, with dominant Na-Cl and Ca-Mg-Cl facies indicating elevated Cl⁻ and SO₄²⁻ levels. A shift from HCO₃⁻ to Cl⁻ further validated salinization, while Ca-HCO₃ facies represented freshwater. The optimized XGBoost model offers a robust tool for managing mega-delta groundwater and assessing global delta vulnerabilities.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101403"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining factors and strategy in sustainable fecal sludge management services","authors":"Nadia Paramita ,&nbsp;Rachmadhi Purwana ,&nbsp;Djoko Mulyo Hartono ,&nbsp;Tri Edhi Budhi Soesilo","doi":"10.1016/j.gsd.2024.101390","DOIUrl":"10.1016/j.gsd.2024.101390","url":null,"abstract":"<div><div>Currently, 65% of the total residents of Jakarta rely on groundwater as their primary water source for daily life. Groundwater quality is critical, with the presence of <em>Escherichia coli</em> bacteria throughout Jakarta significantly exceeding the limit. The solution to preventing groundwater pollution from fecal waste is through domestic wastewater management. On-site treatment is a solution to accelerate service achievement in Jakarta, but it has yet to be known which priority factors affect the sustainability of its services. This study aimed to determine the community's understanding of and interest in regular desludging services, the priority weights of sustainability factors for desludging services in Jakarta Province, and alternative sustainability strategies. The research and sampling in this study were conducted in Jakarta Province. Random sampling was conducted on 410 people. A hierarchical process analysis was conducted with 13 stakeholder respondents to determine the weight of the sustainability factors and the strategy to achieve sustainability of fecal sludge services. This study showed that 34.5% of Jakarta residents still rely on groundwater to meet their clean water needs through private and public wells. According to the regulations, 83% of people use septic tanks, but only 22% use desludging. To achieve sustainability of the fecal sludge service in Jakarta Province, the Leadership Factor has the highest priority, with a weight of 39%. The lowest priority was indicated by the technology factor, with a weight of 4.8%. An alternative strategy to achieve sustainability showed the highest priority weight of 82.6% for regular desludging services compared with on-call desludging. Regulations and sanctions support regular desludging. The role of leaders, both regional leaders and institutions, in committing to achieve service targets in a region is very important. Regular desludging services are recommended to ensure the sustainability of fecal sludge services in Jakarta Province.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101390"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial and temporal distribution of arsenic in groundwater of the Brahmaputra River floodplains in Assam, India","authors":"Smitakshi Medhi,&nbsp;Runti Choudhury","doi":"10.1016/j.gsd.2024.101400","DOIUrl":"10.1016/j.gsd.2024.101400","url":null,"abstract":"<div><div>The present study focuses on spatial and seasonal distribution of arsenic (As) along with the solute chemistry and hydrochemical evolution of groundwater in the southern bank of Brahmaputra floodplains in Assam, India. A total of 100 groundwater samples were collected from shallow aquifers (&lt;30m) that are distributed spatially covering the entire study area during the pre-monsoon (April) and post monsoon (Nov) season in the year 2022.The samples were than analyzed for different physico chemical parameters viz; pH, EC, TDS, Ca²⁺, Na⁺, K⁺, Mg^2+, Cl⁻, HCO₃⁻, NO₃⁻, SO₄²⁻, Fe, Mn and As to interpret the hydrochemistry and groundwater evolution in the study area. Broadly three zones were delineated based on As distribution in the region viz; zone 1 as high As zone, areas adjacent to the foothills of Naga hills,(ranged from below detection level (bdl) to 531 μg/l, mean:93.91 μg/l). Zone 2 is demarcated as low arsenic zone, near the Brahmaputra River, where As concentration was mostly &lt;10 μg/l. Zone 3, lying between the flanks of Mikir Hills and Naga Hills is demarcated as intermediate zone where As concentration ranged from bdl to 50 μg/l. Piper plot indicates Na-HCO₃ as a primary water type during pre-monsoon, while Ca-Mg-HCO₃ type during post monsoon.Groundwater is undersaturated with respect to As phases such as Arsenolite and As₂O₅ specifying that As is in dissolved form in the groundwater. The groundwater is supersaturated with calcite (CaCO₃) and Dolomite (MgCa(CO₃)₂and Fe(III) (Oxyhyroxide). The stable isotopes (δ¹⁸O and δ²H) of groundwater suggest that precipitation is primarily recharging the groundwater with some influence of evaporation. The results of the study will contribute to a deeper understanding of the arsenic distribution dynamics in the Brahmaputra Floodplains along with facilitating evidence-based decision making aimed at providing arsenic safe drinking water to the affected communities.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101400"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}