Groundwater for Sustainable Development最新文献

{"title":"Enhancing bioremediation of diesel/biodiesel blend (B20) impacted sites using in situ bioreactors: A nature-based solution for sustainable groundwater management","authors":"Marcio Roberto Schneider ,&nbsp;Ana Claudia Canalli Bortolassi ,&nbsp;Adriana Ururahy Soriano ,&nbsp;Marcus Paulus Martins Baessa ,&nbsp;Luiz Fernando Martins ,&nbsp;Rodrigo de Almeida Heringer ,&nbsp;Admir José Giachini","doi":"10.1016/j.gsd.2024.101387","DOIUrl":"10.1016/j.gsd.2024.101387","url":null,"abstract":"<div><div>The rise of biodiesel content in diesel/biodiesel blends as a viable alternative to traditional diesel fuel presents several advantages, particularly in terms of reduced emissions of pollutants like carbon monoxide (CO), hydrocarbons (HC), and smoke. However, the increased production and utilization of biodiesel raise concerns about potential environmental impacts, such as groundwater pollution. This has led to a growing need for sustainable and effective bioremediation techniques to address these issues and ensure the safe closure of contaminated sites in accordance with environmental regulatory criteria. Two experimental areas contaminated with B20 (80% diesel and 20% biodiesel v/v) to evaluate ammonium acetate biostimulation (B20_BAA) and natural source zone depletion (B20_NSZD) as remediation strategies were monitored for 10 and 12 years, respectively. Although the benzene half-life was 1.49 and 4.08 years, respectively, hydrocarbon concentrations remained above the maximum contaminant level (MCL) allowed in Brazil for groundwater, requiring additional technologies for site cleanup. Then, two pilot-scale airlift bioreactors were employed as nature-based solutions (NbS) to reduce the concentration of persistent contaminants for site closure. Indeed, concentrations of benzene and 2-methylnaphthalene decreased significantly after the bioreactors began operation, reaching values below their respective MCL. 16S rRNA gene sequencing showed a beneficial response of microbial communities composed of <em>Massilia</em>, <em>Burkholderia-Caballeronia-Paraburkholderia</em>, <em>Mycobacterium</em> and <em>Bacillus</em> genus involved in hydrocarbons aerobic biodegradation. Moreover, predicted functional genes analysis demonstrated that the relative abundances of key aerobic degradation pathways for benzene and 2-methylnaphthalene increased, supporting the hypothesis that the NbS stimulated the hydrocarbons biodegradation. These findings demonstrated that combining different nature-based solutions (NbS) can effectively remediate petroleum hydrocarbons in contaminated groundwater through geochemical characteristics and exploration of indigenous microorganisms. To the best of the authors’ knowledge, this is the first study to employ bioreactors to treat B20-contaminated groundwater at a field scale.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101387"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133047","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":"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":"The groundwater age of the Argentine aquifers: A review and paleoclimate insight","authors":"Daniel E. Martínez ,&nbsp;Orlando M. Quiroz-Londoño ,&nbsp;Alejandro D. Basaldua ,&nbsp;Melisa Glok-Galli ,&nbsp;Ximena Solana ,&nbsp;Emiliano F. Alcaraz ,&nbsp;Leandro Bertolin","doi":"10.1016/j.gsd.2024.101402","DOIUrl":"10.1016/j.gsd.2024.101402","url":null,"abstract":"<div><div>Argentina is a large country in South America, spans from the tropical latitude S 26°15′ to the subpolar latitude S 53°38’, and longitudinally from the Atlantic coast to the Andes range in the West. Such extensive geographical coverage includes diverse climatic and geological conditions, fostering a multitude of aquifers with varying hydrogeological attributes. Groundwater dating is an underdeveloped discipline in Argentina, but several studies have been conducted. These studies are delineated across various aquifer typologies, namely: 1) Sedimentary aquifers in Eastern Patagonia, 2) Alluvial aquifers in the middle Andes area; 3) Shallow aquifers in the Pampa region; 4) Confined intermediate aquifers in the Pampa region; 5) Fractured aquifer in crystalline rocks; 6) the confined sandstones of the Guaraní aquifer; 7) Deep confined aquifers in the Pampa region.</div><div>Most of the studies are based in the classical tracers, ³H and ¹⁴C, with only a few employing other traces such as CFCs, ⁴He and ⁸¹Kr. In almost all the studies only an apparent age (piston flow model) is provided. Only two papers include the analysis of the residence time distribution. Considering the apparent ages, groundwater ranges from young in shallow unconfined aquifers, or fractured aquifers in crystalline rocks, to very old groundwater with an age of 1.2 Ma in the deep thermal aquifers. Groundwater which radiocarbon age corresponds to the Upper Pleistocene period, exhibits a depleted isotopic composition that correlates with colder temperatures in a sequence of glacial periods.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101402"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132985","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":"An adapted groundwater quality index including toxicological critical pollutants","authors":"Dario Cauich-Kau ,&nbsp;Javier Castro-Larragoitia ,&nbsp;Antonio Cardona Benavides ,&nbsp;María E. García-Arreola ,&nbsp;Gonzalo G. García-Vargas","doi":"10.1016/j.gsd.2024.101401","DOIUrl":"10.1016/j.gsd.2024.101401","url":null,"abstract":"<div><div>Water supply for population is mainly supported by groundwater. Its quality for drinking purposes is a worldwide major issue. Groundwater quality indexes are tools to assess its appropriateness for human consumption. This study evaluates the WAWQI index in North-Central Mexico with reported toxicological critical pollutants (arsenic, fluoride, and uranium) and put forward an adapted and optimized Groundwater Quality Index (GWQI). The research utilizes a dataset of groundwater parameters to analyze the chemical composition and suitability of groundwater (<em>n</em> = 121 samples) for drinking purposes. Arsenic concentrations fluctuate between 7.78 and 9798, average of 154 μg/L and only three samples within the Mexican guideline. Fluoride concentrations range from 0.01 to 2.2, average of 0.8 mg/L and 64% of the sample within the Mexican guideline. Uranium, not previously described in the region, show elevated concentrations in the range of 1.01–93.7 average of 11.4 μg/L and 88% within the WHO guideline. The WAWQI Index, applied to water samples, categorized samples as excellent and good status. However, 95%, 33%, and 11% of the samples are above As, F^-, and U guidelines; therefore, a tailored GWQI for drinking water was proposed and optimized considering the WAWQI limitations. The outcome of the GWQI shows that only two samples classified with excellent status and are safe to drink without treatment. However, 119 samples were classified as poor, very poor, and unsuitable for drinking purposes. The GWQI provides a valuable tool for assessing groundwater quality in regions with similar hydrogeological conditions to the study area, and to identify areas requiring urgent attention to ensure safe drinking water supply. The study highlights the urgent need for sustainable water management practices to address the region's water quality challenges and ensure the long-term well-being of its population and fulfill the Sustainable Development Goal 6.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"28 ","pages":"Article 101401"},"PeriodicalIF":4.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143133042","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}