Groundwater for Sustainable Development最新文献

{"title":"Groundwater fluoride contamination, sources, hotspots, health hazards, and sustainable containment measures: A systematic review of the Ghanaian context","authors":"Emmanuel Daanoba Sunkari ,&nbsp;Abayneh Ataro Ambushe","doi":"10.1016/j.gsd.2024.101352","DOIUrl":"10.1016/j.gsd.2024.101352","url":null,"abstract":"<div><div>Groundwater quality is globally threatened by geogenic and human activities. These activities release high levels of potentially toxic elements, such as fluoride (F⁻), which pose significant threats to human health. This has become a global issue, especially in developing countries such as Ghana. Despite efforts to address this issue, knowledge gaps still need to be addressed to ensure safe and healthy drinking water for all Ghanaians. Moreover, Ghana has been reported to be a fluorosis-endemic country but the sources and exact hotspots of F⁻ enrichment in the aquifers on a countrywide scale are lacking in the available literature. Understanding the quality of water used for diverse purposes in Ghana is necessary to achieve the United Nations Sustainable Development Goals like good health and well-being (SDG 3) and clean water and sanitation (SDG 6), among others. Therefore, this study synthesized all previous studies on groundwater F⁻ contamination in Ghana, to identify the sources of F⁻ enrichment in groundwater, delineate the hotspots for fluorosis, assess the associated human health risks, identify the best sustainable defluoridation methods, and recommend policy intervention for high groundwater F⁻ threat to aquifers in Ghana. In the Ghanaian context, F⁻ contamination in groundwater is largely from geogenic sources like the weathering of fluoride-bearing rocks (granitoids and carbonate sedimentary lithologies) from the Birimian and Voltaian Supergroups and the dissolution of fluoride-rich minerals (fluorapatite, amphiboles, fluorite, biotite, and muscovite). Hotspots for high groundwater F⁻ in Ghana are mainly restricted to the Upper East Region (0.10–5.00 mg/L), North East Region (0.01–13.29 mg/L), Northern Region (0.1–11.6 mg/L), and the White Volta River Basin (0.04–3.79 mg/L). The mean and maximum values of F⁻ in these hotspots exceed the maximum permissible level (1.5 mg/L) set by the World Health Organization and Ghana Standards Authority. Most people in these areas suffer from dental fluorosis. Therefore, affordable and sustainable defluoridation technologies as well as community-based initiatives are recommended to deal with this menace.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101352"},"PeriodicalIF":4.9,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Groundwater quality assessment for drinking and irrigation purposes in the Ayad river basin, Udaipur (India)","authors":"Kuldeep Pareta ,&nbsp;Sachin Karan ,&nbsp;Trine Enemark ,&nbsp;Tirumaleswara Reddy ,&nbsp;Yogita Dashora ,&nbsp;Tanya Issar ,&nbsp;Karsten H. Jensen","doi":"10.1016/j.gsd.2024.101351","DOIUrl":"10.1016/j.gsd.2024.101351","url":null,"abstract":"<div><div>Globally, about 5.25 billion people depend on groundwater for their water needs. However, groundwater quality significantly impacts human health and agriculture, influenced by factors such as land use, waste seepage, soil properties, and geological settings. In Rajasthan, the primary groundwater quality issues involve fluoride, nitrate, chloride, and calcium. This study addresses the gap in the understanding of the spatial and temporal variations of these contaminants and how the variations are linked to geology and land use. The basis for the analysis is data spanning 2000 to 2021from the Ground Water Department (GWD), the Central Ground Water Board (CGWB), and citizen science data from 2022 to 2023, focusing on the Ayad River Basin. The research aims to evaluate groundwater quality for drinking and irrigation by assessing physico-chemical parameters and using the Weighted Arithmetic Water Quality Index (WAWQI) method to calculate the Groundwater Quality Index (GWQI) from 2000 to 2023. The findings suggest a decreasing GWQI trend from west to east in the basin, with good groundwater quality (GWQI below 50) in the southern regions near the cities Umarda, Ramgiri, Undri, and Hariyab. The highest index values were near Bhoyana, Khemli, and Sisarma. The results of the salinity hazard test showed that salinity is a major issue in the eastern part of the basin. Though the groundwater is notably hard, a comprehensive analysis of various parameters nevertheless suggested its suitability for irrigation purposes. These results provide new insights in the quality of the groundwater resources in the Ayad River basin and valuable insights for policymakers and for decision-makers to develop strategies to preserve the groundwater quality.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101351"},"PeriodicalIF":4.9,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing a multi-tracer method to investigate sulphate contamination: Novel insights on hydrogeochemical characteristics of groundwater in intricate karst systems","authors":"Marina Filipović ,&nbsp;Josip Terzić ,&nbsp;Jasmina Lukač Reberski ,&nbsp;Igor Vlahović","doi":"10.1016/j.gsd.2024.101350","DOIUrl":"10.1016/j.gsd.2024.101350","url":null,"abstract":"<div><div>Karst environments, especially in Mediterranean area, are highly vulnerable to natural and anthropogenic contamination. This study presents a comprehensive hydrogeochemical assesment of surface water and groundwater across a 2300 km² catchment area spanning Southern Dalmatia (Croatia) and Western Herzegovina (Bosnia and Herzegovina).</div><div>For the first time in the study area, data were collected over six years integrating ion analysis, sulphur isotope (δ³⁴S) composition, and physical-chemical analysis of water from 30 locations. The research identified four hydrogeochemical facies (carbonate, sulphate, mixed carbonate/sulphate and chloride), influenced by seawater intrusion, carbonate dissolution, evaporite presence, and human activities.</div><div>Elevated sulphate levels, often exceeding 250 mg/L, were a main focus of the study due to their potential risks to drinking water quality. The study developed a conceptual model to explain the distribution of sulphates, underscoring the importance of evaporite diapirism and δ³⁴S analysis in tracing sulphate origins. These findings contribute to an improved understanding of karst systems and offer essential data for groundwater protection and legislative measures in the Mediterranean region.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101350"},"PeriodicalIF":4.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424738","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":"Three-dimensional solute transport in finite and curved porous media with surface input sources","authors":"Sujata Kushwaha ,&nbsp;Joy Roy ,&nbsp;R.R. Yadav","doi":"10.1016/j.gsd.2024.101349","DOIUrl":"10.1016/j.gsd.2024.101349","url":null,"abstract":"<div><div>In this paper, an analytical solution for three-dimensional solute transport in porous media between two curved surfaces is investigated. It is assumed that the groundwater velocity and dispersion coefficient vary with time and position. Groundwater velocity is not considered to be horizontal. The components of dispersion coefficient along the axes are considered to be proportional to the square of corresponding the position variable. The dispersion coefficient components along axes are proportional to the corresponding component of groundwater velocity in temporal aspects while former is squarely proportional to letter one in position components. It is assumed that the sources originate from two curved surfaces. The nature of the source on the two surfaces is the same, but there may be a variation in potential. Initially, the aquifer's domain is supposed to be uniformly polluted. The Laplace Integral Transformation Technique (LITT) is used to obtain analytical solutions. Numerical examples are given to demonstrate the effects of various factors on the solute concentration profile in a system where advection and dispersion play important roles.</div><div>In addition, the sub-case of horizontal flow is also discussed. The model is extremely useful in analyzing and dealing with widespread surface sources of groundwater pollution in simulated agricultural fields or urban dumping areas.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101349"},"PeriodicalIF":4.9,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424806","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":"Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods","authors":"Musaab.A.A. Mohammed ,&nbsp;Norbert P. Szabó ,&nbsp;Péter Szűcs","doi":"10.1016/j.gsd.2024.101348","DOIUrl":"10.1016/j.gsd.2024.101348","url":null,"abstract":"<div><div>Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101348"},"PeriodicalIF":4.9,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of the current scenario and best possible solution for fecal sludge management (FSM) in India","authors":"Shubhra Singh ,&nbsp;N. Janardhana Raju ,&nbsp;Gauhar Mehmood ,&nbsp;Sanjay Kumar Gupta ,&nbsp;Sirajuddin Ahmed","doi":"10.1016/j.gsd.2024.101346","DOIUrl":"10.1016/j.gsd.2024.101346","url":null,"abstract":"<div><div>Fecal Sludge (FS) is partially digested slurry which is collected from onsite sanitation system (OSSs) such as septic tanks and pit latrines and dumped into nallas, open drains, open lands and water bodies. The current research is motivated by the awful situation and difficulties associated with managing FS in India. This study aims to provide a comprehensive analysis of FS production, gaps, challenges, impact, and the most cost-effective FS treatment solution for cities of India. The potential for commercialization as well as the reuse of treated FS in Indian cities are covered in this research. The current status of FS management in Indian cities is also reported through fecal waste flow diagram. Many septic tanks are poorly constructed, outdated, and do not meet required specifications in Indian cities. Groundwater is one of India's most valuable resources, and it is also impacted by seepage or infiltration of contaminants from septic tanks. UNICEF claims that if FS is not properly treated, it can pollute the surrounding environment, and drinking water supplies can cause severe diseases such as diarrhoea, dysentery and cholera. A survey revealed that a significant portion of urban India is unsewered and lacks access to adequate sanitation. Hence, there is an urgent need to conduct research in this area to better understand the impact of FS on water resources and land quality. Many individuals and groups from the public, commercial, and civil society sectors are required for the safe handling of FS at every point of the sanitation chain, from the household user to the final disposal of treated FS. To achieve Sustainable Development Goal 6 \"clean water and sanitation\" by 2030, there is an urgent need for cost-effective FSM solutions for developing countries.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101346"},"PeriodicalIF":4.9,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311066","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":"Multiscale groundwater level forecasts with multi-model ensemble approaches: Combining machine learning models using decision theories and bayesian model averaging","authors":"Dilip Kumar Roy ,&nbsp;Sujit Kumar Biswas ,&nbsp;Md Panjarul Haque ,&nbsp;Chitra Rani Paul ,&nbsp;Tasnia Hossain Munmun ,&nbsp;Bithin Datta","doi":"10.1016/j.gsd.2024.101347","DOIUrl":"10.1016/j.gsd.2024.101347","url":null,"abstract":"<div><div>Creating precise groundwater level (GWL) prediction models is of crucial significance for the productive use, extended planning, and controlling of limited sub-surface water supplies. In this research, the accuracy of GWL forecasts in Bangladesh was enhanced for three weeks by utilizing ensembles of Machine Learning (ML) models. Six advanced ML-based models were developed and assessed using eight performance indices, and an Overall Ranking (OR) was provided by combining the rankings produced by Grey Relational Analysis (GRA), Variation Coefficient (COV), and Shannon's Entropy (SE). The standalone forecasting models demonstrated excellent performance across the three forecasting horizons, with accuracy values ranging from 0.986 to 0.997 for one-step, 0.971 to 0.999 for two-step, and 0.960 to 0.997 for three-step forecasts at GT3330001. Results also revealed that three ranking techniques (SE, COV, and GRA), as well as their combined ranking (OR), produced different best-performing models at different prediction horizons for different observation wells. Weighted average ensembles of the prediction models were developed by calculating individual model weights using four ensemble modelling techniques: SE, COV, GRA, and Bayesian Model Averaging (BMA). The BMA-based ensemble technique outperformed three benchmark ensemble approaches, achieving R = 0.947, KGE = 0.925, IOA = 0.972, MAE = 0.062 m, and RMSE = 0.123 m for one-step-ahead forecasts at GT3330001. The findings exhibit a consistent trend across other forecasting horizons and observation wells. Finally, the Dempster-Shafer evidence theory was employed to rank the single and composite models. The ranking results demonstrated that the BMA-based ensemble consistently secured the top position (with the weight values of 0.997, 0.991, and 0.987 for one-week, two-weeks, and three-weeks forward forecasts at GT3330001) for all forecasting horizons and observation wells. This study shows that the BMA-based composite model can produce more accurate GWL projections at Bangladesh study location, with potential for application in other regions worldwide.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101347"},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311065","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":"Application of the DRASTIC-LU/LC method combined with machine learning models to assess and predict the vulnerability of the Rmel aquifer (Northwest, Morocco)","authors":"Morad Chahid ,&nbsp;Jamal Eddine Stitou El Messari ,&nbsp;Ismail Hilal ,&nbsp;Mourad Aqnouy","doi":"10.1016/j.gsd.2024.101345","DOIUrl":"10.1016/j.gsd.2024.101345","url":null,"abstract":"<div><div>The Rmel aquifer, located in the Tangier-Tetouan-Al Hoceima region of northwest Morocco, covers approximately 240 km² and faces increasing pollution threats due to population growth and economic development. This study assesses aquifer vulnerability to pollution, and compares the performance of various machine learning models integrated with the DRASTIC-LU/LC method. The research used a dataset of 52 water samples analyzed for nitrate concentrations, considering eight factors influencing vulnerability: aquifer depth, net recharge, aquifer lithology, soil texture, topography, vadose zone impact, hydraulic conductivity, and land use. An information gain test was applied to evaluate the importance of these factors. Four machine learning algorithms were used with the DRASTIC-LU/LC method: multilayer perceptron (MLP), the bagging algorithm (BA), K-nearest neighbors (KNN), and extremely randomized trees (ERT). Model performance was assessed via the area under the ROC curve (ROC-AUC) to measure accuracy. The ERT model combined with DRASTIC-LU/LC achieved the highest accuracy (AUC = 0.929), followed by BA (AUC = 0.925), MLP (AUC = 0.852), and KNN (AUC = 0.787). In comparison, the original DRASTIC-LU/LC model had an AUC of 0.530. The results highlight significant vulnerability variation across the Rmel aquifer, with high to very high levels in the southern and northwestern regions, and moderate to low levels in the northeast and central areas. Vulnerability maps were validated by comparing the observed nitrate concentrations in the water samples, confirming model accuracy. Groundwater depth, net recharge, and hydraulic conductivity were identified as the most significant factors influencing vulnerability. This study demonstrates the effectiveness of integrating machine learning models with the DRASTIC-LU/LC method for accurate aquifer vulnerability assessment, offering valuable tools for public policy and groundwater management.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101345"},"PeriodicalIF":4.9,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311064","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":"Identifying potential artificial recharge zone in an arid craton","authors":"Ravi Shankar Dubey,&nbsp;Pallavi Banerjee Chattopadhyay,&nbsp;Unmilon Pal","doi":"10.1016/j.gsd.2024.101338","DOIUrl":"10.1016/j.gsd.2024.101338","url":null,"abstract":"<div><p>Identifying sustainable artificial recharge zones in arid cratons is challenging due to complex geology and limited natural recharge conditions, making accurate site selection and management difficult. This study integrates Vertical Electrical Sounding (VES), the Analytic Hierarchy Process (AHP), and Boolean analysis to identify sustainable artificial recharge zones in the arid Bundelkhand craton of India. Aquifer thickness and fractures emerged as critical determinants of groundwater recharge conditions, revealing varying degrees of suitability for recharge across the study area. Approximately 2.31% (13.36 km²) of the area along streams exhibited \"very high\" suitability, while 8.09% (45.82 km²) had \"high\" suitability. “Moderate\" suitability covered 17.86% (101.66 km²), \"low\" suitability accounted for 38.85% (218.39 km²), and \"very low\" suitability represented 17.35% (98.75 km²) of the area. Recharge potential was highest in the northeast and central parts, with the middle of the watershed exhibiting the lowest potential. The study demonstrated that this integrated approach significantly improved precision from 71.40% to 85.70% and enhanced the F1 score from 0.833 to 0.923, surpassing the performance of the AHP method alone. The findings highlighted the importance of strategic selection and targeting of specific locations for artificial recharge, as only ∼18% of the study area was suitable for such efforts, despite ∼43% showing potential for groundwater. AHP with VES proves more precise and reliable than Fuzzy-AHP with VES, with AHP's conservative approach classifying 55.70% of the area as very low to low suitability compared to Fuzzy-AHP's 41.92%, ensuring only the most suitable sites are selected. VES offers cost-effectiveness, noninvasiveness, and rapid generation of a 1D subsurface model, balancing its lower detail compared to Electrical Resistivity Tomography. When combined with the AHP, VES enhances adaptability to changing conditions, emphasizing ecological preservation and climate change resilience. This approach effectively addresses water challenges in arid regions, contributing to sustainable water resource management.</p></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101338"},"PeriodicalIF":4.9,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142238670","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":"Trihalomethanes in chlorinated drinking water: Seasonal variations and health risk assessment in southern Iran","authors":"Amin Mohammadpour ,&nbsp;Zahra Emadi ,&nbsp;Enayat Berizi ,&nbsp;Azadeh Kazemi","doi":"10.1016/j.gsd.2024.101342","DOIUrl":"10.1016/j.gsd.2024.101342","url":null,"abstract":"<div><div>Assessing the adverse impacts of trihalomethanes, the most hazardous disinfection by-products, is crucial for community health protection. This study evaluated physicochemical parameters, trihalomethane levels, their prediction, and risk assessment using probability and Sobol analysis. Results indicated that electrical conductivity, total dissolved solids, nitrate, sulfate, calcium, lithium, total organic carbon, and ammonium exceeded permissible limits. Tribromomethane (0.14–3.21 μg/L in winter; 0.06–0.17 μg/L in summer), trichloromethane (1.90–3.53 μg/L in winter; 3.19–5.44 μg/L in summer), bromodichloromethane (0.62–4.24 μg/L in winter; 3.27–6.41 μg/L in summer), and dibromochloromethane (0.82–2.41 μg/L in winter; 0.69–3.03 μg/L in summer) remained within safe limits. Random Forest analysis identified total organic carbon as the most significant factor in trihalomethane production, with a positive correlation between trihalomethanes and bromide. Per the World Health Organization's risk assessment, trihalomethane concentrations posed no harm to residents (I_WHO&lt;1). However, the United States Environmental Protection Agency's assessment indicated an acceptable low cancer risk (100% cumulative cancer risk for all groups). Additionally, nitrate and fluoride levels surpassed the standard limit, with hazard index above 1 in both seasons for residents. Monte Carlo simulations showed that the 95th percentile of residents faced non-carcinogenic (nitrate and fluoride). However, 100% of children and 99.98% of adults were exposed to an acceptable low carcinogenic risk for THMs. Factors like inhalation rate, body weight, and trihalomethane levels significantly impacted health risk. These findings highlight the necessity for continuous monitoring and effective water treatment to safeguard public health, promote clean water, and advance sustainable development, advocating for sustainable water management to tackle health risks from environmental pollutants like disinfection by-products.</div></div>","PeriodicalId":37879,"journal":{"name":"Groundwater for Sustainable Development","volume":"27 ","pages":"Article 101342"},"PeriodicalIF":4.9,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311067","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}