Groundwater for Sustainable Development最新文献_第3页

Fluoride and nitrate contamination in groundwater of Naini Industrial Area, Uttar Pradesh: Assessing non-carcinogenic human health risk

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101388

Nighat Parveen , Soma Giri , Abhay Kumar Singh , Jayant Kumar Tripathi

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.

{"title":"Fluoride and nitrate contamination in groundwater of Naini Industrial Area, Uttar Pradesh: Assessing non-carcinogenic human health risk","authors":"Nighat Parveen , Soma Giri , Abhay Kumar Singh , 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 NO<sub>3</sub><sup>−</sup> 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<sup>−</sup> 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}

引用次数: 0

Groundwater level prediction using modified recurrent neural network combined with meta-heuristic optimization algorithm

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101398

Eui Hoon Lee

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.

{"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}

引用次数: 0

A novel predictive framework for water quality assessment based on socio-economic indicators and water leaving reflectance

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2025.101405

Hao Chen , Ali P. Yunus

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 (<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.

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引用次数: 0

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

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101395

Babu Mallesh Dasari , Keshav Krishna Aradhi , Dasaram Banothu

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.

{"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 , Keshav Krishna Aradhi , 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}

引用次数: 0

Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101403

Zenhom El-Said Salem , Nesma A. Arafa , Abdelaziz L. Abdeldayem , Youssef M. Youssef

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.

{"title":"Machine learning-enhanced GALDIT modeling for the Nile Delta aquifer vulnerability assessment in the Mediterranean region","authors":"Zenhom El-Said Salem , Nesma A. Arafa , Abdelaziz L. Abdeldayem , 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 (R<sup>2</sup>), 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: R<sup>2</sup> = 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<sub>XGBoost</sub> 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 SO₄<sup>2</sup>⁻ 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}

引用次数: 0

Spatial and temporal distribution of arsenic in groundwater of the Brahmaputra River floodplains in Assam, India

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101400

Smitakshi Medhi, Runti Choudhury

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 (<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 <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.

{"title":"Spatial and temporal distribution of arsenic in groundwater of the Brahmaputra River floodplains in Assam, India","authors":"Smitakshi Medhi, 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 (<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, Ca<sup>2+</sup>, Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+,</sup> Cl<sup>−</sup>, HCO<sub>3</sub><sup>−</sup>, NO<sub>3</sub><sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, 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 <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-HCO<sub>3</sub> as a primary water type during pre-monsoon, while Ca-Mg-HCO<sub>3</sub> type during post monsoon.Groundwater is undersaturated with respect to As phases such as Arsenolite and As<sub>2</sub>O<sub>5</sub> specifying that As is in dissolved form in the groundwater. The groundwater is supersaturated with calcite (CaCO<sub>3</sub>) and Dolomite (MgCa(CO<sub>3</sub>)<sub>2</sub>and Fe(III) (Oxyhyroxide). The stable isotopes (δ<sup>18</sup>O and δ<sup>2</sup>H) 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}

引用次数: 0

Determining factors and strategy in sustainable fecal sludge management services

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101390

Nadia Paramita , Rachmadhi Purwana , Djoko Mulyo Hartono , Tri Edhi Budhi Soesilo

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 Escherichia coli 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.

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引用次数: 0

Decoding groundwater level patterns and abrupt changes in Central and Southern California's alluvial regions

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2025.101409

Fabio Di Nunno, Francesco Granata

The variability in groundwater levels (GWL) in California's Central Valley and Southern California Coastal Basin, driven by climatic and hydrological shifts, poses significant challenges for ecosystems and agricultural sustainability. This study employs a dual-method approach, integrating the Seasonal Kendall (SK) test and the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm, to analyze long-term trends and abrupt shifts in GWL. The SK test reveals statistically significant declines in GWL across most wells, with particularly severe reductions observed in the Central Valley and the counties of San Bernardino and San Diego. For instance, well A5 in the Central Valley recorded a Z-value of 23.83 and a β of 2.36, marking acute groundwater depletion. Similarly, in San Bernardino County, wells S11 (Z = 24.09, β = 14.50) and S17 (Z = 24.20, β = 9.53) demonstrated alarming declines. These findings suggest that reduced recharge rates and intensified extraction are driving the depletion, which in turn threatens local ecosystems through diminished streamflows and wetland contraction. However, some wells exhibited rising GWL, attributed to localized recharge, underscoring the spatial heterogeneity of groundwater dynamics. BEAST analysis further identified both positive and negative abrupt changes in GWL, reflecting complex responses to environmental variability. While several wells recorded sharp drops in GWL, such as up to −7.48 m in the Central Valley and −44.00 m in Southern California, others demonstrated notable recoveries, including up to 4.20 m in the Central Valley and 9.31 m in Southern California. These results emphasize the urgent need for tailored groundwater management strategies that address both declining and rising trends, while accounting for seasonal variability. Adaptive water management practices, which are flexible and responsive to changing conditions, will be crucial to safeguarding ecosystem integrity and sustaining agricultural productivity.

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引用次数: 0

Spatiotemporal assessment of groundwater quality under climate change using multiscale clustering technique

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2025.101407

Roghayeh Ghasempour , V.S. Ozgur Kirca

Assessing spatiotemporal variations of groundwater quality and identifying vulnerable areas is a crucial stage in the planning and management of water resources. This study focuses on utilizing a multiscale method to assess the water quality variables in the groundwater of Ardabil basin located in Iran. This plain is one of the important industrial and agricultural regions in Iran, and groundwater provides 89% of its total water demand. Therefore, investigating groundwater quality for this plain is indispensable. The monthly timescale datasets from 26 piezometers, covering the period of 2000–2022, were de-noised and decomposed using the Wavelet transform (WT) and Variational Mode Decomposition (VMD), respectively. The Permutation Entropy (PE) values of the subseries were computed and considered as inputs of the K-means method to zone and classify the basin in terms of the Total Dissolved Solids (TDS) and Electrical Conductivity (EC). The EC and TDS of central piezometers were predicted and the modeling uncertainty was investigated. From results, excessive use of groundwater resources resulted in a drop in groundwater levels even in rainy years. It was found that the integrated approach exhibited a desirable degree of reliability. Groundwater vulnerability assessment was done considering the hydrogeological parameters affecting groundwater pollution and using the DRASTIC approach. Nitrate values were used to validate the DRASTIC method. Matching the nitrate ion distribution map to the vulnerability map showed that the two maps corresponded, indicating that most of the points with high nitrate (21–42 mg/l) were located in areas with higher vulnerability potential (central parts).

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引用次数: 0

Probabilistic mapping of imbalanced data for groundwater contamination using classification algorithms: Performance and reliability

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL

Groundwater for Sustainable Development

Pub Date : 2025-02-01 DOI: 10.1016/j.gsd.2024.101393

Yang Qiu , Aiguo Zhou , Hanxiang Xiong , Defang Zhang , Cheng Su , Shizheng Zhou , Lin Go , Chi Yang , Hao Cui , Wei Fan , Yao Yu , Fawang Zhang , Chuanming Ma

The probabilistic mapping of groundwater contamination is a crucial foundation for sustainable groundwater management. However, groundwater data often exhibit imbalance, posing challenges for precise and reliable probability mapping. This study focused on the Jianghan Plain, evaluating the performance and reliability of various sampling and ensemble techniques using a small, imbalanced dataset (n = 246, Class0/Class1 = 0.84/0.16). Probabilistic maps revealed significant spatial variability, with high-probability areas concentrated in the western (Yichang City), eastern (Wuhan), and northern regions (north bank of Han River), while low-probability areas were in the central and southern regions. Over-sampling methods outperformed others by maintaining class balance and enhancing the reliability of mapping outcomes. The high-very high probability areas for over-sampling methods ranged from 15.5% to 18.9%, with larger very low-low areas (60.5%–66.3%). In contrast, under-sampling and ensemble methods showed larger high-very high probability areas (34.0%–53.1%) and smaller very low-low areas (21.6%–46.3%). Over-sampling methods exhibited higher F1 scores (0.27–0.33) and precision (0.375–0.43) compared to other methods. SHAP analysis demonstrated that over-sampling methods balance datasets while preserving information integrity, enhancing the credibility of mapping results. Conversely, ensemble methods faced challenges in statistical analysis, hindering interpretability. We strongly recommend, that in conducting probabilistic mapping of groundwater contamination, it is imperative to adequately consider the imbalance of datasets and not solely rely on metrics like AUC and OA. For small-size datasets akin to this study, SMOTE and ADASYN emerge as recommended sampling methods, they not only yield high-precision mapping results but also ensure interpretability, thereby providing a more reliable basis for sustainable groundwater management.

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引用次数: 0