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Geochemistry and hydrology of perched groundwater springs: assessing elevated uranium concentrations at Pigeon Spring relative to nearby Pigeon Mine, Arizona (USA) | Géochimie et hydrologie des émergences de nappes perchées : interprétation des fortes concentrations en uranium de la Source du Pigeon au regard de la proche Mine du Pigeon, Arizona (Etats-Unis d’Amérique) Hidrología y geoquímica de manantiales de agua subterránea colgada: análisis de las concentraciones elevadas de uranio en el Manantial Pigeon relacionado con las inmediaciones de la Mina Pigeon, Arizona (EEUU) 上层滞水泉的地球化学和水文状况:评价 (美国) 亚利桑那州Pigeon 矿附近Pigeon泉水中升高的铀含量摘要 Geoquímica e hidrologia de nascentes de aquíferos suspensos: avaliando concentrações elevadas de urânio na Nascente de Pigeon relativa à Mina de Pigeon nas proximidades, Arizona (EUA) 全文
2017
Beisner, Kimberly R. | Paretti, Nicholas V. | Tillman, Fred D. | Naftz, David L. | Bills, Donald J. | Walton-Day, Katie | Gallegos, Tanya J.
The processes that affect water chemistry as the water flows from recharge areas through breccia-pipe uranium deposits in the Grand Canyon region of the southwestern United States are not well understood. Pigeon Spring had elevated uranium in 1982 (44 μg/L), compared to other perched springs (2.7–18 μg/L), prior to mining operations at the nearby Pigeon Mine. Perched groundwater springs in an area around the Pigeon Mine were sampled between 2009 and 2015 and compared with material from the Pigeon Mine to better understand the geochemistry and hydrology of the area. Two general groups of perched groundwater springs were identified from this study; one group is characterized by calcium sulfate type water, low uranium activity ratio ²³⁴U/²³⁸U (UAR) values, and a mixture of water with some component of modern water, and the other group by calcium-magnesium sulfate type water, higher UAR values, and radiocarbon ages indicating recharge on the order of several thousand years ago. Multivariate statistical principal components analysis of Pigeon Mine and spring samples indicate Cu, Pb, As, Mn, and Cd concentrations distinguished mining-related leachates from perched groundwater springs. The groundwater potentiometric surface indicates that perched groundwater at Pigeon Mine would likely flow toward the northwest away from Pigeon Spring. The geochemical analysis of the water, sediment and rock samples collected from the Snake Gulch area indicate that the elevated uranium at Pigeon Spring is likely related to a natural source of uranium upgradient from the spring and not likely related to the Pigeon Mine.
显示更多 [+] 显示较少 [-]Insights and participatory actions driven by a socio-hydrogeological approach for groundwater management: the Grombalia Basin case study (Tunisia) | Aperçus et actions participatives guidées par une approche socio-hydrogéologique pour la gestion des ressources en eau: le cas d’étude du bassin de Grombalia (Tunisie) Perspectivas y acciones participativas impulsadas por un enfoque socio-hidrogeológico para la gestión del agua subterránea: estudio de caso de la Cuenca de Grombalia (Túnez) 地下水管理中一个社会-水文地质方法带动的深刻认识和参与性行动:(突尼斯)古兰巴利耶流域研究实例 Indagini e azioni partecipative realizzate tramite un approccio socio-idrogeologico per la gestione delle risorse idriche sotterranee: il caso dell’acquifero di Grombalia (Tunisia) Perspectivas e ações participativas conduzidas por uma abordagem socio-hidrogeológica para a gestão das águas subterrâneas: o estudo de caso da Bacia de Grombalia (Tunísia) 全文
2017
Tringali, C. | Re, V. | Siciliano, G. | Chkir, N. | Tuci, C. | Zouari, K.
Sustainable groundwater management strategies in water-scarce countries need to guide future decision-making processes pragmatically, by simultaneously considering local needs, environmental problems and economic development. The socio-hydrogeological approach named ‘Bir Al-Nas’ has been tested in the Grombalia region (Cap Bon Peninsula, Tunisia), to evaluate the effectiveness of complementing hydrogeochemical and hydrogeological investigations with the social dimension of the issue at stake (which, in this case, is the identification of groundwater pollution sources). Within this approach, the social appraisal, performed through social network analysis and public engagement of water end-users, allowed hydrogeologists to get acquainted with the institutional dimension of local groundwater management, identifying issues, potential gaps (such as weak knowledge transfer among concerned stakeholders), and the key actors likely to support the implementation of the new science-based management practices resulting from the ongoing hydrogeological investigation. Results, hence, go beyond the specific relevance for the Grombaila basin, showing the effectiveness of the proposed approach and the importance of including social assessment in any given hydrogeological research aimed at supporting local development through groundwater protection measures.
显示更多 [+] 显示较少 [-]Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine) | Intégration d’une approche d’intelligence artificielle avec des moyennes de k par bouquet pour modéliser la salinité de l’eau souterraine: cas de l’aquifère côtier de Gaza (Palestine) Integración de un enfoque de inteligencia artificial con el agrupamiento de k-medios para modelar la salinidad del agua subterránea: el caso del acuífero costero de Gaza (Palestina) دمج تقنية الذكاء الصناعي مع وسيلة التصنيف "k-means" لنمذجة ملوحة المياه الجوفية : الحالة الدراسية، خزان قطاع غزة الجوفي (فلسطين) 人工智能方法与k-均值聚类结合在一起模拟地下水盐度:(巴勒斯坦)加沙沿海含水层的实例 Integrando uma abordagem de inteligência artificial com clusterização por k-means para modelar a salinidade das águas subterrâneas: o caso de um aquífero costeiro de Gaza (Palestina) 全文
2017
Alagha, Jawad S. | Seyam, Mohammed | Md Said, Md Azlin | Mogheir, Yunes
Artificial intelligence (AI) techniques have increasingly become efficient alternative modeling tools in the water resources field, particularly when the modeled process is influenced by complex and interrelated variables. In this study, two AI techniques—artificial neural networks (ANNs) and support vector machine (SVM)—were employed to achieve deeper understanding of the salinization process (represented by chloride concentration) in complex coastal aquifers influenced by various salinity sources. Both models were trained using 11 years of groundwater quality data from 22 municipal wells in Khan Younis Governorate, Gaza, Palestine. Both techniques showed satisfactory prediction performance, where the mean absolute percentage error (MAPE) and correlation coefficient (R) for the test data set were, respectively, about 4.5 and 99.8% for the ANNs model, and 4.6 and 99.7% for SVM model. The performances of the developed models were further noticeably improved through preprocessing the wells data set using a k-means clustering method, then conducting AI techniques separately for each cluster. The developed models with clustered data were associated with higher performance, easiness and simplicity. They can be employed as an analytical tool to investigate the influence of input variables on coastal aquifer salinity, which is of great importance for understanding salinization processes, leading to more effective water-resources-related planning and decision making.
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