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Kumar, V.; Setia, R.; Pandita, S.; Singh, S.; Mitran, T. |
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Title |
Assessment of U and As in groundwater of India: A meta-analysis |
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Journal Article |
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Year |
2022 |
Publication |
Chemosphere |
Abbreviated Journal |
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303 |
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135199 |
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Keywords |
Arsenic, Geology, Groundwater, Health risk, Soil texture, Uranium |
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Abstract |
More than 2.5 billion people depend upon groundwater worldwide for drinking, and giving quality water has become one of the great apprehensions of human culture. The contamination of Uranium (U) and Arsenic (As) in the groundwater of India is gaining global attention. The current review provides state-of-the-art groundwater contamination with U and As in different zones of India based on geology and soil texture. The average concentration of U in different zones of India was in the order: West Zone (41.07 μg/L) \textgreater North Zone (37.7 μg/L) \textgreater South Zone (13.5 μg/L)\textgreater Central Zone (7.4 μg/L) \textgreater East Zone (5.7 μg/L) \textgreaterSoutheast Zone (2.4 μg/L). The average concentration of As in groundwater of India is in the order: South Zone (369.7 μg/L)\textgreaterCentral Zone (260.4 μg/L)\textgreaterNorth Zone (67.7 μg/L)\textgreaterEast Zone (60.3 μg/L)\textgreaterNorth-east zone (9.78 μg/L)\textgreaterWest zone (4.14 μg/L). The highest concentration of U and As were found in quaternary sediments, but U in clay skeletal and As in loamy skeletal. Results of health risk assessment showed that the average health quotient of U in groundwater for children and adults was less than unity. In contrast, it was greater than unity for As posing a harmful impact on human health. This review provides the baseline data regarding the U and As contamination status in groundwater of India, and appropriate, effective control measures need to be taken to control this problem. |
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0045-6535 |
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THL @ christoph.kuells @ kumar_assessment_2022 |
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161 |
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Jana, A.; Unni, A.; Ravuru, S.S.; Das, A.; Das, D.; Biswas, S.; Sheshadri, H.; De, S. |
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Title |
In-situ polymerization into the basal spacing of LDH for selective and enhanced uranium adsorption: A case study with real life uranium alkaline leach liquor |
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Journal Article |
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Year |
2022 |
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Chemical Engineering Journal |
Abbreviated Journal |
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428 |
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131180 |
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In-situ polymerization, Layered double hydroxide, Leach liquor, Uranium adsorption, Uranium recovery |
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Uranium is used as a fuel for nuclear power plant and can be extracted from different ores, mainly acidic (silicious ore) and alkaline (carbonate ore). Recovery of uranium through acid leaching from silicious ore is well established, whereas, alkaline leaching from carbonate ore is challenging due to the excessive salinity of leach liquor and high concentration of carbonate, bicarbonate and sulphate. Herein, two monomers, acrylic acid (AA) and N, N-methylene bisacrylamide (BAM), selective towards uranyl were intercalated in-situ into the interlayer, followed by their polymerization and cross-linking to form novel polymer intercalated hybrid layered double hydroxide (LDH). The LDH acts as a backbone to overcome coiling and swelling of polymer and anchors them as free-standing. Various parameters, like, the type of metal ions, monomer ratio (AA: BAM) and metal ion ratio (M2+:M3+), were studied to determine the optimum conditions for effective intercalation and polymerization of monomers. Magnesium aluminum (MgAl) LDH with a cross-linked polymer having a monomer ratio of 3:2 (AA: BAM) as intercalating species showed maximum efficiency of uranyl adsorption (1456 mg/g at 30 °C) with highest capacity so far. The distribution coefficient (Kd, l/mg) in the order of 105 suggested that the adsorbent was highly selective for uranyl in the presence of different cations, anions and humic acid. The adsorbent extracts uranium effectively and selectively from a real-life alkaline leach liquor with an efficiency of 96% at 5 g/l dose. Uranium can be recovered from the adsorbent in the form of sodium di-uranate using 2(M) NaOH and was reused for eight cycles. |
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1385-8947 |
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THL @ christoph.kuells @ jana_-situ_2022 |
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209 |
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Seidl, C.; Wheeler, S.A.; Page, D. |
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Understanding the global success criteria for managed aquifer recharge schemes |
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Journal Article |
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2024 |
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Journal of Hydrology |
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628 |
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130469 |
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Managed Aquifer Recharge (MAR), Fuzzy-set Qualitative Comparative Analysis, Water banking, Groundwater, Water management, Water storage |
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Water availability and quality issues will only gain importance in the future, with climate change impacts putting increasing pressure on global water resources. Dealing with these challenges requires drawing on all available water management tools, including Managed Aquifer Recharge (MAR). Although MAR has seen increasing global implementation during the last half a century, it is still often overlooked as a management tool. While technical, bio-physical, and hydrogeological aspects of MAR are well researched, this cannot be said for socio-economic and other governance factors. Where information is available, this study seeks to understand the conditions necessary for MAR success. We apply fuzzy-set Qualitative Comparative Analysis on 313 world MAR applications, and also model separately for high- and low-middle-income countries. Results show that sophisticated hydrogeological site understanding and scheme operation is paramount for MAR success, as is utilizing natural water sources for high value end uses. Successful high-income country MAR schemes tend to be large and utilize natural water sources and sophisticated water injection and treatment methods to augment potable water supply; while successful low-middle-income country schemes are not large, older than 20 years, and use gravity infiltration methods and (limited) no water treatment. These findings will help inform the future suitability of MAR application design and its likely success within various contexts. |
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0022-1694 |
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THL @ christoph.kuells @ Seidl2024130469 |
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273 |
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Leeuwen, Z.R. van; Klaar, M.J.; Smith, M.W.; Brown, L.E. |
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Quantifying the natural flood management potential of leaky dams in upland catchments, Part II: Leaky dam impacts on flood peak magnitude |
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Journal Article |
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2024 |
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Journal of Hydrology |
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628 |
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130449 |
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Nature based solutions, Large wood, Empirical, Hydrograph analysis, Ecosystem services, Transfer function noise model |
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Leaky dams are an increasingly popular natural flood management measure, yet their impacts on flood peak magnitude have not yet been empirically quantified for a range of event types and magnitudes, even at the stream scale. In this study, the novel application of a transfer function noise modelling approach to empirical Before-After-Control-Impact stage data from an upland catchment allowed leaky dam effectiveness in reducing flood peak magnitude to be quantified. Flood peak stage and discharge magnitude changes were assessed from empirical data for 50 single and multi-peaked high flow events with return periods ranging from less than one year to six years. Overall, event peak magnitude was significantly reduced following the installation of eight leaky dams on the impact stream. Effectiveness was highly variable, but on average, flood peak magnitude was reduced by 10% for events with a return period up to one year. Some of the variability was explained by the size of the event and whether it was a single or multi-peaked event. This finding emphasises the need to manage expectations by considering both a range of event magnitudes and types when designing or assessing leaky dam natural flood management schemes. |
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0022-1694 |
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THL @ christoph.kuells @ Vanleeuwen2024130449 |
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228 |
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Wang, B.; Luo, Y.; Qian, J.-zhong; Liu, J.-hui; Li, X.; Zhang, Y.-hong; Chen, Q.-qian; Li, L.-yao; Liang, D.-ye; Huang, J. |
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Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium |
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Journal Article |
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2023 |
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Journal of Hydrology |
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626 |
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130234 |
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In-situ leaching, Injection rate design, Lixiviant concentration design, Machine learning, Simulation-optimisation, Uncertainty |
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The migration process of leached uranium in the in-situ leaching of uranium is considered a typical reactive transport problem. During this process, the lixiviant concentration and injection rate are important in-situ leaching process parameters (ISLPP) to efficiently recover uranium. However, several uncertain factors affect the outcomes of the ISLPP design. In addition, the repeated use of the reactive transport model (RTM) for investigating the acid in-situ leaching of uranium with the application of the Monte Carlo method leads to a substantial computational load. For this reason, a machine learning (ML)–based surrogate model was developed with the backpropagation neural network (BPNN) method to replace the RTM under the condition of uncertain parameters. Moreover, the simulated annealing optimisation model for ISLPP was created based on the proposed surrogate model. The optimal ISLPP was achieved that generated maximum profits from uranium recovery under different lixiviant prices, uranium prices and exploitation times. The optimal design framework of ISLPP based on the proposed ML algorithm was then applied in the Bayan-Uul sandstone-type uranium deposit in Inner Mongolia, China. From our analysis, it was demonstrated that the ML-based surrogate model exhibited great fitness with the RTM. The optimal results of the ISLPP indicated that the lixiviant concentration and injection rate could be adjusted based on the fluctuations in lixiviant price, uranium price and exploitation time. If the prices of sulphuric acid were high, a specific concentration of hydrogen peroxide could be injected into the injection well to promote the oxidation and dissolution of the uranium ore to increase the income from the uranium recovery. The optimisation model can also use the ISLPP scheme to boost the revenues from different lixiviant prices, uranium prices and exploitation times under the uncertainty of porosity, illustrating the applicability of the ML-based optimal design method of ISLPP in ISL mining. A general framework for developing surrogate models, as well as for conducting uncertainty analyses for a wide range of groundwater models was proposed here yielding valuable insights. |
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0022-1694 |
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THL @ christoph.kuells @ wang_machine_2023 |
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210 |
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