Abstract: Predicting groundwater levels is pivotal in curbing overexploitation and ensuring effective water resource governance. However, groundwater level prediction is intricate, driven by dynamic nonlinear factors. To comprehend the dynamic interaction among these drivers, leveraging machine learning models can provide valuable insights. The drastic increase in computational capabilities has catalysed a substantial surge in the utilisation of machine learning-based solutions for effective groundwater management. The performance of these models highly depends on the selection of hyperparameters. The optimisation of hyperparameters is a complex process that often requires application-specific expertise for a skillful prediction. To mitigate the challenge posed by hyperparameter tuning’s problem-specific nature, we present an innovative approach by introducing the automated machine learning (AutoML-GWL) framework. This framework is specifically designed for precise groundwater level mapping. It seamlessly integrates the selection of best machine learning model and adeptly fine-tunes its hyperparameters by using Bayesian optimisation. We used long time series (1997-2018) data of precipitation, temperature, evaporation, soil type, relative humidity, and lag of groundwater level as input features to train the AutoML-GWL model while considering the influence of Land Use Land Cover (LULC) as a contextual factor. Among these input features, the lag of groundwater level emerged as the most relevant input feature. Once the model is trained, it performs well over the unseen data with a strong correlation of coefficient (R = 0.90), low root mean square error (RMSE = 1.22), and minimal bias = 0.23. Further, we compared the performance of the proposed AutoML-GWL with sixteen benchmark algorithms comprising baseline and novel algorithms. The AutoML-GWL outperforms all the benchmark algorithms. Furthermore, the proposed algorithm ranked first in Friedman’s statistical test, confirming its reliability. Moreover, we conducted a spatial distribution and uncertainty analysis for the proposed algorithm. The outcomes of this analysis affirmed that the AutoML-GWL can effectively manage data with spatial variations and demonstrates remarkable stability when faced with small uncertainties in the input parameters. This study holds significant promise in revolutionising groundwater management practices by establishing an automated framework for simulating groundwater levels for sustainable water resource management.

Abstract: This study investigated the process of in situ leaching (ISL) method of uranium mining, and the removal of solid particles from the leaching solution. Investigations were carried out for 4months. The content of firm suspensions in the productive solutions arriving from the well field was up to standard of 3–5mg/l. After keeping in a settler of productive solutions within one hour concentration of suspensions decreases to 2–2.5mg/l. To increase the life of the wells requires more fine purification of the ISL solutions. The best results can be obtained but using filtration. Bag filters were used in experiments carried out at the extraction site. All samples of polypropylene bag filter was produced by the Tamfelt Corporation. The best results were obtained for fabrics S-51M03-L2K4 (pore size 3μm). After three month of trials following indicators of wells work were fixed: on the trial cell decrease in intake capacity did not occur; on the other cells of well field injectability of holes for the same period of time decreased for 15–40%. The results illustrated the high efficiency of this method, which allows injection wells to reach a constant intake capacity, making it possible for technological cells to achieve a constant productivity and balance. Purification of solutions allows to reduce acidulation term of new technological cells from 3–4 to 1.5–2months.

Abstract: The city of Alcudia de Crespins, in the centre of the Valencia province (east of Spain), has an exceptional water distribution system that in the past served fresh water to many houses in the town. This system is formed by more than one km of tunnels and underground cisterns, and dates probably in the late medieval times, while it has been in use and suffering modifications until 1955. This paper presents the structure and characteristics of such exceptional system, and explains the functioning parameters of the infrastructure.

Abstract: 4He concentrations in excess of the solubility equilibrium with the atmosphere by up to two to three orders of magnitude are observed in the Carrizo Aquifer in Texas, the Ojo Alamo and Nacimiento aquifers in the San Juan Basin, New Mexico, and the Auob Sandstone Aquifer in Namibia. A simple 4He accumulation model is applied to explain these excess 4He concentrations in terms of both in situ production and a crustal flux across the bottom layer of the aquifer. Results from the model simulations suggest variability in the 4He fluxes, ranging from 6×10−6 cm3 STP cm−2 yr−1 for the Auob Sandstone Aquifer to 3.6×10−7 cm3 STP cm−2 yr−1 for the Carrizo aquifer. For the Ojo Alamo and Nacimiento aquifers an intermediate value of 3×10−6 cm3 STP cm−2 yr−1 was estimated. The contribution of in-situ produced 4He to the measured concentrations was also estimated. This contribution is negligible for the Auob Sandstone Aquifer as compared with both the concentrations measured at the top and bottom of the aquifer for most of the pathway. In the Carrizo aquifer, in-situ produced 4He contributes 27.5% and 15.4%, to the total 4He observed at the top and bottom of the aquifer, respectively. For both aquifers of the San Juan Basin in-situ production almost entirely dominates the 4He concentrations at the top of the aquifer for most of the pathway. In contrast, the internal production is negligible as compared with the measured concentrations at the bottom of these aquifers, reaching, at most, 1.1%. The model simulations require an exponential decrease in the horizontal velocity of the water with increasing recharge distance to reproduce the distribution of 4He in these aquifers. For the Auob Sandstone Aquifer the highest range in the velocity values is obtained (25 to 0.4 m yr−1). The simulations for the Carrizo aquifer and both aquifers located in the San Juan Basin require velocities varying from 4 to 0.1 m yr−1, and from 2 to 0.3 m yr−1, respectively. For each aquifer, average permeability values were also estimated. They are generally in agreement with results obtained from pumping tests, hydrodynamic modeling and previous 14C measurements. On the basis of the results obtained by calibrating the model with the measured 4He concentrations, the mean water residence times were estimated. They agree reasonably well with 14C ages. When applied as chronologies for noble gas temperatures in the same aquifers, the calculated 4He ages allow the identification of three different climate periods similar to those previously identified using 14C ages: (1) the Holocene period (0–10 Ka BP), (2) the Last Glacial Maximum (≈18 Ka BP), and (3) the preceeding period (30–150 Ka BP).

Abstract: The concentrations of U in natural waters are usually low, being typically less than 4 μg/L in river water, around 3.3 μg/L in open seawater, and usually less than 5 μg/L in groundwater. Higher concentrations can occur in both surface water and groundwater and the range spans some six orders of magnitude, with extremes in the mg/L range. However, such extremes in surface water are rare and linked to localized mineralization or evaporation in alkaline lakes. High concentrations in groundwater, substantially above the WHO provisional guideline value for U in drinking water of 30 μg/L, are associated most strongly with (i) granitic and felsic volcanic aquifers, (ii) continental sandstone aquifers especially in alluvial plains and (iii) areas of U mineralization. High-U groundwater provinces are more common in arid and semi-arid terrains where evaporation is an additional factor involved in concentrating U and other solutes. Examples of granitic and felsic volcanic terrains with documented high U concentrations include several parts of peninsular India, eastern USA, Canada, South Korea, southern Finland, Norway, Switzerland and Burundi. Examples of continental sandstone aquifers include the alluvial plains of the Indo-Gangetic Basin of India and Pakistan, the Central Valley, High Plains, Carson Desert, Española Basin and Edwards-Trinity aquifers of the USA, Datong Basin, China, parts of Iraq and the loess of the Chaco-Pampean Plain, Argentina. Many of these plains host eroded deposits of granitic and felsic volcanic precursors which likely act as primary sources of U. Numerous examples exist of groundwater impacted by U mineralization, often accompanied by mining, including locations in USA, Australia, Brazil, Canada, Portugal, China, Egypt and Germany. These may host high to extreme concentrations of U but are typically of localized extent. The overarching mechanisms of U mobilization in water are now well-established and depend broadly on redox conditions, pH and solute chemistry, which are shaped by the geological conditions outlined above. Uranium is recognized to be mobile in its oxic, U(VI) state, at neutral to alkaline pH (7–9) and is aided by the formation of stable U–CO3(±Ca, Mg) complexes. In such oxic and alkaline conditions, U commonly covaries with other similarly controlled anions and oxyanions such as F, As, V and Mo. Uranium is also mobile at acidic pH (2–4), principally as the uranyl cation UO22+. Mobility in U mineralized areas may therefore occur in neutral to alkaline conditions or in conditions with acid drainage, depending on the local occurrence and capacity for pH buffering by carbonate minerals. In groundwater, mobilization has also been observed in mildly (Mn-) reducing conditions. Uranium is immobile in more strongly (Fe-, SO4-) reducing conditions as it is reduced to U(IV) and is either precipitated as a crystalline or ‘non-crystalline’ form of UO2 or is sorbed to mineral surfaces. A more detailed understanding of U chemistry in the natural environment is challenging because of the large number of complexes formed, the strong binding to oxides and humic substances and their interactions, including ternary oxide-humic-U interactions. Improved quantification of these interactions will require updating of the commonly-used speciation software and databases to include the most recent developments in surface complexation models. Also, given their important role in maintaining low U concentrations in many natural waters, the nature and solubility of the amorphous or non-crystalline forms of UO2 that result from microbial reduction of U(VI) need improved quantification. Even where high-U groundwater exists, percentage exceedances of the WHO guideline value are variable and often small. More rigorous testing programmes to establish usable sources are therefore warranted in such vulnerable aquifers. As drinking-water regulation for U is a relatively recent introduction in many countries (e.g. the European Union), testing is not yet routine or established and data are still relatively limited. Acquisition of more data will establish whether analogous aquifers elsewhere in the world have similar patterns of aqueous U distribution. In the high-U groundwater regions that have been recognized so far, the general absence of evidence for clinical health symptoms is a positive finding and tempers the scale of public health concern, though it also highlights a need for continued investigation.