Abstract: The Zoovch Ovoo uranium roll-front-type deposit is hosted in the Sainshand Formation, a Late Cretaceous siliciclastic reservoir, which constitutes the upper part of the post-rift infilling of the Mesozoic East Gobi Basin in SE Mongolia. The Sainshand Formation consists of unconsolidated medium-grained sand, silt and clay intervals deposited in fluvial-lacustrine settings. The uranium deposit is confined within a 60–80 m thick siliciclastic sequence inside aquifer-driven systems. The overall system experienced shallow burial and was never subjected to temperatures higher than 40 °C. This study proposes a comprehensive metallogenic model for this uranium deposit. Sedimentological and mineralogical observations from drill core samples to the microscopic scale (optical and Scanning Electron Microscopy) together with in situ geochemistry of late-formed phases (Laser Ablation–Inductively Coupled Plasma Mass Spectrometry, Electron Probe Microanalysis, Fourier Transform–Infrared Spectroscopy) were considered for the reconstruction of the main stages of U trapping. In the mineralized zone, the uranium ore is expressed as Ca–enriched uraninite (UO2) and less commonly as Ca–enriched phospho-coffinite (U, P)SiO4. Trapping mechanisms include i) complexation (i.e. uranyl-carboxyl complexes), ii) adsorption on organic or clay particles) and iii) reduction by pyrite and by bacterial activity to amorphous uraninite. In all cases, the organic matter plays either the role of trap for uranium or nutrient for bacteria that can trap uranium through their metabolism. The shallow burial diagenesis conditions do not allow direct reduction of U(VI) by organic carbon. The δ34S values of the iron disulfide are very diverse, fluctuating in extreme cases between −50 to + 50‰, with an average δ34S value for framboidal pyrite at 2‰, and −20‰ for euhedral pyrite. The positive and negative values reflect close versus open fractionation systems, while bacterial sulphate reduction (BSR) is active during the whole diagenetic history of the deposit as an essential source of reduced sulfur. Therefore, using detrital organic matter as a carbon source, microorganisms play a significant role in uranium trapping, either as a direct reducing agent for uranium or pyrite formation, which will trap uranium through redox driven epigenetic processes.

Abstract: Groundwater resources in the crystalline basement complex of India are crucial for supplying drinking water in both rural and urban settings. Groundwater depletion is recognised as a challenge across parts of India due to over-abstraction, but groundwater quality constraints are perhaps even more widespread and often overlooked at the local scale. Uranium contamination in basement aquifers has been reported in many parts of India, locally exceeding WHO drinking water guideline values of 30 μg/L and posing a potential health risk. In this study 130 water samples were collected across three crystalline basement catchments to assess hydrochemical, geological and anthropogenic controls on uranium mobility and occurrence in drinking water sources. Groundwaters with uranium concentrations exceeding 30 μg/L were found in all three study catchments (30% of samples overall), with concentrations up to 589 μg/L detected. There appears to be a geological control on the occurrence of uranium in groundwater with the granitic gneiss of the Halli and Bengaluru study areas having higher mean uranium concentrations (51 and 68 μg/L respectively) compared to the sheared gneiss of the Berambadi catchment (6.4 μg/L). Uranium – nitrate relationships indicate that fertiliser sources are not a major control on uranium occurrence in these case studies which include two catchments with a long legacy of intense agricultural land use. Geochemical modelling confirmed uranium speciation was dominated by uranyl carbonate species, particularly ternary complexes with calcium, consistent with uranium mobility being affected by redox controls and the presence of carbonates. Urban leakage in Bengaluru led to low pH and low bicarbonate groundwater hydrochemistry, reducing uranium mobility and altering uranium speciation. Since the majority of inhabitants in Karnataka depend on groundwater abstraction from basement aquifers for drinking water and domestic use, exposure to elevated uranium is a public health concern. Improved monitoring, understanding and treatment of high uranium drinking water sources in this region is essential to safeguard public health.

Abstract: The mining sector is at the edge of expanding to cater for natural resources that are much needed for technological development and manufacturing. Mushrooming of mines will consequently increase the number of mines closure. Moreover, mines closure have adverse impact on the environment at large and specifically on water resources. This study analyses historical groundwater quality parameters in mine intensive basin of Westrand Basin (WRB) to understand the status of groundwater quality in relation to mining activities and mine closure. Geographic information system (GIS) was used to map the spatio-temporal variation of groundwater quality in the basin and groundwater quality index (GQI) to evaluate its status. The coefficient of variation (CV) was applied to understand the stability of groundwater quality after the mine closure. Results indicated unstable and altered trend with increasing levels of acidity and salts concentration around the mines vicinity following the mine closure. The resultant maps indicated a significant deterioration of groundwater quality around the WRB with concentrations decreasing downstream. Obtained average GQI for the study period of 1996–2015 suggested a moderate groundwater quality at a range of GQI = 64–73. The CV indicated varying water quality at CV \textgreater 30% suggesting presence of source of contamination. Observed groundwater quality trends in Westrand basin suggested that mines closure present potential threat on groundwater quality and thus, a need for a robust mine closure plan and implementation.

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: A complete hydrogeological understanding of the deep Upper Jurassic carbonate aquifer in the South German Molasse Basin is essential for the future development of this important drinking water resource and geothermally used system. Water chemistry data, δ13CDIC, 14C of the dissolved inorganic carbon (14CDIC) and stable water isotope (δ18O and δD) measurements have been used to evaluate a promising groundwater dating approach with 14C of dissolved organic carbon (14CDOC). The pre-concentration of dissolved organic matter (DOM) was performed by the easy applicable solid phase extraction (SPE) with a styrene-divinylbenzene copolymer sorbent (PPL). Based on the sampling campaign of seven groundwater wells conducted between 2017 and 2019, it was shown that the groundwater is mainly of Ca–HCO3 type with some evidence of ion exchange between Ca2+ and Na+ at two of the investigated wells. The δD values ranged from −89.4‰ to −70.9‰ while δ18O values varied between −12.5‰ and −9.8‰. The obtained stable water isotope signatures indicated that the groundwater is of meteoric origin and was recharged during warm climate (Holocene), intermediate climate and cold climate (Pleistocene) infiltration conditions. The measured 14CDOC activities varied from 5.7 pmC to 51.1 pmC and the calculated piston-flow water ages (ORAs) ranged from 4200 years to 25,248 years using an initial 14C0DOC of 85 pmC. The calculated ORAs showed a very good correlation to the infiltration temperature-sensitive δ18O values which were affirmed with noble gas infiltration temperatures for two wells after Weise et al. (1991) and were also in good accordance with the atmospheric temperature record of the northern hemisphere from Dokken et al. (2015). The results reflect a consistent hydrogeological picture of the carbonate aquifer, which also supports the applicability of the SPE-PPL method for 14CDOC dating in groundwater with a low DOC content (<1 mg/l). In contrast, 14CDIC activities of 1.4 pmC to 21.3 pmC led to geochemically corrected piston-flow ages between 8057 years and >30,000 years and generally to an overestimation of the apparent water ages. This study gives insights into the promising approach of 14CDOC groundwater dating in carbonate aquifers with low DOC contents and allows future sustainable groundwater resource management of the investigated aquifer system.