Abstract: The Chhatrapur-Gopalpur coastal area in Odisha, India is a well-known natural high background radiation (HBRA) area due to the abundance of monazite (a thorium bearing radioactive mineral) in beach sands and soils. Recent studies on Chhatrapur-Gopalpur HBRA groundwater have reported high concentrations of uranium and its decay products. Therefore, the soils of the Chhatrapur-Gopalpur HBRA are reasonably suspected as the sources of these high uranium concentrations in groundwater. In this report, first the uranium concentrations in soil samples were measured using inductively coupled plasma mass spectrometry (ICP-MS) and they were found to range from 0.61 ± 0.01 to 38.59 ± 0.16 mg kg−１. Next, the 234U/238U and 235U/238U isotope ratios were measured to establish a baseline for the first time in Chhatrapur-Gopalpur HBRA soil. Multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) was used for measurement of these isotope ratios. The 235U/238U ratio was observed to be the normal terrestrial value. The 234U/238U activity ratio, was calculated to understand the secular equilibrium between 234U and 238U in soil and it varied from 0.959 to 1.070. To understand the dynamics of uranium in HBRA soil, physico-chemical characteristics of soil were correlated with uranium isotope ratios and this correlation of 234U/238U activity ratio indicated the leaching of 234U from Odisha HBRA soil.

Abstract: Acid mine water from in situ chemical leaching of uranium (Straz pod Ralskem, Czech Republic) was treated in laboratory scale experiments by zero-valent iron nanoparticles (nZVI). For the first time, nZVI were applied for the treatment of the real acid water system containing the miscellaneous mixture of pollutants, where the various removal mechanisms occur simultaneously. Toxicity of the treated saline acid water is caused by major contaminants represented by aluminum and sulphates in a high concentration, as well as by microcontaminants like As, Be, Cd, Cr, Cu, Ni, U, V, and Zn. Laboratory batch experiments proved a significant decrease in concentrations of all the monitored pollutants due to an increase in pH and a decrease in oxidation–reduction potential related to an application of nZVI. The assumed mechanisms of contaminants removal include precipitation of cations in a lower oxidation state, precipitation caused by a simple pH increase and co-precipitation with the formed iron oxyhydroxides. The possibility to control the reaction kinetics through the nature of the surface stabilizing shell (polymer vs. FeO nanolayer) is discussed as an important practical aspect.

Abstract: 81Kr and 36Cl can both be used to date groundwater beyond the dating range of 14C. 81Kr usually provides reliable groundwater ages because it has uniform initial distribution and negligible subsurface generation, while 36Cl is commonly influenced by subsurface sources or “dead” chloride dissolution. Therefore, the combined use of 81Kr and 36Cl could provide clues on the evolution history of groundwater. In the present study, we performed 36Cl and 81Kr dating of geothermal water in Weihe Basin of China and interpreted the possible cause of disagreement. Two distinct water masses were identified with distinctive isotopic signals: groundwater with significant δ18O shifts (up to −2.0‰), dissolved dead Cl and ages < 1.0 Ma (Cluster A), and older water with little δ18O shifts, negligible dissolved Cl and ages >1.0 Ma (Cluster B). The results confirm the eastward flow path of Cluster B to the Ancient Sanmen Lake with an increasing trend of Cl concentration and age. Modern recharge from the mountains flows to the basin center with intense interaction between water and carbonate under respective reservoir temperatures (100 ∼ 130 °C). These waters flow through the saline stratum emerging from the spillover of the Ancient Sanmen Lake, resulting in higher dead Cl dissolution. A significant linear relationship is observed with the older end-member of ∼1.3Ma under the topographically-driven faster circulation effect. 81Kr ages seem to support the hypothesis that the birth of the modern Yellow River was at about 1.0–1.3 Ma. We inferred the drainage reorganization from the Ancient Sanmen Lake to the modern Yellow River since the Mid-Pleistocene Transition induced the change in groundwater dynamics as well as its chemical evolution. The excavation of the Ancient Sanmen Lake and the accentuated incision of the Weihe River induced groundwater gradient, and therefore the recharge from precipitation from both slopes of the Qinling Mountains in the south and the Beishan Mountains in the north. Our results highlight the effects of dead Cl on 36Cl dating and demonstrate the significant impact of catchment reorganization on groundwater dynamics and its chemistry.

Abstract: Water harvesting is the intentional collection and concentration of rainwater and runoff to offset irrigation demands. Secondary benefits include decreased flood and erosion risk. Water harvesting techniques include micro- and macro-catchment systems, floodwater harvesting, and rooftop and groundwater harvesting. The techniques vary with catchment type and size, and the method of water storage. Micro-catchment water harvesting, for example, requires the development of small structures and targets increased water delivery and storage to the root zone whereas macro-catchment systems collect runoff water from large areas. The sustainability of water harvesting techniques at the local level are usually constrained by several factors such as labor, construction costs, loss of productive land, and maintenance, suggesting that multiple solutions are required to sustain the benefits of water harvesting techniques.

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.