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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.
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Tisherman, R. A., Rossi, R. J., Shonkoff, S. B. C., & DiGiulio, D. C. (2023). Groundwater uranium contamination from produced water disposal to unlined ponds in the San Joaquin Valley. Science of The Total Environment, 904, 166937.
Abstract: In the southern San Joaquin Valley (SJV) of California, an agriculturally productive region that relies on groundwater for irrigation and domestic water supply, the infiltration of produced water from oil reservoirs is known to impact groundwater due to percolation from unlined disposal ponds. However, previously documented impacts almost exclusively focus on salinity, while contaminant loadings commonly associated with produced water (e.g., radionuclides) are poorly constrained. For example, the infiltration of bicarbonate-rich produced waters can react with sediment-bound uranium (U), leading to U mobilization and subsequent transport to nearby groundwater. Specifically, produced water infiltration poses a particular concern for SJV groundwater, as valley-fill sediments are well documented to be enriched in geogenic, reduced U. Here, we analyzed monitoring well data from two SJV produced water pond facilities to characterize U mobilization and subsequent groundwater contamination. Groundwater wells installed within 2 km of the facilities contained produced water and elevated levels of uranium. There are \textgreater400 produced water disposal pond facilities in the southern SJV. If our observations occur at even a fraction of these facilities, there is the potential for widespread U contamination in the groundwaters of one of the most productive agricultural regions in the world.
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Musy, S., & Purtschert, R. (2023). Reviewing 39Ar and 37Ar underground production in shallow depths with implications for groundwater dating. Science of The Total Environment, 884, 163868.
Abstract: Argon-37 (37Ar) and Argon-39 (39Ar) are used for groundwater dating on timescales from weeks to centuries. For both isotopes, the quantification of underground sources is essential to accurately infer water residence times from sampled dissolved activities. Subsurface production resulting from interactions with neutrons from the natural radioactivity in rocks and with primary cosmogenic neutrons has been known for a long time. More recently, the capture of slow negative muons and reactions with muon-induced neutrons were documented for 39Ar subsurface production in the context of underground particle detectors (e.g. for Dark Matter research). However, the contribution from these particles was never considered for groundwater dating applications. Here, we reevaluate the importance of all potential depth-related production channels at depth ranges relevant for 39Ar groundwater dating [0 − 200 meters below the surface (m.b.s)]. The production of radioargon by muon-induced processes is considered in this depth range for the first time. The uncertainty on the total depth-dependent production rate is estimated with Monte Carlo simulations assuming a uniform distribution of the parameter uncertainties. This work aims to provide a comprehensive framework for interpreting 39Ar activities in terms of groundwater residence times and for exposure age dating of rocks. The production of 37Ar is also addressed since this isotope is relevant as a proxy for 39Ar production, for the timing of river-groundwater exchanges, and in the context of on-site inspections (OSI) within the verification framework of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). In this perspective, we provide an interactive web-based application for the calculation of 37Ar and 39Ar production rates in rocks.
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Liu, Z., Li, C., Tan, K., Li, Y., Tan, W., Li, X., et al. (2023). Study of natural attenuation after acid in situ leaching of uranium mines using isotope fractionation and geochemical data. Science of The Total Environment, 865, 161033.
Abstract: Acid in situ leaching (AISL) is a subsurface mining approach suitable for low-grade ores which does not generate tailings, and has been adopted widely in uranium mining. However, this technique causes an extremely high concentration of contaminants at post-mining sites and in the surroundings soon after the mining ceases. As a potential AISL remediation strategy, natural attenuation has not been studied in detail. To address this problem, groundwater collected from 26 wells located within, adjacent, upgradient, and downgradient of a post-mining site were chosen to analyze the fate of U(VI), SO42−, δ34S, and δ238U, to reveal the main mechanisms governing the migration and attenuation of the dominant contaminants and the spatio-temporal evolutions of contaminants in the confined aquifer of the post-mining site. The δ238U values vary from −0.07 ‰ to 0.09 ‰ in the post-mining site and from −1.43 ‰ to 0.03 ‰ around the post-mining site. The δ34S values were found to vary from 3.3 ‰ to 6.2 ‰ in the post-mining site and from 6.0 ‰ to 11.0 ‰ around the post-mining site. Detailed analysis suggests that there are large differences between the range of isotopic composition variation and the range of pollutants concentration distribution, and the estimated Rayleigh isotope fractionation factor is 0.9994–0.9997 for uranium and 1.0032–1.0061 for sulfur. The isotope ratio of uranium and sulfur can be used to deduce the migration history of the contaminants and the irreversibility of the natural attenuation process in the anoxic confined aquifer. Combining the isotopic fractionation data for U and S with the concentrations of uranium and sulfate improved the accuracy of understanding of reducing conditions along the flow path. The study also indicated that as long as the geological conditions are favorable for redox reactions, natural attenuation could be used as a cost-effective remediation scheme.
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Jroundi, F., Povedano-Priego, C., Pinel-Cabello, M., Descostes, M., Grizard, P., Purevsan, B., et al. (2023). Evidence of microbial activity in a uranium roll-front deposit: Unlocking their potential role as bioenhancers of the ore genesis. Science of The Total Environment, 861, 160636.
Abstract: Uranium (U) roll-front deposits constitute a valuable source for an economical extraction by in situ recovery (ISR) mining. Such technology may induce changes in the subsurface microbiota, raising questions about the way their activities could build a functional ecosystem in such extreme environments (i.e.: oligotrophy and high SO4 concentration and salinity). Additionally, more information is needed to dissipate the doubts about the microbial role in the genesis of such U orebodies. A U roll-front deposit hosted in an aquifer driven system (in Zoovch Ovoo, Mongolia), intended for mining by acid ISR, was previously explored and showed to be governed by a complex bacterial diversity, linked to the redox zonation and the geochemical conditions. Here for the first time, transcriptional activities of microorganisms living in such U ore deposits are determined and their metabolic capabilities allocated in the three redox-inherited compartments, naturally defined by the roll-front system. Several genes encoding for crucial metabolic pathways demonstrated a strong biological role controlling the subsurface cycling of many elements including nitrate, sulfate, metals and radionuclides (e.g.: uranium), through oxidation-reduction reactions. Interestingly, the discovered transcriptional behaviour gives important insights into the good microbial adaptation to the geochemical conditions and their active contribution to the stabilization of the U ore deposits. Overall, evidences on the importance of these microbial metabolic activities in the aquifer system are discussed that may clarify the doubts on the microbial role in the genesis of low-temperature U roll-front deposits, along the Zoovch Ovoo mine.
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Veerasamy, N., Kasar, S., Murugan, R., Inoue, K., Natarajan, T., Ramola, R. C., et al. (2023). 234U/238U disequilibrium and 235U/238U ratios measured using MC-ICP-MS in natural high background radiation area soils to understand the fate of uranium. Chemosphere, 323, 138217.
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.
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Wang, B., Luo, Y., Qian, J. -zhong, Liu, J. -hui, Li, X., Zhang, Y. -hong, et al. (2023). Machine learning–based optimal design of the in-situ leaching process parameter (ISLPP) for the acid in-situ leaching of uranium. Journal of Hydrology, 626, 130234.
Abstract: 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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Johnson, R. S. H., & Alila, Y. (2023). Nonstationary stochastic paired watershed approach: Investigating forest harvesting effects on floods in two large, nested, and snow-dominated watersheds in British Columbia, Canada. Journal of Hydrology, 625, 129970.
Abstract: Drawing on advances in nonstationary frequency analysis and the science of causation and attribution, this study employs a newly developed nonstationary stochastic paired watershed approach to determine the effect of forest harvesting on snowmelt-generated floods. Moreover, this study furthers the application of stochastic physics to evaluate the environmental controls and drivers of flood response. Physically-based climate and time-varying harvesting data are used as covariates to drive the nonstationary flood frequency distribution parameters to detect, attribute, and quantify the effect of harvesting on floods in the snow-dominated Deadman River (878 km2) and nested Joe Ross Creek (99 km2) watersheds. Harvesting only 21% of the watershed caused a 38% and 84% increase in the mean but no increase in variability around the mean of the frequency distribution in the Deadman River and Joe Ross Creek, respectively. Consequently, the 7-year, 20-year, 50-year, and 100-year flood events became approximately two, four, six, and ten times more frequent in both watersheds. An increase in the mean is posited to occur from an increase in moisture availability following harvest from suppressed snow interception and increased net radiation reaching the snowpack. Variability was not increased because snowmelt synchronization was inhibited by the buffering capacity of abundant lakes, evenly distributed aspects, and widespread spatial distribution of cutblocks in the watersheds, preventing any potential for harvesting to increase the efficiency of runoff delivery to the outlet. Consistent with similar recent studies, the effect of logging on floods is controlled not only by the harvest rate but most importantly the physiographic characteristics of the watershed and the spatial distribution of the cutblocks. Imposed by the probabilistic framework to understanding and predicting the relation between extremes and their environmental controls, commonly used in the general sciences but not forest hydrology, it is the inherent nature of snowmelt-driven flood regimes which cause even modest increases in magnitude, especially in the upper tail of the distribution, to translate into surprisingly large changes in frequency. Contrary to conventional wisdom, harvesting influenced small, medium, and very large flood events, and the sensitivity to harvest increased with increasing flood event size and watershed area.
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Gimeno, M. J., Tullborg, E. - L., Nilsson, A. - C., Auqué, L. F., & Nilsson, L. (2023). Hydrogeochemical characterisation of the groundwater in the crystalline basement of Forsmark, the selected area for the geological nuclear repositories in Sweden. Journal of Hydrology, 624, 129818.
Abstract: Numerous groundwater analyses from the crystalline bedrock in the Forsmark area have been performed between 2002 and 2019, together with thorough geological, geophysical, and hydrogeological studies, within the site investigations carried out by the Swedish Nuclear Fuel and Waste Management Company. The groundwater samples have been taken from boreholes down to ≈ 1000 m and the analysis include major- and trace-elements, stable and radiogenic isotopes, gases and microbes. The chemical and isotopic composition of these groundwaters evidences the presence of non-marine brackish to saline groundwaters with very long residence times (many hundreds of thousands of years) and a series of complex mixing events resulting from the recharge of different waters over time: glacial meltwaters, probably from different glaciations of which the latest culminated some 20,000 years ago, and marine waters from the Baltic starting some 7000 years ago. Later, meteoric water and present Baltic Sea water have recharged in different parts of the upper 100 m. These mixing events have also triggered chemical and microbial reactions that have conditioned some of the important groundwater parameters and, together with the structural complexity of the area, they have promoted a heterogeneous distribution of groundwater compositions in the bedrock. Due to these evident differences in chemistry, residence time and origin of the groundwater, several groundwater types were defined in order to facilitate the visualisation and communication. The differentiation (linked to the paleohydrological history of the area) was based on Cl concentration, Cl/Mg ratio (marine component), and δ18O value (glacial component). The work presented in this paper increases the understanding of the groundwater evolution in fractured and compartmentalised aquifers where mixing processes are the most important mechanisms. The model proposed to characterise the present groundwater system of the Forsmark area will also help to predict the future hydrogeochemical behaviour of the groundwater system after the construction of the repositories for the nuclear wastes.
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Heidari, B., Prideaux, V., Jack, K., & Jaber, F. H. (2023). A planning framework to mitigate localized urban stormwater inlet flooding using distributed Green Stormwater Infrastructure at an urban scale: Case study of Dallas, Texas. Journal of Hydrology, 621, 129538.
Abstract: Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the “very high” severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices.
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