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| Author |
Rusli, S.R.; Weerts, A.H.; Mustafa, S.M.T.; Irawan, D.E.; Taufiq, A.; Bense, V.F. |
| Title |
Quantifying aquifer interaction using numerical groundwater flow model evaluated by environmental water tracer data: Application to the data-scarce area of the Bandung groundwater basin, West Java, Indonesia |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Hydrology: Regional Studies |
Abbreviated Journal |
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| Volume |
50 |
Issue |
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Pages |
101585 |
| Keywords |
Aquifer interaction, Multi-layer groundwater abstraction, Environmental water tracers, Groundwater flow model, Bandung groundwater basin |
Abstract  |
Study Region: Bandung groundwater basin, Indonesia. Study focus: Groundwater abstraction of various magnitudes, pumped out from numerous depths in a multitude of layers of aquifers, stimulates different changes in hydraulic head distribution, including ones under vertical cross-sections. This generates groundwater flow in the vertical direction, where groundwater flows within its storage from the shallow to the underlying confined aquifers. In the Bandung groundwater basin, previous studies have identified such processes, but quantitative evaluations have never been conducted, with data scarcity mainly standing as one of the major challenges. In this study, we utilize the collated (1) environmental water tracer data, including major ion elements (Na+/K+, Ca2＋, Mg2+, Cl−, SO42−,HCO3−), stable isotope data (2H and δ18O), and groundwater age determination (14C), in conjunction with (2) groundwater flow modeling to quantify the aquifer interaction, driven mainly by the multi-layer groundwater abstraction in the Bandung groundwater basin, and demonstrate their correspondence. In addition, we also use the model to quantify the impact of multi-layer groundwater abstraction on the spatial distribution of the groundwater level changes. New hydrological insights for the region: In response to the limited calibration data availability, we expand the typical model calibration that makes use of the groundwater level observations, with in-situ measurement and a novel qualitative approach using the collated environmental water tracers (EWT) data for the model evaluation. The analysis in the study area using EWT data and quantitative methods of numerical groundwater flow modeling is found to collaborate with each other. Both methods show agreement in their assessment of (1) the groundwater recharge spatial distribution, (2) the regional groundwater flow direction, (3) the groundwater age estimates, and (4) the identification of aquifer interaction. On average, the downwelling to the deeper aquifer is quantified at 0.110 m/year, which stands out as a significant component compared to other groundwater fluxes in the system. We also determine the unconfined aquifer storage volume decrease, calculated from the change in the groundwater table, resulting in an average declining rate of 51 Mm3/year. This number shows that the upper aquifer storage is dwindling at a rate disproportionate to its groundwater abstraction, hugely influenced by losses to the deeper aquifer. The outflow to the deeper aquifer contributes to 60.3% of the total groundwater storage lost, despite representing only 32.3% of the total groundwater abstraction. This study shows the possibility of quantification of aquifer interaction and groundwater level change dynamics driven by multi-layer groundwater abstraction in a multi-layer hydrogeological setting, even in a data-scarce environment. Applying such methods can assist in deriving basin-scale groundwater policies and management strategies under the changing anthropogenic and climatic factors, thereby ensuring sustainable groundwater management. |
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2214-5818 |
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THL @ christoph.kuells @ Rusli2023101585 |
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222 |
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Veerasamy, N.; Kasar, S.; Murugan, R.; Inoue, K.; Natarajan, T.; Ramola, R.C.; Fukushi, M.; Sahoo, S.K. |
| Title |
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 |
Type |
Journal Article |
| Year |
2023 |
Publication |
Chemosphere |
Abbreviated Journal |
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| Volume |
323 |
Issue |
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Pages |
138217 |
| Keywords |
HBRA, MC-ICP-MS, Monazites, U/U, Uranium |
| 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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0045-6535 |
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THL @ christoph.kuells @ veerasamy_234u238u_2023 |
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149 |
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Qiu, W.; Yang, Y.; Song, J.; Que, W.; Liu, Z.; Weng, H.; Wu, J.; Wu, J. |
| Title |
What chemical reaction dominates the CO2 and O2 in-situ uranium leaching?: Insights from a three-dimensional multicomponent reactive transport model at the field scale |
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Journal Article |
| Year |
2023 |
Publication |
Applied Geochemistry |
Abbreviated Journal |
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148 |
Issue |
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Pages |
105522 |
| Keywords |
Carbonate minerals, In-situ leaching (ISL) of uranium, Pyrite oxidation, Reactive transport modeling (RTM) |
| Abstract |
The complex behavior of uranium in recovery is mostly driven by water-rock interactions following lixiviant injection into ore-bearing aquifers. Significant challenges exist in exploring the geochemical processes responsible for uranium release and mobilization. Herein this study provides an illustration of a ten-year field scale CO2 and O2 in-situ leaching (ISL) process at a typical sandstone-hosted uranium deposit in northern China. We also conducte a three-dimensional (3-D) multicomponent reactive transport model to assess the effects of potential chemical reactions on uranium recovery, in particular, to focus on the role of sulfide mineral pyrite (FeS2). Numerical simulations are performed considering three potential ISL reaction pathways to determine the relative contributions to uranium release, and the results indicate that bicarbonate promotes the oxidative dissolution of uranium-bearing minerals and further accelerates the uranium leaching in a neutral geochemical system. Moreover, the presence of FeS2 exerts a strong competitive role in the uranium-bearing mineral dissolution by increasing oxygen consumption, favoring the formation of iron oxyhydroxide, and therefore causing an associated decrease in uranium recovery rates. The simulation model demonstrates that dissolution of carbonate neutralizes acidic water generated from pyrite oxidation and aqueous CO2 dissociation. In addition, the cation concentrations (i.e., Ca and Mg) are increasing in the pregnant solutions, showing that the recycling of lixiviants and kinetic dissolution of carbonate generates a larger number of dissolved Ca and Mg and inevitably triggers the secondary dolomite mineral precipitation. The findings improve our fundamental understanding of the geochemical processes in a long-term uranium ISL system and provide important environmental implications for the optimal design of uranium recovery, remediation, and risk exposure assessment. |
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0883-2927 |
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THL @ christoph.kuells @ qiu_what_2023 |
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207 |
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Smedley, P.L.; Kinniburgh, D.G. |
| Title |
Uranium in natural waters and the environment: Distribution, speciation and impact |
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Journal Article |
| Year |
2023 |
Publication |
Applied Geochemistry |
Abbreviated Journal |
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148 |
Issue |
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105534 |
| Keywords |
Drinking water, Mine water, NORM, Radionuclide, Redox, U isotopes, Uranium, Uranyl |
| 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. |
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0883-2927 |
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THL @ christoph.kuells @ smedley_uranium_2023 |
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118 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
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Journal Article |
| Year |
2023 |
Publication |
Systems and Soft Computing |
Abbreviated Journal |
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5 |
Issue |
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Pages |
200049 |
| Keywords |
Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
| 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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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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