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.

Abstract: Groundwater abstracted from aquifers in the Vale of Pickering, North Yorkshire, UK and monitored over the period 2015–2022, shows evidence of variable but commonly high concentrations of dissolved CH4. Sampled groundwater from the Jurassic organic-rich Kimmeridge Clay Formation (boreholes up to 180 m depth) has concentrations up to 57 mg/L, and concentrations up to 59 mg/L are found in groundwater from underlying confined Corallian Group limestone (borehole depths 50–227 m). The high concentrations are mainly from boreholes in the central parts of the vale. Small concentrations of ethane (C2H6, up to 800 μg/L) have been found in the Kimmeridge Clay and confined Corallian groundwaters, and of propane (C3H8, up to 160 μg/L) in deeper boreholes (110–180 m) from these formations. The concentrations are typically higher in groundwater from the deeper boreholes and vary with hydrostatic pressure, reflecting the pressure control on CH4 solubility. The occurrences contrast with groundwater from shallow Quaternary superficial deposits which have low CH4 concentrations (up to 0.39 mg/L), and with the unconfined and semi-confined sections of the Corallian aquifer (up to 0.7 mg/L) around the margins of the vale. Groundwater from the Quaternary, Kimmeridge Clay formations and to a small extent the confined Corallian aquifer, supports local private-water supplies, that from the peripheral unconfined sections of Corallian also supports public supply for towns and villages across the region. Dissolved methane/ethane (C1/C2) ratios and stable-isotopic compositions (δ13C-CH4, δ2H-CH4 and δ13C-CO2) suggest that the high-CH4 groundwater from both the Kimmeridge Clay and confined Corallian formations derives overwhelmingly from biogenic reactions, the methanogenesis pathway by CO2 reduction. A small minority of groundwater samples shows a more enriched δ13C-CH4 composition (−50 to −44 ‰) which has been interpreted as due to anaerobic or aerobic methylotrophic oxidation in situ or post-sampling oxidation, rather than derivation by a thermogenic route. Few of the existing groundwater sites are proximal to abandoned or disused conventional hydrocarbon wells that exist in the region, and little evidence has been found for an influence on groundwater dissolved gases from these sites. The Vale of Pickering has also been under recent consideration for development of an unconventional hydrocarbon (shale-gas) resource. In this context, the monitoring of dissolved gases has been an important step in establishing the high-CH4 baseline of groundwaters from Jurassic deposits in the region and in apportioning their sources and mechanisms of genesis.

Abstract: This synthesis on groundwater recharge targets the Southern African region as a result of the dependence of the community and economic sector on the groundwater resource. Several literature based recharge studies were collected and assessed in order to find out the main controls to the occurrence of recharge. The Water Table Fluctuation and Base flow separation methods have been tested in the catchment that drains crystalline basement rocks and dolostones close to the city of Johannesburg, South Africa. Based on the assessed data the Chloride Mass Balance method resulted in groundwater recharge of less than 4% of the rainfall, while it reaches 20%, when rainfall exceeds 600mm. For the classical water balance method, recharge proportion is less than 3% of rainfall as a result of very high ambient temperature in the region. Based on the Saturated Volume Fluctuation and Water Table Fluctuation methods, recharge could be less than 6% for annual rainfall of less than 600mm. Observational results further suggest that sporadic recharge from high intensity rainfall has important contribution to the groundwater recharge in the region, owing to the presence of permeable geological cover, which could not be fully captured by most of the recharge estimation methods. This study further documents an evaluation of the most reliable recharge estimation methods in the area such as the chloride mass balance, saturated volume fluctuation and water table fluctuation methods in order to successfully manage the groundwater resource.

Abstract: Most of the World’s germanium (Ge) is mined from Ge-rich lignite, where it is commonly associated with elevated arsenic (As), tungsten (W) and beryllium (Be) contents. Over the past decade, new evidence showing that World-class Ge deposits result from the interaction of hydrothermal fluids with organic matter in coal seams has emerged. Yet, the chemical state of Ge and associated metals in lignite remains poorly understood. We used Mega-pixel Synchrotron X-ray Fluorescence (MSXRF), X-ray Absorption Near Edge Structure (XANES) and Extended X-ray Absorption Fine Structure (EXAFS) to characterize the oxidation states and chemical bonding environment of Ge, As, and W in two world-class Mesozoic Ge-in-lignite deposits (Lincang, Yunnan, southwestern China; Wulantuga, Inner Mongolia, northeastern China); in lignite-bearing uranium (U) ores from the Beverley deposit (South Australia) hosted in Eocene sandstones; and in lignite and preserved wood in late Oligocene-Miocene fluviatile sediments (Gore, Southland, New Zealand). The aim was to improve our understanding of the enrichment mechanism of Ge in lignite and better evaluate the environmental mobility of Ge and some of the associated metals (specifically As and W) in lignite ores. In all samples, chemical maps show that Ge is distributed homogeneously (down to 2μm) within the organic matter. XANES and EXAFS data show that Ge exists in the tetravalent oxidation state and in a distorted octahedral coordination with O, consistent with complexing of Ge by organic ligands. In some pyrite-bearing samples, a minor fraction of Ge is also present as Ge(IV) in association with pyrite. In contrast, As displays a more complex speciation pattern, sometimes even in a single sample, including As(III), As(V), and As(−I/+II) in solid solution in sulfides. Arsenic in sulfides occurs in anionic and cationic forms, i.e., it shows both the common substitution for S22− and the substitution for Fe recently discovered in some hydrothermal pyrites. Tungsten was present as W(VI) in distorted octahedral (3+3) coordination. The EXAFS data confirm the absence or minor contribution of individual W-rich minerals such as scheelite or ferberite to W mass balance in the studied samples. These data show that Ge, W, and probably some As are scavenged via formation of insoluble, oxygen-bridged metal organic complexes in lignite. Destruction of the organic ligands responsible for fixing Ge and W (As) in these lignites is required for liberating the metals, e.g. from waste materials. Geochemical modelling suggests that Ge, W, Be and As all can be extracted from granitic rocks by dilute, low temperature hydrothermal fluids. Germanium is transported mainly as the tetrahedral Ge(OH)4(aq) complex, but fixed as an octahedral oxy-bridged organic complex. The same situation is valid for W, which is transported at the tetrahedral tungstate ion, but most likely scavenged via formation of a 6-coordinated metal-organic species. The Ge-Be-W±As association in Ge-rich coals reflects the source of the metals as well as related scavenging mechanisms.

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.