| Records |
| Author |
Smedley, P.L.; Bearcock, J.M.; Ward, R.S.; Crewdson, E.; Bowes, M.J.; Darling, W.G.; Smith, A.C. |
| Title |
Monitoring of methane in groundwater from the Vale of Pickering, UK: Temporal variability and source discrimination |
Type |
Journal Article |
| Year |
2023 |
Publication |
Chemical Geology |
Abbreviated Journal |
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| Volume |
636 |
Issue |
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Pages |
121640 |
Keywords  |
Aquifer, Biogenic, Ethane, Hydrocarbons, Methane, Shale gas |
| 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. |
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0009-2541 |
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THL @ christoph.kuells @ smedley_monitoring_2023 |
Serial |
172 |
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Boumaiza, L.; Ammar, S.B.; Chesnaux, R.; Stotler, R.L.; Mayer, B.; Huneau, F.; Johannesson, K.H.; Levison, J.; Knöller, K.; Stumpp, C. |
| Title |
Nitrate sources and transformation processes in groundwater of a coastal area experiencing various environmental stressors |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Environmental Management |
Abbreviated Journal |
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| Volume |
345 |
Issue |
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Pages |
118803 |
| Keywords |
Aquifer, Denitrification, MixSIAR, Nitrate, Nitrification, Stable isotopes |
| Abstract |
In coastal salinized groundwater systems, contamination from various nitrate (NO3) inputs combined with complex hydrogeochemical processes make it difficult to distinguish NO3 sources and identify potential NO3 transformtation processes. Effective field-based NO3 studies in coastal areas are needed to improve the understanding of NO3 contamination dynamics in groundwater of such complex coastal systems. This study focuses on a typical Mediterranean coastal agricultural area, located in Tunisia, experiencing substantial NO3 contamination from multiple anthropogenic sources. Here, multiple isotopic tracers (δ18OH2O, δ2HH2O, δ15NNO3, δ18ONO3, and δ11B) combined with a Bayesian isotope MixSIAR model are used (i) to identify the major NO3 sources and their contributions, and (ii) to describe the potential NO3 transformation processes. The measured NO3 concentrations in groundwater are above the natural baseline threshold, suggesting anthropogenic influence. The measured isotopic composition of NO3 indicates that manure, soil organic matter, and sewage are the potential sources of NO3, while δ11B values constrain the NO3 contamination to manure; a finding that is supported by the results of MixSIAR model revealing that manure-derived NO3 dominates over other likely sources. Nitrate derived from manure in the study area is attributed to organic fertilizers used to promote crop growth, and livestock that deposit manure directly on the ground surface. Evidence for ongoing denitrification in groundwaters of the study area is supported by an enrichment in both 15N and 18O in the remaining NO3, although isotopic mass balances between the measured and the theoretical δ18ONO3 values also suggest the occurrence of nitrification. The simultaneous occurrence of these biogeochemical processes with heterogeneous distribution across the study area reflect the complexity of interactions within the investigated coastal aquifer. The multiple isotopic tracer approach used here can identify the effect of multiple NO3 anthropogenic activities in coastal environments, which is fundamental for sustainable groundwater resources management. |
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0301-4797 |
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THL @ christoph.kuells @ boumaiza_nitrate_2023 |
Serial |
170 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
| Title |
Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
Type |
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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Holmes, M.; Campbell, E.E.; Wit, M. de; Taylor, J.C. |
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Can diatoms be used as a biomonitoring tool for surface and groundwater?: Towards a baseline for Karoo water |
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Journal Article |
| Year |
2023 |
Publication |
South African Journal of Botany |
Abbreviated Journal |
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161 |
Issue |
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Pages |
211-221 |
| Keywords |
Bioindicator, Diatom, Hydraulic fracturing, Karoo, Water quality |
| Abstract |
The environmental risks from shale gas extraction through the unconventional method of ‘fracking’ are considerable and impact on water supplies below and above ground. Since 2010 the recovery of natural shale gas through fracking has been proposed in parts of the fragile semi-arid ecosystems that make up the Karoo biome in South Africa. These unique ecosystems are heavily reliant on underground water, intermittent and ephemeral springs, which are at great risk of contamination by fracking processes. Diatoms are present in all water bodies and reflect aspects of the environment in which they are located. As the possibility of fracking has not been removed, the aim of the project was to determine if diatoms could be used for rapid biomonitoring of underground and surface waters in the Karoo. Over a period of 24 months, water samples and diatom species were collected simultaneously from 65 sites. A total of 388 diatom taxa were identified from 290 samples with seasonal and substrate variation affecting species composition but not the environmental information. Species diversity information, on the other hand, often varied significantly between substrates within a single sample. Analysis using CCA established that the diatom composition was affected by lithium, oxidized nitrogen, electrical conductivity, and sulphate levels in the sampled water. We conclude that changes in diatom community composition in the Karoo do reflect the water chemistry and could be useful as bioindicators. |
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0254-6299 |
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THL @ christoph.kuells @ holmes_can_2023 |
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163 |
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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 |
Type |
Journal Article |
| Year |
2023 |
Publication |
Applied Geochemistry |
Abbreviated Journal |
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| Volume |
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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no |
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THL @ christoph.kuells @ qiu_what_2023 |
Serial |
207 |
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