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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. |
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Title |
Nitrate sources and transformation processes in groundwater of a coastal area experiencing various environmental stressors |
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2023 |
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Journal of Environmental Management |
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345 |
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118803 |
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Aquifer, Denitrification, MixSIAR, Nitrate, Nitrification, Stable isotopes |
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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 |
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170 |
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Abiye, T. |
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Title |
Synthesis on groundwater recharge in Southern Africa: A supporting tool for groundwater users |
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2016 |
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Groundwater for Sustainable Development |
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2-3 |
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182-189 |
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Arid and semi-arid areas, Groundwater recharge, Recharge estimation methods, Southern Africa |
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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. |
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2352-801x |
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THL @ christoph.kuells @ abiye_synthesis_2016 |
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101 |
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Kamash, Z. |
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Irrigation technology, society and environment in the Roman Near East |
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2012 |
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Journal of Arid Environments |
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86 |
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65-74 |
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Army, Urbanism, Qanats, Dams, Field systems, Irrigation channels |
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This paper uses a multi-faceted approach to understand the use and distribution of different irrigation technologies in the Roman Near East (63 BC – AD 636), looking at the ways in which social and environmental factors affected the implementation of those irrigation technologies. It is argued that no single factor can fully explain how irrigation technologies were used across time and space in this region. Instead, choices in irrigation technology seem to have been governed by a complex nexus of both social and environmental factors. |
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0140-1963 |
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Ancient Agriculture in the Middle East |
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THL @ christoph.kuells @ Kamash201265 |
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259 |
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Etschmann, B.; Liu, W.; Li, K.; Dai, S.; Reith, F.; Falconer, D.; Kerr, G.; Paterson, D.; Howard, D.; Kappen, P.; Wykes, J.; Brugger, J. |
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Enrichment of germanium and associated arsenic and tungsten in coal and roll-front uranium deposits |
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Journal Article |
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2017 |
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Chemical Geology |
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463 |
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29-49 |
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Arsenic, Coal, EXAFS and XANES, germanium, Hydrothermal fluids, Metallogenesis, Speciation, Tungsten |
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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. |
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0009-2541 |
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THL @ christoph.kuells @ etschmann_enrichment_2017 |
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183 |
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Kumar, V.; Setia, R.; Pandita, S.; Singh, S.; Mitran, T. |
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Assessment of U and As in groundwater of India: A meta-analysis |
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2022 |
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Chemosphere |
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303 |
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135199 |
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Arsenic, Geology, Groundwater, Health risk, Soil texture, Uranium |
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More than 2.5 billion people depend upon groundwater worldwide for drinking, and giving quality water has become one of the great apprehensions of human culture. The contamination of Uranium (U) and Arsenic (As) in the groundwater of India is gaining global attention. The current review provides state-of-the-art groundwater contamination with U and As in different zones of India based on geology and soil texture. The average concentration of U in different zones of India was in the order: West Zone (41.07 μg/L) \textgreater North Zone (37.7 μg/L) \textgreater South Zone (13.5 μg/L)\textgreater Central Zone (7.4 μg/L) \textgreater East Zone (5.7 μg/L) \textgreaterSoutheast Zone (2.4 μg/L). The average concentration of As in groundwater of India is in the order: South Zone (369.7 μg/L)\textgreaterCentral Zone (260.4 μg/L)\textgreaterNorth Zone (67.7 μg/L)\textgreaterEast Zone (60.3 μg/L)\textgreaterNorth-east zone (9.78 μg/L)\textgreaterWest zone (4.14 μg/L). The highest concentration of U and As were found in quaternary sediments, but U in clay skeletal and As in loamy skeletal. Results of health risk assessment showed that the average health quotient of U in groundwater for children and adults was less than unity. In contrast, it was greater than unity for As posing a harmful impact on human health. This review provides the baseline data regarding the U and As contamination status in groundwater of India, and appropriate, effective control measures need to be taken to control this problem. |
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0045-6535 |
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THL @ christoph.kuells @ kumar_assessment_2022 |
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161 |
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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 |
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2023 |
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Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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Jaireth, S.; Roach, I.C.; Bastrakov, E.; Liu, S. |
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Basin-related uranium mineral systems in Australia: A review of critical features |
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Journal Article |
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2016 |
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Ore Geology Reviews |
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76 |
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360-394 |
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Australia’s uranium deposits, Calcrete-uranium, Sandstone-hosted uranium, Unconformity-related uranium |
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This paper reviews critical features of basin-related uranium mineral systems in Australia. These mineral systems include Proterozoic unconformity-related uranium systems formed predominantly from diagenetic fluids expelled from sandstones overlying the unconformity, sandstone-hosted uranium systems formed from the influx of oxidised groundwaters through sandstone aquifers, and calcrete uranium systems formed from oxidised groundwaters flowing through palaeochannel aquifers (sand and calcrete). The review uses the so-called ‘source-pathway-trap’ paradigm to summarise critical features of fertile mineral systems. However, the scheme is expanded to include information on the geological setting, age and relative timing of mineralisation, and preservation of mineral systems. The critical features are also summarised in three separate tables. These features can provide the basis to conduct mineral potential and prospectivity analysis in an area. Such analysis requires identification of mappable signatures of above-mentioned critical features in geological, geophysical and geochemical datasets. The review of fertile basin-related systems shows that these systems require the presence of at least four ingredients: a source of leachable uranium (and vanadium and potassium for calcrete-uranium deposits); suitable hydrological architecture enabling connection between the source and the sink (site of accumulation); physical and chemical sinks or traps; and a post-mineralisation setting favourable for preservation. The review also discusses factors that may control the efficiency of mineral systems, assuming that world-class deposits result from more efficient mineral systems. The review presents a brief discussion of factors which may have controlled the formation of large deposits in the Lake Frome region in South Australia, the Chu-Sarysu and Syrdarya Basins in Kazakhstan and calcrete uranium deposits in the Yilgarn region, Western Australia. |
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0169-1368 |
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THL @ christoph.kuells @ jaireth_basin-related_2016 |
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139 |
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Singh, A.; Patel, S.; Bhadani, V.; Kumar, V.; Gaurav, K. |
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AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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2024 |
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Engineering Applications of Artificial Intelligence |
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127 |
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107405 |
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AutoML, Bayesian optimisation, Groundwater, Machine learning |
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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. |
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0952-1976 |
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THL @ christoph.kuells @ singh_automl-gwl_2024 |
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Pereira, A.J.S.C.; Neves, L.J.P.F. |
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Estimation of the radiological background and dose assessment in areas with naturally occurring uranium geochemical anomalies—a case study in the Iberian Massif (Central Portugal) |
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2012 |
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Journal of Environmental Radioactivity |
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112 |
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96-107 |
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Background, Dose assessment, Geochemical anomalies, Mine remediation, Natural radioactivity, Uranium |
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Naturally occurring uranium geochemical anomalies, representative of the several thousand recognized in the Portuguese section of the Iberian Massif and outcropping in three target areas with a total of a few thousand square metres, were subjected to a detailed study (1:1000 scale) to evaluate the radiological health-risk on the basis of a dose assessment. To reach this goal some radioactive isotopes from the uranium, thorium and potassium radioactive series were measured in 52 samples taken from different environmental compartments: soils, stream sediments, water, foodstuff (vegetables) and air; external radiation was also measured through a square grid of 10×10m, with a total of 336 measurements. The results show that some radioisotopes have high activities in all the environmental compartments as well as a large variability, namely for those of the uranium decay chain, which is a common situation in the regional geological setting. Isotopic disequilibrium is also common and led to an enrichment of several isotopes in the different pathways, as is the case of 226Ra; maximum values of 1.76BqL−1 (water), 986Bqkg−1 (soils) and 18.9Bqkg−1 (in a turnip sample) were measured. On the basis of a realistic scenario combined with the experimental data, the effective dose from exposure to ionizing radiation for two groups of the population (rural and urban) was calculated; the effective dose is variable between 8.0 and 9.5mSvyear−1, which is 3–4 times higher than the world average. Thus, the radiological health-risk for these populations could be significant and the studied uranium anomalies must be taken into account in the assessment of the geochemical background. The estimated effective dose can also be used as typical of the background of the Beiras uranium metalogenetic province and therefore as a “benchmark” in the remediation of the old uranium mining sites. |
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0265-931x |
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THL @ christoph.kuells @ pereira_estimation_2012 |
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129 |
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Jroundi, F.; Descostes, M.; Povedano-Priego, C.; Sánchez-Castro, I.; Suvannagan, V.; Grizard, P.; Merroun, M.L. |
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Title |
Profiling native aquifer bacteria in a uranium roll-front deposit and their role in biogeochemical cycle dynamics: Insights regarding in situ recovery mining |
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Journal Article |
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Year |
2020 |
Publication |
Science of The Total Environment |
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721 |
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137758 |
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Keywords |
Bacterial diversity, Bioremediation, In-situ recovery, Natural attenuation, Network analysis, Uranium |
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Abstract |
A uranium-mineralized sandy aquifer, planned for mining by means of uranium in situ recovery (U ISR), harbors a reservoir of bacterial life that may influence the biogeochemical cycles surrounding uranium roll-front deposits. Since microorganisms play an important role at all stages of U ISR, a better knowledge of the resident bacteria before any ISR actuations is essential to face environmental quality assessment. The focus here was on the characterization of bacteria residing in an aquifer surrounding a uranium roll-front deposit that forms part of an ISR facility project at Zoovch Ovoo (Mongolia). Water samples were collected following the natural redox zonation inherited in the native aquifer, including the mineralized orebody, as well as compartments located both upstream (oxidized waters) and downstream (reduced waters) of this area. An imposed chemical zonation for all sensitive redox elements through the roll-front system was observed. In addition, high-throughput sequencing data showed that the bacterial community structure was shaped by the redox gradient and oxygen availability. Several interesting bacteria were identified, including sulphate-reducing (e.g. Desulfovibrio, Nitrospira), iron-reducing (e.g. Gallionella, Sideroxydans), iron-oxidizing (e.g. Rhodobacter, Albidiferax, Ferribacterium), and nitrate-reducing bacteria (e.g. Pseudomonas, Aquabacterium), which may also be involved in metal reduction (e.g. Desulfovibrio, Ferribacterium, Pseudomonas, Albidiferax, Caulobacter, Zooglea). Canonical correspondence analysis (CCA) and co-occurrence patterns confirmed strong correlations among the bacterial genera, suggesting either shared/preferred environmental conditions or the performance of similar/complementary functions. As a whole, the bacterial community residing in each aquifer compartment would appear to define an ecologically functional ecosystem, containing suitable microorganisms (e.g. acidophilic bacteria) prone to promote the remediation of the acidified aquifer by natural attenuation. Assessing the composition and structure of the aquifer’s native bacteria is a prerequisite for understanding natural attenuation and predicting the role of bacterial input in improving ISR efficiency. |
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0048-9697 |
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THL @ christoph.kuells @ jroundi_profiling_2020 |
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177 |
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