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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Title |
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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Year |
2023 |
Publication |
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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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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Jaireth, S.; Roach, I.C.; Bastrakov, E.; Liu, S. |
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Title |
Basin-related uranium mineral systems in Australia: A review of critical features |
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Journal Article |
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Year |
2016 |
Publication |
Ore Geology Reviews |
Abbreviated Journal |
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Volume |
76 |
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360-394 |
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Keywords |
Australia’s uranium deposits, Calcrete-uranium, Sandstone-hosted uranium, Unconformity-related uranium |
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Abstract |
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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Title |
AutoML-GWL: Automated machine learning model for the prediction of groundwater level |
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Journal Article |
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Year |
2024 |
Publication |
Engineering Applications of Artificial Intelligence |
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127 |
Issue |
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Pages |
107405 |
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Keywords |
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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168 |
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Pereira, A.J.S.C.; Neves, L.J.P.F. |
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Title |
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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Journal Article |
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Year |
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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Bacterial diversity, Bioremediation, In-situ recovery, Natural attenuation, Network analysis, Uranium |
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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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Ren, Y.; Yang, X.; Hu, X.; Wei, J.; Tang, C. |
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Title |
Mineralogical and geochemical evidence for biogenic uranium mineralization in northern Songliao Basin, NE China |
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Journal Article |
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Year |
2022 |
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Ore Geology Reviews |
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141 |
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104556 |
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Bacterial sulfate reduction, In-situ S isotope of pyrite, Northern Songliao basin, Sandstone-type uranium deposit, Sifangtai Formation |
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The sandstone-hosted uranium mineralization areas in the Sanzhao Sag of the northern Songliao Basin have been newly identified. The target stratum is the Upper Cretaceous Sifangtai Formation and the uranium mineralization mainly occurs in the bottom of Sifangtai Formation, corresponding to channel sand bodies in meandering river system, characterized by medium to fine-grained sandstone. This study proposes the uranium metallogenic model through petrographic observation, whole rock geochemistry, mineralogical study of uranium occurrence form (SEM), organic matter rock–eval pyrolysis analysis (REP) and in-situ sulfur isotope determination of different generations of pyrite by LA-MC-ICP-MS. Compared with the sandstones collected in barren reduction and oxidization zones, the mineralized sandstones show obvious increase in the contents of TOC, total sulfur, Y and U. Petrographic observations indicate that organic matters are mainly inherited from land plants. REP data display that the organic matter (OM) disseminated in the sandstone has very low hydrogen index (HI) from around 0 to 21 mg HC/g TOC and varied oxygen index (OI) from 44 to 115 mg CO2/g TOC, corresponding to Type Ⅳ kerogen (degraded kerogen). There are two types of coffinite with different grain size, micro-particles (μm-sized) and large aggregates (generally up to 100 μm) respectively. The coffinite micro spherules exhibit short rod-like or worm-like morphology occurring in clay matrix and cell cavities in degradofusinite or around subidiomorphic-idiomorphic pyrite. The coarse-grained coffinite contains other mineral facies (e.g. pyrite, quartz) and some of large coffinite aggregates display thrombolite-type microbial structures. The irregular pyrite relict particles in coarse-grained colloidal coffinite have light sulfur isotope compositions characterized by δ34S values from –39.96‰ to –49.89‰. The δ34S values of colloidal pyrite in replacement of OM or of the sub-idiomorphic FeS2 cement filling in the cavities of OM range from –52.77‰ to –13.88‰. Some of sub-idiomorphic pyrite cement and idiomorphic crystal have the heavier signature from – 27.06‰ to + 14.23‰. The light sulfur isotope signature suggests that the sulfur originates from bacterial sulfate reduction (BSR). The OM replacement by pyrite and the highest OI values recorded by REP in uranium mineralized samples are lines of evidence of biodegradation. Bacteria use the organic matter as food source and produce isotopically light reduced sulfur species. Oxygenated uranium-bearing waters infiltrated through the denudated windows at Daqing placanticline into the porous reduced sandstones deposited in the Sanzhao Sag. Uranium was indirectly reduced by BSR-derived iron disulfides or directly reduced by sulfate-reducing bacteria. |
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THL @ christoph.kuells @ ren_mineralogical_2022 |
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144 |
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Pastukhov, A.M.; Rychkov, V.N.; Smirnov, A.L.; Skripchenko, S.Y.; Poponin, N.A. |
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Purification of in situ leaching solution for uranium mining by removing solids from suspension |
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Journal Article |
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2014 |
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Minerals Engineering |
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55 |
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1-4 |
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Bag filter, Firm particles, In situ leaching mining, Injection wells, Intake capacity, Purification |
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This study investigated the process of in situ leaching (ISL) method of uranium mining, and the removal of solid particles from the leaching solution. Investigations were carried out for 4months. The content of firm suspensions in the productive solutions arriving from the well field was up to standard of 3–5mg/l. After keeping in a settler of productive solutions within one hour concentration of suspensions decreases to 2–2.5mg/l. To increase the life of the wells requires more fine purification of the ISL solutions. The best results can be obtained but using filtration. Bag filters were used in experiments carried out at the extraction site. All samples of polypropylene bag filter was produced by the Tamfelt Corporation. The best results were obtained for fabrics S-51M03-L2K4 (pore size 3μm). After three month of trials following indicators of wells work were fixed: on the trial cell decrease in intake capacity did not occur; on the other cells of well field injectability of holes for the same period of time decreased for 15–40%. The results illustrated the high efficiency of this method, which allows injection wells to reach a constant intake capacity, making it possible for technological cells to achieve a constant productivity and balance. Purification of solutions allows to reduce acidulation term of new technological cells from 3–4 to 1.5–2months. |
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0892-6875 |
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THL @ christoph.kuells @ pastukhov_purification_2014 |
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204 |
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Edmunds, W.M.; Shand, P.; Hart, P.; Ward, R.S. |
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The natural (baseline) quality of groundwater: a UK pilot study |
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2003 |
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Science of The Total Environment |
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310 |
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1 |
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25-35 |
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Baseline quality, Groundwater, Hydrogeochemistry, Monitoring, Water Policy |
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Knowledge of the natural baseline quality of groundwaters is an essential prerequisite for understanding pollution and for imposing regulatory limits. The natural baseline of groundwaters may show a range of concentrations depending on aquifer mineralogy, facies changes, flow paths and residence time. The geochemical controls on natural concentrations are discussed and an approach to defining baseline concentrations using geochemical and statistical tools is proposed. The approach is illustrated using a flowline from the Chalk aquifer in Berkshire, UK where aerobic and anaerobic sections of the aquifer are separately considered. The baseline concentrations for some elements are close to atmospheric values whereas others evolve through time-dependent water–rock interaction. Certain solutes (K, NH4+), often considered contaminants, reach naturally high concentrations due to geochemical controls; transition metal concentrations are generally low, although their concentrations may be modified by redox controls. It is recommended that the baseline approach be incorporated into future management strategies, notably monitoring. |
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0048-9697 |
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THL @ christoph.kuells @ edmunds_natural_2003 |
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166 |
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Moreau, M.; Daughney, C. |
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Defining natural baselines for rates of change in New Zealand’s groundwater quality: Dealing with incomplete or disparate datasets, accounting for impacted sites, and merging into state of the-environment reporting |
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2021 |
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Science of The Total Environment |
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755 |
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143292 |
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Baseline, Groundwater quality, Machine-learning, Monitoring, New Zealand, Trends |
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To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (\textgreater1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting. |
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THL @ christoph.kuells @ moreau_defining_2021 |
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164 |
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Author |
YI, Z.-ji; LIAN, B.; YANG, Y.-qun; ZOU, J.-ling |
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Title |
Treatment of simulated wastewater from in situ leaching uranium mining by zerovalent iron and sulfate reducing bacteria |
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Journal Article |
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2009 |
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Transactions of Nonferrous Metals Society of China |
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19 |
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840 |
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Keywords |
basification, sulfate, sulfate reducing bacteria (SRB), uranium, wastewater, zerovalent iron (ZVI) |
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Abstract |
Batch and column experiments were conducted to determine whether zerovalent iron (ZVI) and sulfate reducing bacteria (SRB) can function synergistically and accelerate pollutant removal. Batch experiments suggest that combining ZVI with SRB can enhance the removal of U(?) synergistically. The removal rate of U(?) in the ZVI+SRB combining system is obviously higher than the total rate of ZVI system and SRB system with a difference of 13.4% at t=2 h and 29.9% at t=4 h. Column experiments indicate that the reactor filled with both ZVI and SRB biofilms is of better performance than the SRB bioreactor in wastewater basification, desulfurization and U(?) fixation. The results imply that the ZVI+SRB permeable reactive barrier may be a promising method for treating subsurface uranium contamination. |
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1003-6326 |
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no |
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Call Number |
THL @ christoph.kuells @ yi_treatment_2009 |
Serial |
206 |
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