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Mühr-Ebert, E. L., Wagner, F., & Walther, C. (2019). Speciation of uranium: Compilation of a thermodynamic database and its experimental evaluation using different analytical techniques. Applied Geochemistry, 100, 213–222.
Abstract: Environmental hazards are caused by uranium mining legacies and enhanced radioactivity in utilized groundwater and surface water resources. Knowledge of uranium speciation in these waters is essential for predicting radionuclide migration and for installing effective water purification technology. The validity of the thermodynamic data for the environmental media affected by uranium mining legacies is of utmost importance. Therefore, a comprehensive and consistent database was established according to current knowledge. The uranium data included in the database is based on the NEA TDB (Guillaumont et al., 2003) and is modified or supplemented as necessary e.g. for calcium and magnesium uranyl carbonates. The specific ion interaction theory (Brönsted, 1922) is used to estimate activity constants, which is sufficient for the considered low ionic strengths. The success of this approach was evaluated by comparative experimental investigations and model calculations (PHREEQC (Parkhurst and Appelo, 1999)) for several model systems. The waters differ in pH (2.7–9.8), uranium concentration (10−9-10−4 mol/L) and ionic strength (0.002–0.2 mol/L). We used chemical extraction experiments, ESI-Orbitrap-MS and time-resolved laser-induced fluorescence spectroscopy (TRLFS) to measure the uranium speciation. The latter method is nonintrusive and therefore does not change the chemical composition of the investigated waters. This is very important, because any change of the system under study may also change the speciation.
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Botha, R., Lindsay, R., Newman, R. T., Maleka, P. P., & Chimba, G. (2019). Radon in groundwater baseline study prior to unconventional shale gas development and hydraulic fracturing in the Karoo Basin (South Africa). Applied Radiation and Isotopes, 147, 7–13.
Abstract: The prospect of unconventional shale gas development in the semi-arid Karoo Basin (South Africa) has created the prerequisite to temporally characterise the natural radioactivity in associated groundwater which is solely depended on for drinking and agriculture purposes. Radon (222Rn) was the primary natural radionuclide of interest in this study; however, supplementary radium (226Ra and 228Ra) in-water measurements were also conducted. A total of 53 aquifers spanning three provinces were studied during three separate measurement campaigns from 2014 to 2016. The Karoo Basin’s natural radon-in-water levels can be characterised by a minimum of 1 ± 1 Bq/L (consistent with zero or below LLD), a maximum of 183 ± 18 Bq/L and mean of 41 ± 5 Bq/L. The mean radon-in-water levels for shallow aquifers were systematically higher (55 ± 10 Bq/L) compared to deep (14 ± 3 Bq/L) or mixed aquifers (20 ± 6 Bq/L). Radon-in-water activity concentration fluctuations were predominantly observed from shallow aquifers compared to the generally steady levels of deep aquifers. A collective seasonal mean radon-in-water levels increase from the winter of 2014 (44 ± 8 Bq/L) to winter of 2016 (61 ± 16 Bq/L) was noticed which could be related to the extreme national drought experienced in 2015. Radium-in-water (228Ra and 226Ra) levels ranged from below detection level to a maximum of 0.008 Bq/L (226Ra) and 0.015 Bq/L (228Ra). The 228Ra/226Ra ratio was characterised by a minimum of 0.93, a maximum of 6.5 and a mean value of 3.3 ± 1.3. Developing and improving baseline naturally occurring radionuclide groundwater databases is vital to study potential radiological environmental impacts attributed to industrial processes such as hydraulic fracturing or mining.
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Hebert, B., Baron, F., Robin, V., Lelievre, K., Dacheux, N., Szenknect, S., et al. (2019). Quantification of coffinite (USiO4) in roll-front uranium deposits using visible to near infrared (Vis-NIR) portable field spectroscopy. Journal of Geochemical Exploration, 199, 53–59.
Abstract: Coffinite (USiO4) is a common uranium-bearing mineral of roll-front uranium deposits. This mineral can be identified by the visible near infrared (Vis-NIR) portable field spectrometers used in mining exploration. However, due to the low detection limits and associated errors, the quantification of coffinite abundance in the mineralized sandstones or sandy sediments of roll-front uranium deposits using Vis-NIR spectrometry requires a specific methodological development. In this study, the 1135 nm absorption band area is used to quantify the abundance of coffinite. This absorption feature does not interfere with NIR absorption bands of any other minerals present in natural sands or sandstones of uranium roll-front deposits. The correlation between the 1135 nm band area and coffinite content was determined from a series of spectra measured from prepared mineral mixtures. The samples were prepared with a range of weighted amounts of arenitic sands and synthetic coffinite simulating the range of uranium concentration encountered in roll-front uranium deposits. The methodology presented in this study provides the quantification of the coffinite content present in sands between 0.03 wt% to 1 wt% coffinite with a detection limit as low as 0.005 wt%. The integrated area of the 1135 nm band is positively correlated with the coffinite content of the sand in this range, showing that the method is efficient to quantify coffinite concentrations typical of roll-front uranium deposits. The regression equation defined in this study was then used as a reference to predict the amount of natural coffinite in a set of mineralized samples from the Tortkuduk uranium roll-front deposit (South Kazakhstan).
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Karaimeh, S. A. (2019). Maintaining desert cultivation: Roman, Byzantine, and Early Islamic water-strategies at Udhruh region, Jordan. Journal of Arid Environments, 166, 108–115.
Abstract: The site of Udhruh is located in the arid desert of southern Jordan, about 15 km to the east of Petra. The site was built by the Nabataeans but expanded by the Romans (as a defensive site) and was continuously occupied until the Early Islamic period. It receives less than the 200 mm of annual precipitation, which is crucial for agricultural cultivation. Archaeological evidence from earlier excavations together with new data from several survey projects indicate that areas around Udhruh were cultivated throughout the Roman, Byzantine, and Early Islamic periods (300 BCE–800 CE). The fundamental question is: how did the people of Udhruh sustain their community in the desert, and how did they transform the desert into arable land? The landscape could be utilised thanks to sophisticated water management and irrigation techniques. At least four underground qanat systems were identified providing Udhruh with access to groundwater. At the terminal end of the qanat systems, several types of closed surface channels conveyed the water to reservoirs, which subsequently distributed the water to the field systems. The water systems of Udhruh differ from the well-known Nabataean systems in the surrounding area. As Udhruh was taken over by the Roman army in 106 CE, this study analyses how the Nabataean water systems continued to function and adapt through the Roman and Byzantine periods. A complete understanding of Udhruh’s water systems helps to reconstruct past land use, agricultural activity, and irrigation practices in a currently arid region.
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Merembayev, T., Yunussov, R., & Yedilkhan, A. (2019). Machine Learning Algorithms for Stratigraphy Classification on Uranium Deposits. Procedia Computer Science, 150, 46–52.
Abstract: Machine learning today becomes more and more effective instrument to solve many particular problems, where there are difficulties to apply well known and described math model. In other words – it is a great tool to describe non-linear phenomena. We tried to use this technique to improve existing process of stratigraphy, and reduce costs on site by applying computer leaded predictions on the basis of existing on-field collected data. Article describes usage of machine learning algorithms for stratigraphy boundaries classification based on geophysics logging data for uranium deposit in Kazakhstan. Correct marking of stratigraphy from geophysics logging data is complex non-linear task. To solve this task we applied several algorithms of machine learning: random forest, logistic regression, gradient boosting, k nearest neighbour and XGBoost.
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