234U/238U activity ratios in groundwaters from two aquifers in Saudi Arabia, and correlation with water chemistry

El-Sharkawy, Ahmed

doi:10.1016/j.jrras.2018.07.005

Cited by 8 publications

(4 citation statements)

References 12 publications

(10 reference statements)

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“…Those reviews support the origin being the direct use of groundwater without pre‐treatment in the arid area far away from the populated residential area in the main cities, where the activities of sheep and camel grazing are widespread in the desert around the residential area of the city. It was documented that, the existence of uranium isotopes in groundwater is related to the hydrothermal, geochemical, and mineralogical conditions of the aquifers 35,36 . Uranium mobility to urinary stones was confirmed in the literature using a gamma spectrometer, and it might have a role in the formation of a specific type of urinary stones 37,38 .…”

Section: Main Results Obtainedmentioning

confidence: 95%

“…It was documented that, the existence of uranium isotopes in groundwater is related to the hydrothermal, geochemical, and mineralogical conditions of the aquifers. 35,36 Uranium mobility to urinary stones was confirmed in the literature using a gamma spectrometer, and it might have a role in the formation of a specific type of urinary stones. 37,38 The divalent uranium compounds have the ability to deposit on the surface of hydroxyapatite crystal by ion exchange of Ca +2 ions, and it is strongly bound with two phosphate groups.…”

Section: Uranium In the Urinary Stonesmentioning

confidence: 88%

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Elemental and statistical analysis of small individual urinary stones using TXRF spectrometry

et al. 2023

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Due to the small quantity of most of the extracted human urinary stone samples, there is an urgent need for an analytical technique that is able to perform a multi‐elemental quantitative analysis for a small fraction of these samples. In the present work, a few milligrams of different types of urinary stones were microwave digested in ultra‐pure nitric acid, and the elemental determination was achieved by total reflection X‐ray fluorescence (TXRF). The elements P, S, Ca, Fe, Cu, Zn, Se, and Sr were detected in most of the different stones. However, the trace elements: K, Ti, V, Cr, Mn, Ni, As, Pb, and U were also found in a certain number of samples. Furthermore, inductively coupled plasma optical emission spectrometry (ICP‐OES) was used and the elements Mg, Ti, Mn, Cu, Zn, and Cd were determined. A good agreement between the results of TXRF and ICP‐OES was obtained with respect to the elements Ti, Mn, Cu, and Zn. Cadmium and magnesium were only determined by ICP‐OES. More attention was given to the existence and the spectral interference of As‐Kα and Pb‐Lα as well as As‐Kβ and Br‐Kα in a limited number of urinary stones. Based on the analysis of variance and Pearson's correlation analysis, an additional statistical analysis study was performed in terms of quantified elements and the types of urinary stones. Calcium has a remarkable positive correction with Ni, Zn, and P, whereas a negative correlation was found with K, S, and Cu. Based on the hierarchical cluster analysis, the square Euclidean showed four main groups of urinary stones starting with high to trace amounts of calcium oxalate. Furthermore, the squared Euclidean showed further subgroups of the urinary stones. The role of certain elements in terms of forming or inhabiting the urinary stone formation was discussed. Uranium was determined in a limited number of urinary stones using TXRF and ICP‐OES. The origin of the uranium may be the drinking and groundwater. Uranium could be accumulated in the urinary stones from these sources. In addition, the divalent uranium compounds can deposit on the surface of hydroxyapatite crystal, which is one of the main compounds in the urinary stones.

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Section: Main Results Obtainedmentioning

confidence: 95%

Section: Uranium In the Urinary Stonesmentioning

confidence: 88%

Elemental and statistical analysis of small individual urinary stones using TXRF spectrometry

et al. 2023

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“…The sample solution was loaded on the UTEVA resin [12]. Uranium was eluted from the UTEVA column using 0.01M HCl, transferred to Teflon beaker [13].…”

Section: Apparatus and Proceduresmentioning

confidence: 99%

Determination of Uranium Isotopic Ratios by Alpha Spectrometry for Nuclear Materials Control Purposes

Abouzeid¹,

Kandil

Khedr

et al. 2020

Arab Journal of Nuclear Sciences and Applications

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Uranium is the most common element in the nuclear fuel cycle. Different techniques have been used for analyzing the isotopic composition of uranium. Precise determination of uranium isotopic ratios for an efficient control of nuclear material was carried out through the modification of the used chromatographic method of uranium. Alpha spectrometry was utilized to identify different types of natural, depleted and enriched samples for safeguard purposes. Uranium element was extracted from other interfering radionuclides, purified, electrodeposited on a stainless-steel disc, and measured using alpha-spectrometer, then isotopic ratios were calculated. For comparison purposes, the samples were also measured using a hyper pure germanium (HPGe) spectrometer and the uranium isotopic ratios were obtained. The results obtained by both techniques were in agreement within difference ranged from-8.7 to 12%. It was concluded that alpha spectrometric measurements of uranium isotopes preceded by efficient radiochemical separation using extraction chromatography is a fast and reliable technique for safeguard purposes.

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“…In the traditional water quality analysis, regression analysis is often used to determine the correlation between variables. For example, some scholars have used regression analysis to process data on the relationship between activity ratios of 234 U and 238 U and TDS in Saudi groundwater [1]; Mamun and Kwang-Guk also used regression analysis to evaluate the water quality of Yeongsan River in Korea with pollution source analysis and found that the river can be influenced by other systems [2]. Due to the existence of uncertain and nonlinear factors in the data, neural network has become a very popular water quality prediction method with its own good nonlinear mapping ability.…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification

Xin

Mou

2022

Wireless Communications and Mobile Computing

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With the development of society and the accelerated industrialization, the problem of water pollution has become increasingly prominent. In order to stop the gathering and diffusion of harmful substances in water bodies, leading to further deterioration of water quality and more serious environmental problems, environmental management departments have developed a series of pollutant discharge standards to prevent water pollution in real time. Common testing methods are the colorimetric method and TDS (total dissolved solids) value testing method, which are mostly through water bodies that contain acid, alkali, salt, and other indicators of the concentration test, to produce an assessment of water quality. However, the traditional methods of water quality testing, whether in the measurement time or in the accuracy of the test, are certain defects. In order to be able to quickly detect the concentration of water quality indicators in water bodies, timely response and treatment of highly polluted water bodies are urgently needed. In this paper, we propose a water quality detection classification model based on multimodal machine learning algorithm. Firstly, we preprocessed and analyzed the collected water quality dataset and determined the reasonable and perfect water quality classification influencing factors. Then, we successively built 15 kinds of classification models based on machine learning algorithms for water quality detection. At the same time, we evaluated the performance of each model. From the four evaluation indexes of precision, recall rate, F1 value, and accuracy, respectively, the real value is compared with the predicted value of each model. The experimental results show that sulfate, pH, solids, and hardness are the important influencing factors to perform water quality testing. And the three models XGBoost (Extreme Gradient Boosting), CatBoost (Categorical Boosting), and LGBM (Light Gradient Boosting Machine) have better performances in conducting water quality testing. Finally, we further optimized the classification models based on XGBoost, CatBoost, and LGBM by using two major tools: cross-validation and hyperparameter tuning.

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234U/238U activity ratios in groundwaters from two aquifers in Saudi Arabia, and correlation with water chemistry

Cited by 8 publications

References 12 publications

Elemental and statistical analysis of small individual urinary stones using TXRF spectrometry

Elemental and statistical analysis of small individual urinary stones using TXRF spectrometry

Determination of Uranium Isotopic Ratios by Alpha Spectrometry for Nuclear Materials Control Purposes

Research on the Application of Multimodal-Based Machine Learning Algorithms to Water Quality Classification

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