Search Documents

By Kyeong Woo Chung, Rina Kim, Ho‑Seok Jeon, Yeongjin Ahn, Byunghun Go, Min‑seuk Kim, Hong‑In Kim, Sujeong Lee, Yuik Eom

The study focused on the recovery of vanadium from a Korean vanadium-bearing titaniferous magnetite (VTM) ore, examining the behaviors of magnetic separation and Na2CO3 roasting-water leaching. Two concentrates were prepared using single-stage dry magnetic separation and three-step wet magnetic separation, resulting in V2O5 grades of 0.8% and 1.0% and major gangue mineral SiO2 contents of 6.5% and 1.7%, respectively. These concentrates were subsequently roasted at 1050 °C with the addition of Na2CO3 and then water-leached at 25 °C. Various stoichiometric ratios of V2O5 to Na2CO3 were tested, ranging from 1:1 to 1:40 of the theoretically required amount. As the amount of Na2CO3 increased, the vanadium leaching efficiency showed fluctuations between 4 and 35% for ratios of 1:1 to 1:10, while it linearly increased from 4 to 82% for ratios of 1:10 to 1:40 in the leaching of the low-grade concentrate. In contrast, the leaching efficiency increased from 47 to 74% for ratios of 1:1 to 1:8 and then sharply rose to 84% at a ratio of 1:10 in the leaching of the high-grade concentrate. Subsequently, it reached a plateau of 92 to 94% for ratios of 1:20 to 1:40. The difference in leaching efficiency was primarily due to the reaction of gangue minerals with Na2CO3, which hindered vanadium leaching from the VTM concentrates. This hindrance was notably more pronounced in the low-grade concentrate with the lower salt ratio, as the gangue minerals exhibited a preference for consuming Na2CO3 over vanadium. The mineral formation was analyzed in detail using XRD and MLA to shed light on the mechanism of different vanadium leaching behavior depending on the concentrate grade and vanadium to salt ratio. Based on the results of this study, it is evident that applying a proper magnetic separation procedure is significant to achieving satisfactory vanadium recovery with less salt amount by removing gangue minerals ahead.

Mar 15, 2024

By R. J. Quevedo, Y. A. Sari, S. D. McKinnon

The large scale of cave mines leads to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method, the effects of which are difficult to account for during the design stages. In this paper we present a case study of the application of a machine learning approach to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile. Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analysed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms, nor the calibration of associated controlling parameters. The proposed random forest model predicted pillar collapses with 80% accuracy despite limited samples to model from. The main contributing factors to collapses were found to be related to available pillar volume, cave front geometry, and time under abutment stress conditions. The effects and interactions of such factors were also studied, showing that careful and improved control over operational conditions can significantly reduce the likelihood of pillar collapses. These conclusions could not have been obtained from stress analysis alone, illustrating the complementary nature of conventional stress analysis and machine learning approaches.

Mar 14, 2024

By J. P. Gouvea

Back-analysis of two mining-induced tremors in 2011 was used to determine a modelling criterion for the entire Klerksdorp Goldfield (i.e., Vaal Reefs region). At that time, most mining operations in the region were owned by AngloGold Ashanti (AGA) and shared many geotechnical similarities. By 2018 AGA had sold off all its Klerksdorp operations to different mining companies, which continued applying the 2011 modelling criterion. As mining activities became more remote, scattered, and isolated, the extraction of isolated blocks of ground and remnants became increasingly necessary. However, the inherited criterion allowed only minimal or no mining activities at these areas. Keeping in mind that safety is the overriding consideration, an innovative numerical modelling approach, which includes the application of peak particle velocity designs, was developed to provide practitioners in the Klerksdorp Goldfield with a suitable criterion to justifiably and safely mine isolated blocks of ground and remnants. This would not always be possible if a conformist approach was used.

Mar 14, 2024

By W. Bendaikha, S. Larbi, A. Ramdane

An oolitic iron ore from Gara Djebilet, Algeria was characterized by X-ray diffraction, scanning electron microscopy, and energy dispersive spectrosopy and the phosphorus distribution between various phases studied. Two ore types were identified – a haematite-dominated, oolitic type and a more massive, magnetite-dominated type. The haematite-dominated ore had a higher phosphorus content, up to 0.6% in the iron oxide minerals, than the magnetite-dominated type (0.3% P). Owing to the very fine-grained texture of the gangue minerals, reducing the phosphorus content to levels suitable for steelmaking (below 0.05% P) by conventiobal means would entail an energyintensive process with a high carbon footprint. Preliminary leaching test work using a sulphurous geothermal water as lixiviant, assisted by solar thermal acceleration, gave very promising results, removing 0.13% of the residual P (or 21.7% removal of total P) from the high-phosphorus oolitic ore.

Mar 14, 2024

By S. Swanepoel, A. V. Cherkaev, Q. G. Reynolds, M. Erwee

Ferrochrome (FeCr) is a vital ingredient in stainless steel production and is commonly produced by smelting chromite ores in submerged arc furnaces. Silicon (Si) is a componrnt of the FeCr alloy from the smelting process. Being both a contaminant and an indicator of the state of the process, its content needs to be kept within a narrow range. The complex chemistry of the smelting process and interactions between various factors make Si prediction by fundamental models infeasible. A data-driven approach offers an alternative by formulating the model based on historical data. This paper presents a systematic development of a data-driven model for predicting Si content. The model includes dimensionality reduction, regularized linear regression, and a boosting method to reduce the variability of the linear model residuals. It shows a good performance on testing data (R2 = 0.63). The most significant predictors, as determined by linear model analysis and permutation testing, are previous Si content, carbon and titanium in the alloy, calcium oxide in the slag, resistance between electrodes, and electrode slips. Further analysis using thermodynamic data and models, links these predictors to electrode control and slag chemistry. This analysis lays the foundation for implementing Si content control on a ferrochrome smelter.

Mar 14, 2024

By I. Ince, S. Kahraman, M. Rostami, B. Dibavar

The density, porosity, and strength of rocks are fundamental in the design of geo-engineering projects. Determining these properties requires the preparation of smooth core samples, which is usually impossible for soft rocks. Besides, only fragmented samples may be available in some projects. A method for predicting rock properties from rock particles such as drilling debris would therefore be useful. This study was undertaken to develop prediction equations for compressive strength (UCS), tensile strength (BTS), density, and porosity values from the crushability index (CI) obtained from rock fragments. The results showed that UCS, BTS, density, and porosity were strongly correlated with the CI. The physical characteristics and strength of rocks may be predicted with the help of the derived equations. The CI test is a valuable tool for estimating rock properties in drilling projects and in situations where core specimens are not available.

Mar 14, 2024

By Yao Yevenyo Ziggah, Victor Amoako Temeng, Solomon Evans Kweku Koomson

Tyre hours/life deficit is a major operational challenge facing the mining industry which adversely affects materials production and costs. An accurate forecast of the tyre life is key in addressing this menace. This study for the first time employed the hybrid intelligent technique by utilising three metaheuristic optimisation algorithms, including particle swarm optimisation (PSO), genetic algorithm (GA), and whale optimisation algorithm (WOA), as trainers for the parametric weights and biases to optimise multilayer perceptron neural network (MLPNN) for enhancing prediction of on-site dump truck tyre life in the mine. Four hybrid models known as PSO-MLPNN, WOA-MLPNN, GA-MLPNN, and BP-MLPNN were developed using a total of 157 tyre dataset records obtained from a surface mine in Ghana. In assessing the prediction performances for the models developed, five statistical performance metrics of variance accounted for (VAF), Nash–Sutcliffe efficiency index (NASH), coefficient of determination (r2), mean absolute percentage error (MAPE), and correlation coefficient (r) were utilised. Moreover, ranking, uncertainty analysis and Bayesian information criterion (BIC) techniques were utilised to establish the most effective hybrid model, whereas sensitivity analysis was conducted on the input parameters. Results achieved showed that PSO-MLPNN was the best for prediction because it had the least MAPE value of 1.196% and relatively high values of VAF (99.642%), NASH (0.996), r2 (0.996), and r (0.998). Besides, PSO-MLPNN had the best selection criteria values of 6, 7.1725, and 444.834 for the ranking, uncertainty analysis and BIC respectively. Hence, PSO-MLPNN is recommended for the prediction of on-site dump truck tyre life for the studied mine.

Mar 11, 2024

By Xin Wang, He Shao, Wenpu Li, Yiyi Zheng, Xi Chen, Yurong Gao

This study utilized experimental and numerical simulation methods to investigate and analyze the atomization characteristics of three internal air–water mixed atomizers under different operating conditions. By comparing the research results, the effective dust control working parameters for the application of the selected atomizer in the engineering field were determined. Firstly, the atomization characteristics of the three atomizers were studied using a customized experimental system. The results indicated that nozzle 2 had a wide atomization range, a small atomization distance, and small droplet size; nozzle 3 had a narrow atomization range, large droplet size, and fast atomization speed; and nozzle 1 not only had a large atomization range but also could produce droplets of dry mist size with fast atomization speed, meeting the dust reduction requirements of the Majiliang Coal Preparation Plant. Secondly, Fluent software was used to simulate the atomization process and particle size distribution of the three internal mixed atomizers, determining the working conditions of the atomizers, the required number of atomizers, and the application angle of the atomizers in the internal mixed dust removal system. Finally, the atomized dust control system was applied on-site, achieving a total dust removal efficiency of 90.72% and a respirable dust removal efficiency of 89.75%.

Mar 9, 2024

By Lingyun Zou, Xinghai Lei, Tiezheng Li, Yifei Song, Nan Wang, Yang Li

The stability of key strata is of utmost importance in ensuring safe and effective coal seam mining operations. To minimize disturbance impacts on these strata and ensure their stability, it is crucial to assess the degree of disturbance caused by mining activities. This study proposes the evaluation index “disturbance degree of key stratum (KSDD)” and establishes a quantitative KSDD criterion to measure the degree of disturbance on key strata. To facilitate the assessment process, an intelligent calculation system for KSDD is developed. The classification criteria are derived by combining the measured three-zone heights from multiple mines. The reliability and accuracy of the KSDD quantitative criterion are validated using field measurement data. Furthermore, the KSDD quantitative criterion is applied to the Meihuajing mining area No. 23, and the distribution characteristics of KSDD are analyzed. The findings of this study can serve as a valuable reference for reducing disturbance and enhancing the stability of key strata during subsequent mining operations. Overall, this work contributes to the field of ground control in coal seam mining by providing a comprehensive approach to assess and mitigate the disturbance caused by mining activities on key strata.

Mar 8, 2024

By Dayu Long, Changhong Li, Yu Wang, Juzhou Li, Yongyue Hu

To address the problem of seepage failure that affects the stability of the fine-grained tailings dam, two kinds of prevention and treatment measures were proposed involving composite flocculants and composite curing agents. The orthogonal test was conducted to determine the optimal ratio flocculant (ORF) and the optimal ratio curing agent (ORCG). Then, numerical models were established to evaluate the effects of the prevention and treatment measures on the seepage and stability of the tailings dam. The results show that the mechanical properties and microstructure of the tailings are significantly improved by adding the ORF and ORCG. Furthermore, numerical studies suggest that the addition of the ORF and the ORCG to the tailings improves the factor of safety of the tailings dam, but the addition of the ORCG also increases the height of the phreatic line. It is also speculated that the addition of the ORF can prevent the formation of through failure zone in the tailings dam. Therefore, the optimal measure to prevent seepage failure in the tailings dam is adding an appropriate amount of the ORF, as it reduces the height of the dam’s phreatic line and lowers the risk of seepage failure. The typical area susceptible to seepage failure can be treated by adding the ORCG. However, it is important to ensure adequate drainage of the area and to maintain the phreatic line at a reasonable buried depth.

Mar 4, 2024

By Shakuntala Tudu, Indrajit Paul, Surajit Sinha, Pulibandla Basaveswara Rao, Shaik Mahaboob Basha, Srinivas Dwarapudi

The iron and steel manufacturing sector directly accounts for 7–9% of global CO2 emissions. Raw material preparation, such as iron ore sintering, pelletizing, and cock making, is the major CO2 emitter. As climate change becomes a bigger concern, steel manufacturers need to lower CO2 emissions without hindering efficiency or increasing costs. In recent years, the percentage of iron ore pellets in blast furnaces has increased due to its uniform size, good reducibility, and high tumbler index. Pelletizing is energy-intensive and emits considerable CO2. Around 20% of CO2 emissions during pellet making come from fluxes. In the present work, the authors established a novel CO2- free flux called wollastonite (CaO.SiO2) for sustainable palletization. Pellets with varying percentages of wollastonite (0–6%) are prepared and tested for chemical, physical, and metallurgical properties. Image analysis through an optical microscope is carried out to quantify the phases of fired pellets. SEM–EDS is performed to evaluate the chemical composition of the melt and slag phases. The results showed that the reducibility index (RI) and swelling index (SI) of pellets decreased with an increase in wollastonite dosage. Pellet strength increases with an increase in wollastonite addition up to 1.2% CaO (2.27% wollastonite), and a decrease in strength is observed thereafter. The increase in strength is attributed to the increase in slag bonds. The decrease in strength beyond 1.2% CaO is mainly because of an increase in low melting eutectics and more magnetite content. Pellets with 2.27% wollastonite to get 1.2% CaO showed good physical and metallurgical properties.

Mar 4, 2024

By Cheng Xie, Zhihu Li, Xiaoming Sun, Linsen He, Jiaxu Ding, Jinkun Yang, Yong Zhang, Zhenmin Qi

To solve the problem of large deformation of deep soft rock roadway, taking the soft rock roadway of no. 1 coal mine in New Shanghai as an engineering example, the deformation mechanism of surrounding rock of deep soft rock roadway is analyzed by combining theoretical analysis, numerical simulation and field test, and the control countermeasures centering on deep and shallow dense drilling are put forward. Through theoretical derivation, based on the masonry beam theory, surrounding rock structure S-R stability principle and composite beam principle, the length, angle, and spacing of deep and shallow dense drilling are determined. Through numerical simulation, the deformation evolution process of soft rock roadway surrounding rock after dense drilling pressure relief is reproduced, and the influence of drilling parameters (drilling spacing, dip angle) on pressure relief effect is analyzed and compared. The field application test and monitoring verify that the deformation control measures of deep and shallow dense drilling broken roof surrounding rock have a good effect of large deformation control of soft rock roadway, and provide a new support means for the safety and stability control of soft rock roadway.

Mar 2, 2024

By Kasimcan Koruk, Julian M. Ortiz

A geological model accounting for uncertainties possesses important advantages for resource estimation. Machine learning algorithms (MLAs) employed on multivariate geochemical datasets open up ways to new methodologies for such geological models with ease in comparison to traditional geostatistical methods. This article proposes a two-step MLA with an ensemble implementation to define geological domains and their uncertainties based on geochemical data. The proposed workflow is applied hierarchically on a dataset from a porphyry copper deposit to perform binary classification that can be attributed to alteration domains.

Mar 1, 2024

By Quanjun Liu, Rongping Lin, Rongdong Deng

Arsenic contamination significantly affects the quality of copper concentrates, presenting challenges during smelting processes. Considering that covellite and enargite have similar surface properties, they are difficult to separate by flotation, resulting in copper concentrates with excessive levels of arsenic. To address this issue, this study systematically investigated the flotation separation of covellite and enargite in microflotation and bench-scale flotation tests using calcium lignosulfonate as a depressant, with the results indicating that calcium lignosulfonate can be used to separate covellite and enargite because it enables the separation of arsenic-bearing copper minerals into high- and low-arsenic (As 0.57%) copper concentrates. According to contact angle and adsorption tests, scanning electron microscopy–energy dispersive spectroscopy, and X-ray photoelectron spectroscopy, the differential adsorption of calcium lignosulfonate onto covellite and enargite is the primary reason for their distinct flotation behaviors in the presence of calcium lignosulfonate. The selective depressive effect of calcium lignosulfonate provides a novel approach for improving the flotation separation of arsenic-bearing minerals from copper sulfide minerals.

Feb 28, 2024

By Tuanhui Wang, Qiwei Wang, Quanrui Liu, Jianhui Xu, Chao Wang, Yu Liu, Yuefeng Li

Rockburst is a highly destructive geological hazard that can cause casualties and equipment damage. To achieve highaccuracy discrimination of rockburst intensity, this article proposes an improved model that addresses the inefficient maximum membership principle used in traditional rockburst fuzzy evaluation models. The stress coefficient σθ/σc, brittleness coefficient σc/σt, and elastic deformation energy index Wet are selected as evaluation indicators for rockburst classification. Subjective and objective weights are obtained using the Delphi method and entropy weight method (EWM). Three types of membership function distribution forms are then used to obtain the membership degrees of each indicator to rockburst grades: trapezoidal membership function (TMF), normal membership function (NMF), and quadratic parabolic membership function (QPMF). Finally, six traditional models and six improved models are established using the maximum membership principle (MMP) and weighted average-maximum membership principle combination evaluation principle (WMP), respectively. Based on the analysis of 100 sets of rockburst field data, the accuracy, precision, recall, and F1-score of the improved evaluation model are increased by 11.3%, 0.097, 0.068, and 0.089, respectively, compared to the traditional model. The Delphi-NMF-WMP model is selected as the best model, with four performance indices reaching 97.0%, 0.979, 0.979, and 0.978. The best model is applied to evaluate the rockburst intensity of the Cangling Tunnel, Dongguashan Copper Mine, and Jiangbian Hydropower Station Diversion Tunnel, with evaluation results consistent with the actual situation, demonstrating the reliability and scientificity of the model.

Feb 28, 2024

By Iga Trisnawati, Fitria Puspita, Himawan Tri Bayu Murti Petrus, Gyan Prameswara, Idi Amin, Andi Nurfaadhilah Ulfah

Laterite ore is a valuable resource due to its high content of metals essential for modern technology, including nickel, iron, and cobalt. However, extracting nickel from laterite ores is challenging due to their complex mineral composition and low nickel concentrations. This study examined the leaching behavior of pre-roasted laterite ore and the effect of roasting on leaching kinetics. The ore was roasted at temperatures of 280°C and 610°C for 3 h, followed by sulfuric acid atmospheric leaching. The results demonstrated that pre-roasting at 610°C significantly enhanced nickel leaching recovery, with up to 93% nickel successfully recovered. The leaching process of pre-roasted laterite ore was found to be controlled by diffusion through the ash layer, with activation energy values of 20.97 and 18.97 kJ/mol for laterite roasted at 280°C and 610°C, respectively. These findings indicate that pre-roasting can improve the leaching performance of laterite ore, potentially leading to more efficient and sustainable nickel extraction processes.

Feb 27, 2024

By Gabriel K. Nzulu, Lars Hultman, Per Eklund, Martin Magnuson, Abu Yaya, Prosper M. Nude, Hans Högberg

Pyrite is the most common among the group of sulfide minerals in the Earth and abundant in most geological settings. This gangue mineral in association with garnet, hematite, magnetite, and other sulfide minerals acts as an indicator mineral in the Kubi concession of the Asante Gold corporation in Ghana. X-ray diffraction (XRD), air annealing in a furnace, energydispersive x-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS) were applied to investigate the crystal structure, identify individual elements, permanence, transformation, and chemical/electronic properties of such pyrite. The study aims to identify individual elements and to gain an understanding of the surface reaction mechanisms, as well as the properties of precipitated pyrite particles observed during the hydrothermal formation of the ore deposit. XRD shows that pristine and annealed samples contain some hematite and quartz besides pyrite. Results from air annealing indicate that the relationship between pyrite and hematite-magnetite is controlled by temperature. EDX reveals that the sample has O and C as contaminants, while XPS in addition reveals Ba, Au, P, Al, and N. These elements are attributed to pyrite that bonds metallically or covalently to neighboring ligands/impurity minerals such as oxides, chalcogenide sulfides, as well as the gangue alteration minerals of magnetite and hematite in the pyrite sample. These findings suggest that during the hydrothermal flow regime, pyrite, pathfinder elements, and impurity minerals/metals were in contact with quartz minerals before undergoing hematite transformation, which thus becomes an indicator mineral in the Kubi gold concession.

Feb 27, 2024

By Ebrahim Ghasemi, Mohammad Hossein Kadkhodaei, Mosareza Jalilian

Entry-type mining methods, such as cut and fill method, play a significant role in underground mining, and they are still suitable methods for extracting deposits with irregular shapes and with weak host rocks. In these mining methods, the stability of the open spans is important for improving worker safety and increasing ore recovery. This study focuses on the application of six data mining classification methods, namely multinomial logistic regression (MLR), classification and regression trees (CART), logistic model tree (LMT), decision tree (J48), multivariate adaptive regression spline (MARS), and gene expression programming (GEP), in order to develop predictive models for stability assessment of underground entry-type excavations. For this purpose, a database containing 399 case histories of entry-type excavations from seven Canada underground mines was used. These models predict the stability status of entry-type excavations based on two input parameters, dimensions of the span and rock mass rating (RMR). The performance of developed models was evaluated using six performance metrics namely accuracy (ACC), sensitivity (SE), precision (PR), Matthew’s correlation coefficient (MCC), area under the receiver operating characteristic (ROC) curve (AUC), and performance index (PI). The results showed that all developed models can be used to predict the excavation stability, but the comparison of models indicated that the J48 model provides the highest performance with ACC = 0.900, SE = 0.869, PR = 0.922, MCC = 0.835, AUC = 0.891, and PI = 4.417. Thus, the proposed J48 model can be applied to identify the susceptibility of span instability in the entry-type mining, and if instability has been predicted, the necessary measures, including the use of suitable support systems, can be considered to reduce the casualties resulting from span instability.

Feb 26, 2024

By Fouad Faraj

For every blast pattern, zones of unique material types need to be classified before mining. Most material types can be summarized into different kinds of ore and waste. Blocks generated from blasthole assays are too small to be selectively mined so areas with a single material classification meeting the mine’s selective mining unit must be defined. Current industry standard practices are to manually draw ore/waste dig limits but this process is subjective and results in quantities of dilution and ore loss which can be improved upon to enhance the profitability of mining operations. Two weeks production data from a homogeneous Cu porphyry and heterogeneous Manto-type Cu deposit are used to assess the variability in the hand-drawn dig limits for 20 geologists, mining engineers, or ore control specialists and are compared with the optimal ore/waste delineation. The profit for 20 distinct hand-drawn dig limits for the homogeneous mine ranged by 3.7% from $10.5 million to $10.9 million averaging at $10.8 million. The percent range nearly doubled for the more heterogeneous mine at 5.9% from $17.9 million to $19.0 million averaging at $18.6 million. Using the optimal ore waste delineation, the profit increased to $11.0 million (1.9% increase) and $19.3 million (7.0% increase) for the homogeneous and heterogeneous deposits respectively. The natural variability, diggability requirement, and selectivity are identified as the main drivers of ore loss and dilution. Recommendations are provided for reducing the subjectivity in manual hand-drawn dig limits by integrating dig limit optimization algorithms as a tool to assist practitioners.

Feb 23, 2024

By O. K. Olawale, G. O. Edaogbogun, B. N. Ben‑Festus, F. Ben, F. R. Amodu

The environmental and health implications of artisanal gold mining activities at mining sites across Nigeria have rekindled research interest. This study aimed to assess the scalability of naturally occurring radioactive materials in gold mining sites in Iperindo, Nigeria. Soil samples were collected from three different mining sites and control locations and analyzed for natural radionuclides, mainly 238U, 232Th, and 40K, using gamma spectrometry having a sodium iodide detector. The average activity concentration values of 61.55 ± 3.71, 72.65 ± 4.45, and 1134.99 ± 38.12 Bqkg− 1 obtained from within the mining sites for 238U, 232Th, and 40K, respectively, were above the world permissible values of 33.0 Bqkg− 1 for 238U, 45.0 Bqkg− 1 for 232Th, and 420.0 Bqkg− 1 for 40K. The average activity concentration values of 15.26 ± 3.19, 21.46 ± 4.27, 381.04 ± 23.36 Bqkg− 1 estimated for 238U, 232Th, and 40K, respectively, for the control location were, however, lower than the global permissible values. The study also evaluated other radiological parameters such as radium equivalent, dose rate, annual effective dose rate, internal and external hazard indices, and alpha and gamma indices. The obtained values were 252.83 Bqkg− 1, 119.98 nGyh− 1, 147.15 mSvy− 1, 0.85 Bqkg− 1, 0.68 Bqkg− 1, 0.31 Bqkg− 1, and 1.89 Bqkg− 1 respectively. Except for radium equivalent, external and internal hazard risks, and alpha index, which were significantly lower than the recommended threshold, all other radiological parameters were higher than the recommended global limits. Moderate data reproducibility and a p-value of 0.000574 further reinforced the robustness and reliability of the results obtained in this study.

Feb 22, 2024