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Radioactivity and radiological risk associated with effluent sediment containing technologically enhanced naturally occurring radioactive materials in amang (tin tailings) processing industry

Bahari I, Mohsen N, Abdullah P

J Environ Radioact, 2007;95(2-3):161-70.
PMID: 17428589

The processing of amang, or tin tailings, for valuable minerals has been shown to technologically enhance NORM and this has stirred significant radiological safety and health concerns among Malaysia's regulatory authority. A growing radiological concern is now focused on the amang effluent containing NORM in recycling ponds, since these ponds may be reclaimed for future residential developments. A study was carried out to assess the radiological risk associated with amang processing and the accumulated effluent in the recycling ponds. Twenty-six sediment samples from the recycling ponds of two amang plants in the states of Selangor and Perak, Malaysia, were collected and analyzed. The maximum activity concentrations of (238)U, (226)Ra, (232)Th and (40)K recorded in sediments from these ponds were higher than Malaysia's and the world's natural highest. Correspondingly, the mean radium equivalent activity concentration indices, Ra(eq), and gamma radiation representative level index, I(gammar), were higher than the world's average. The enhancement of NORM in effluent sediments as a consequence of amang processing, and the use of a closed water management recycling system created Effective Dose Rates, E (nSv h(-1)), that signal potential environmental radiological risks in these ponds, should they be reclaimed for future land use.

Matched MeSH terms: Mining
Fulltext Quantification and Radiological Risk Estimation Due to the Presence of Natural Radionuclides in Maiganga Coal, Nigeria

Kolo MT, Khandaker MU, Amin YM, Abdullah WH

PLoS One, 2016;11(6):e0158100.
PMID: 27348624 DOI: 10.1371/journal.pone.0158100

Following the increasing demand of coal for power generation, activity concentrations of primordial radionuclides were determined in Nigerian coal using the gamma spectrometric technique with the aim of evaluating the radiological implications of coal utilization and exploitation in the country. Mean activity concentrations of 226Ra, 232Th, and 40K were 8.18±0.3, 6.97±0.3, and 27.38±0.8 Bq kg-1, respectively. These values were compared with those of similar studies reported in literature. The mean estimated radium equivalent activity was 20.26 Bq kg-1 with corresponding average external hazard index of 0.05. Internal hazard index and representative gamma index recorded mean values of 0.08 and 0.14, respectively. These values were lower than their respective precautionary limits set by UNSCEAR. Average excess lifetime cancer risk was calculated to be 0.04×10-3, which was insignificant compared with 0.05 prescribed by ICRP for low level radiation. Pearson correlation matrix showed significant positive relationship between 226Ra and 232Th, and with other estimated hazard parameters. Cumulative mean occupational dose received by coal workers via the three exposure routes was 7.69 ×10-3 mSv y-1, with inhalation pathway accounting for about 98%. All radiological hazard indices evaluated showed values within limits of safety. There is, therefore, no likelihood of any immediate radiological health hazards to coal workers, final users, and the environment from the exploitation and utilization of Maiganga coal.

Matched MeSH terms: Coal Mining*
Fulltext Evaluation of risk factors for prolonged invasive mechanical ventilation in Paediatric Intensive Care Unit (PICU)

Ismail, I., Yap, B.W., Abidin, A.S.Z.

Pertanika Journal of Science & Technology, 2017;25(3):811-824.
MyJurnal

Prolonged mechanical ventilation (PMV) is associated with increase in mortality and resource utilisation as well as hospitalisation costs. This study evaluates the risk factors of PMV. A retrospective study was conducted involving 890 paediatric patients comprising 237 neonates, 306 infants, 223 of pre-school age and 124 who are of school going age. The data mining decision trees algorithms and logistic regression was employed to develop predictive models for each age category. The independent variables were classified into four categories, that is, demographic data, admission factors, medical factors and score factors. The dependent variable is the duration of ventilation where it is categorized 0 denoting non-PMV and 1 denoting PMV. The performances of three decision tree models (CHAID, CART and C5.0) and logistic regression were compared to determine the best model. The results indicated that the decision tree outperformed the logistic regression model for all age categories, given its good accuracy rate for testing dataset. Decision trees results identified length of stay and inotropes as significant risk factors in all age categories. PRISM 12 hours and principal diagnosis were identified as significant risk factors for infants.

Matched MeSH terms: Data Mining
Machine learning-based coronary artery disease diagnosis: A comprehensive review

Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.

Comput Biol Med, 2019 08;111:103346.
PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.

Matched MeSH terms: Data Mining
Fulltext Fischer Linear Discrimination and Quadratic Discrimination Analysis-Based Data Mining Technique for Internet of Things Framework for Healthcare

Hasan MK, Ghazal TM, Alkhalifah A, Abu Bakar KA, Omidvar A, Nafi NS, et al.

Front Public Health, 2021;9:737149.
PMID: 34712639 DOI: 10.3389/fpubh.2021.737149

The internet of reality or augmented reality has been considered a breakthrough and an outstanding critical mutation with an emphasis on data mining leading to dismantling of some of its assumptions among several of its stakeholders. In this work, we study the pillars of these technologies connected to web usage as the Internet of things (IoT) system's healthcare infrastructure. We used several data mining techniques to evaluate the online advertisement data set, which can be categorized as high dimensional with 1,553 attributes, and the imbalanced data set, which automatically simulates an IoT discrimination problem. The proposed methodology applies Fischer linear discrimination analysis (FLDA) and quadratic discrimination analysis (QDA) within random projection (RP) filters to compare our runtime and accuracy with support vector machine (SVM), K-nearest neighbor (KNN), and Multilayer perceptron (MLP) in IoT-based systems. Finally, the impact on number of projections was practically experimented, and the sensitivity of both FLDA and QDA with regard to precision and runtime was found to be challenging. The modeling results show not only improved accuracy, but also runtime improvements. When compared with SVM, KNN, and MLP in QDA and FLDA, runtime shortens by 20 times in our chosen data set simulated for a healthcare framework. The RP filtering in the preprocessing stage of the attribute selection, fulfilling the model's runtime, is a standpoint in the IoT industry. Index Terms: Data Mining, Random Projection, Fischer Linear Discriminant Analysis, Online Advertisement Dataset, Quadratic Discriminant Analysis, Feature Selection, Internet of Things.

Matched MeSH terms: Data Mining
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.

Sci Total Environ, 2019 Mar 10;655:684-696.
PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Matched MeSH terms: Data Mining
Fulltext Effect of temporal relationships in associative rule mining for web log data

Khairudin NM, Mustapha A, Ahmad MH

ScientificWorldJournal, 2014;2014:813983.
PMID: 24587757 DOI: 10.1155/2014/813983

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

Matched MeSH terms: Data Mining/methods*
Fulltext Analysis of the perception of occupational accidents in mining and quarrying sector towards a safe and healthy working environment

Nur Azlina Abd Rahman, Muhamad Arifpin Mansor, Ahmad Rasdan Ismail

Journal of Occupational Safety and Health, 2015;12(1):165-170.
MyJurnal

The occurrences of occupational accidents and incidents are increasing in parallel with the growth of industries
such as mining and quarrying. The main objective of this study was to analyze data on the perception of occupational
accidents in the mining and quarrying sector in Malaysia. The data was collected and examined based on the
questionnaires on the level of perception of accident investigation in mining and quarrying sector. Statistical data
reported by the Department of Occupational Safety and Health (DOSH) was also reviewed. The findings of this study
prove that the level of perception of workers towards occupational accident issues in mining and quarrying sector
is still in the moderate level with the mean value of 3.28. The findings show that 51.7% of the workers agree while
25.9% totally agree to the accident occurrence. Only 1.7% of the workers are not aware of accident occurrence at the
workplace. Employers and employees must carry out their responsibilities to prevent accidents by adhering to health
and safety practices at the workplace.

Matched MeSH terms: Mining
Geospeciation of arsenic using MINTEQA2 for a post-mining lake

Sari SA, Ujang Z, Ahmad UK

Water Sci Technol, 2006;54(11-12):289-99.
PMID: 17302332

The objective of this study was to investigate the cycling of arsenic in the water column of a post-mining lake. This study is part of a research project to develop health risk assessment for the surrounding population. Inductively Coupled Plasma-Mass Spectrophotometer (ICP-MS) and Capillary Electrophoresis (CE) have been used to analyze the total amount and speciation, respectively. A computer program, called MINTEOA2, which was developed by the United States Environmental Protection Agency (USEPA) was used for predicting arsenic, iron, and manganese as functions of pH and solubility. Studying the pH values and cycle of arsenic shows that the percentage of bound arsenate, As(V) species in the form of HAsO4- increases with range pH from 5 to 7, as well as Fe(II) and Mn(III). As expected phases of arsenic oxides are FeAsO4 and Mn3(AsO4), as a function of solubility, however none of these phases are over saturated and not precipitated. It means that the phases of arsenic oxides have a high solubility.

Matched MeSH terms: Mining
Fulltext YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Teng S, Khong KW, Pahlevan Sharif S, Ahmed A

JMIR Public Health Surveill, 2020 10 01;6(4):e19618.
PMID: 33001036 DOI: 10.2196/19618

BACKGROUND: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.
OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.
METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.
RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.
CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

Matched MeSH terms: Data Mining
Fulltext Adapting Document Similarity Measures for Ligand-Based Virtual Screening

Himmat M, Salim N, Al-Dabbagh MM, Saeed F, Ahmed A

Molecules, 2016 Apr 13;21(4):476.
PMID: 27089312 DOI: 10.3390/molecules21040476

Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.

Matched MeSH terms: Data Mining*
Fulltext A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Nilashi M, Ahmadi H, Shahmoradi L, Ibrahim O, Akbari E

J Infect Public Health, 2018 10 04;12(1):13-20.
PMID: 30293875 DOI: 10.1016/j.jiph.2018.09.009

BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning.
METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

Matched MeSH terms: Data Mining
Fulltext First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene

Mohamoud HS, Hussain MR, El-Harouni AA, Shaik NA, Qasmi ZU, Merican AF, et al.

Comput Math Methods Med, 2014;2014:904052.
PMID: 24723968 DOI: 10.1155/2014/904052

GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.

Matched MeSH terms: Data Mining/methods
Fulltext Intelligent bar chart plagiarism detection in documents

Al-Dabbagh MM, Salim N, Rehman A, Alkawaz MH, Saba T, Al-Rodhaan M, et al.

ScientificWorldJournal, 2014;2014:612787.
PMID: 25309952 DOI: 10.1155/2014/612787

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

Matched MeSH terms: Data Mining/methods*
Carcinogenic and non-carcinogenic health risk of arsenic ingestion via drinking water in Langat River Basin, Malaysia

Ahmed MF, Mokhtar MB, Alam L

Environ Geochem Health, 2021 Feb;43(2):897-914.
PMID: 32372251 DOI: 10.1007/s10653-020-00571-w

The prolonged persistence of toxic arsenic (As) in environment is due to its non-biodegradable characteristic. Meanwhile, several studies have reported higher concentrations of As in Langat River. However, it is the first study in Langat River Basin, Malaysia, that As concentrations in drinking water supply chain were determined simultaneously to predict the health risks of As ingestion. Water samples collected in 2015 from the four stages of drinking water supply chain were analysed for As concentration by inductively coupled plasma mass spectrometry. Determined As concentrations along with the time series data (2004-2015) were significantly within the maximum limit 0.01 mg/L of drinking water quality standard set by World Health Organization. The predicted As concentration by auto-regression moving average was 3.45E-03 mg/L in 2020 at 95% level based on time series data including climatic control variables. Long-term As ingestion via household filtration water at Langat Basin showed no potential lifetime cancer risk (LCR) 9.7E-06 (t = 6.68; p = 3.37E-08) as well as non-carcinogenic hazard quotient (HQ) 4.8E-02 (t = 6.68; p = 3.37E-08) risk at 95% level. However, the changing landscape, ex-mining ponds and extensive use of pesticides for palm oil plantation at Langat Basin are considered as the major sources of increased As concentration in Langat River. Therefore, a two-layer water filtration system at Langat Basin should be introduced to accelerate the achievement of sustainable development goal of getting safe drinking water supply.

Matched MeSH terms: Mining
The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data

Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.

Nat Biotechnol, 2020 06;38(6):679-684.
PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
Matched MeSH terms: Data Mining
Fulltext Diabetes disease prediction system using HNB classifier based on discretization method

Al-Hameli BA, Alsewari AA, Basurra SS, Bhogal J, Ali MAH

J Integr Bioinform, 2023 Mar 01;20(1).
PMID: 36810102 DOI: 10.1515/jib-2021-0037

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

Matched MeSH terms: Data Mining
Source profiling of arsenic and heavy metals in the Selangor River basin and their maternal and cord blood levels in Selangor State, Malaysia

Sakai N, Alsaad Z, Thuong NT, Shiota K, Yoneda M, Ali Mohd M

Chemosphere, 2017 Oct;184:857-865.
PMID: 28646768 DOI: 10.1016/j.chemosphere.2017.06.070

Arsenic and 5 heavy metals (nickel, copper, zinc, cadmium and lead) were quantitated in surface water (n = 18) and soil/ore samples (n = 45) collected from 5 land uses (oil palm converted from forest, oil palm in peat swamp, bare land, quarry and forest) in the Selangor River basin by inductively coupled plasma mass spectrometry (ICP-MS). Geographic information system (GIS) was used as a spatial analytical tool to classify 4 land uses (forest, agriculture/peat, urban and bare land) from a satellite image taken by Landsat 8. Source profiling of the 6 elements was conducted to identify their occurrence, their distribution and the pollution source associated with the land use. The concentrations of arsenic, cadmium and lead were also analyzed in maternal blood (n = 99) and cord blood (n = 87) specimens from 136 pregnant women collected at the University of Malaya Medical Center for elucidating maternal exposure as well as maternal-to-fetal transfer. The source profiling identified that nickel and zinc were discharged from sewage and/or industrial effluents, and that lead was discharged from mining sites. Arsenic showed a site-specific pollution in tin-tungsten deposit areas, and the pollution source could be associated with arsenopyrite. The maternal blood levels of arsenic (0.82 ± 0.61 μg/dL), cadmium (0.15 ± 0.2 μg/dL) and lead (2.6 ± 2.1 μg/dL) were not significantly high compared to their acute toxicity levels, but could have attributable risks of chronic toxicity. Those in cord blood were significantly decreased in cadmium (0.06 ± 0.07 μg/dL) and lead (0.99 ± 1.2 μg/dL) but were equivalent in arsenic (0.82 ± 1.1 μg/dL) because of the different kinetics of maternal-to-fetal transfer.

Matched MeSH terms: Mining
Geochemical characteristics of rare earth elements in different types of soil: A chemometric approach

Khan AM, Behkami S, Yusoff I, Md Zain SB, Bakar NKA, Bakar AFA, et al.

Chemosphere, 2017 Oct;184:673-678.
PMID: 28628904 DOI: 10.1016/j.chemosphere.2017.06.032

Rare earth elements (REEs) are becoming significant due to their huge applications in many industries, large-scale mining and refining activities. Increasing usage of such metals pose negative environmental impacts. In this research ICP-MS has been used to analyze soil samples collected from former ex-mining areas in the depths of 0-20 cm, 21-40 cm, and 41-60 cm of residential, mining, natural, and industrial areas of Perak. Principal component analysis (PCA) revealed that soil samples taken from different mining, industrial, residential, and natural areas are separated into four clusters. It was observed that REEs were abundant in most of the samples from mining areas. Concentration of the rare elements decrease in general as we move from surface soil to deeper soils.

Matched MeSH terms: Mining
Assessing anthropogenic levels, speciation, and potential mobility of rare earth elements (REEs) in ex-tin mining area

Khan AM, Yusoff I, Bakar NKA, Bakar AFA, Alias Y

Environ Sci Pollut Res Int, 2016 Dec;23(24):25039-25055.
PMID: 27677993 DOI: 10.1007/s11356-016-7641-x

A study was carried out to determine the level of rare earth elements (REEs) in water and sediment samples from ex-mining lakes and River in Kinta Valley, Perak, Malaysia. Surface water and sediments from an ex-mining lake and Kinta River water samples were analyzed for REEs by inductively coupled plasma mass spectrometry. The total concentration of REEs in the ex-mining lake water samples and sediments were found to be 3685 mg/l and 14159 mg/kg, respectively, while the total concentration of REEs in Kinta River water sample was found to be 1224 mg/l. REEs in mining lake water were found to be within 2.42 mg/l (Tb) to 46.50 mg/l (Ce), while for the Kinta River, it was 1.33 mg/l (Ho) to 29.95 mg/l (Ce). Sediment samples were also found with REEs from 9.81 mg/kg (Ho) to 765.84 mg/kg (Ce). Ce showed the highest average concentrations for mining lake (3.88 to 49.08 mg/l) and Kinta River (4.44 to 33.15 mg/l) water samples, while the concentration of La was the highest (11.59 to 771.61 mg/kg) in the mining lake sediment. Lu was shown to have the highest enrichment of REEs in ex-mining lake sediments (107.3). Multivariate statistical analyses such as factor analysis and principal component analysis indicated that REEs were associated and controlled by mixed origin, with similar contributions from anthropogenic and geogenic sources. The speciation study of REEs in ex-tin mining sediments using a modified five-stage sequential extraction procedure indicated that yttrium (Y), gadolinium (Gd), and lanthanum (La) were obtained at higher percentages from the adsorbed/exchanged/carbonate fraction. The average potential mobility of the REEs was arranged in a descending order: Yb > Gd > Y = Dy > Pr > Er > Tm > Eu > Nd > Tb > Sc > Lu > Ce > La, implying that under favorable conditions, these REEs could be released and subsequently pollute the environment.

Matched MeSH terms: Mining*

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