Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China

Wang, Jie; Liu, Guijian; Liu, Houqi; Lam, Paul K.S.

doi:10.1016/j.scitotenv.2017.01.088

Cited by 372 publications

(201 citation statements)

References 45 publications

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“…The application of multivariate statistical methods to environmental monitoring datasets is beneficial to highlight information that is not available at first glance, especially when the results of the univariate analysis reveal statistically significant associations for several dataset variables. The factor analysis (FA), principal component analysis (PCA), and cluster analysis (CA) are the most commonly used multivariate analysis methods for complex environmental datasets assessment . Complex structures within the datasets could be extracted without losing any information by reducing them to a few dominating factors .…”

Section: Methodsmentioning

confidence: 99%

“…The FA/PCA and HCA were performed on the z‐scale transformed Khibiny Alkaline Massif groundwater dataset to reduce the dimensionality of the dataset variables to several factors and group them into clusters. The calculated values of factor loadings using FA/PCA were classified as strong, moderate, and weak according to the absolute loading values of >0.75, 0.75‐0.50, and 0.50‐0.30, respectively . The HCA results were interpreted using a dendrogram displaying the distance level at which there is a merger of objects and clusters.…”

Section: Methodsmentioning

confidence: 99%

“…It is well‐known that natural processes and anthropogenic influence can change the quality of natural water resources, limiting the potential for water use for human consumption as well as industrial and agricultural purposes . The contamination of drinking water with metals, due to their potential acute (occurring in the short term) and/or chronic (occurring over the long term) adverse effects on human health, can lead to life‐threatening cancers and neurological disorders .…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

2019

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Groundwater is a major source for the water supply of households in the mining‐intensive area of Khibiny Alkaline Massif, Kola Peninsula, in the Arctic. There are an increasing number of signs of groundwater quality deterioration in the area caused by the presence of elevated aluminum concentrations. Groundwater quality studies using univariate and multivariate statistical methods and the Water Quality Index were conducted to analyze a field dataset including 12 groundwater quality parameters monitored between 1999 and 2012. Descriptive statistics showed that the monitored water did not meet the established drinking water standards for aluminum concentration and pH level. The calculated Spearman correlation coefficient matrix revealed statistically significant associations (α‐level = .05) between Al concentrations and pH values, concentrations of SO42−, NO3−, Cl−, and TDS. Factor analysis using the principal component analysis extraction method (FA/PCA) identified four major influencing factors. Altogether the factors captured 67.53% of the dataset total variance. The outcomes of the hierarchical cluster analysis (HCA) revealed that the 12 monitored groundwater quality parameters can be grouped into three clusters where the concentration of Al and pH level formed a separate cluster. The calculated score values of the Canadian Council of Ministers of the Environment Water Quality Index indicated a deterioration of groundwater quality over the monitoring period.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

2019

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“…1.3 µg/L by European Commission [16]. Similarly, Wang et al [17] recorded very high concentration of Pb i.e. 154.96±193.34 µg/L in Huaihe River, China, because of both natural and man-made activities.…”

Section: Dissolved Pb Concentration In Raw Watermentioning

confidence: 99%

Untitled

2017

MOJT

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Safe water supply at Langat River Basin, Malaysia is very crucial being Langat River the prime source of raw water in the basin. Although several study both river and supply water at the Langat Basin determined higher concentration of dissolved lead (Pb), however, there is no significant study that investigates the Pb concentration in the drinking water supply chain of the basin. Therefore, the review of secondary literature explores the status of dissolved Pb in drinking water supply chain at the Langat Basin in comparison with some other countries. Apart from man-made activities, the higher concentrations of dissolved Pb both in the raw and supply water are mainly from the natural weathering of Pb minerals within the granite rock of Titiwangsa Mountain Range of Langat Basin and the corrosion of galvanized iron pipe, respectively. Therefore, the use of reverse osmosis technology to treat both the raw and supply water at the basin could ensure safe drinking water supply as well as sound health of the populations.

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“…Currently, there are several main methods of water quality evaluation including single factor evaluation, principal component analysis and comprehensive water quality identification index [5]- [12]. The mechanism of the single factor evaluation method is that using the classification of the worst single index of water quality to determine the classification of the comprehensive water quality; the method is simple and clear, and can directly attain the relationship between water quality and evaluation criteria, but fails to get a comprehensive evaluation, furthermore, the accuracy of the evaluation results is poor.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

Li¹,

Wu²,

Chen³

et al. 2017

CWEEE

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To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

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Multivariate statistical evaluation of dissolved trace elements and a water quality assessment in the middle reaches of Huaihe River, Anhui, China

Cited by 372 publications

References 45 publications

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Assessment of Khibiny Alkaline Massif groundwater quality using statistical methods and water quality index

Untitled

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

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