Using correlation and multivariate statistical analysis to identify hydrogeochemical processes affecting the major ion chemistry of waters: a case study in Laoheba phosphorite mine in Sichuan, China

Wu, Jianhua; Li, Peiyue; Qian, Hui; Duan, Zhao; Zhang, Xuedi

doi:10.1007/s12517-013-1057-4

Cited by 269 publications

(74 citation statements)

References 20 publications

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“…Hierarchical cluster analysis (HCA), which successively joins the most similar observations, is widely used in hydrochemical analyses to identify different chemical characteristics [6,[27][28][29]. In this study, HCA was applied using Euclidian distance with Ward's linkage method for computing cluster distance.…”

Section: Hierarchical Cluster Analysismentioning

confidence: 99%

Groundwater Chemistry Regulated by Hydrochemical Processes and Geological Structures: A Case Study in Tongchuan, China

Qian

et al. 2018

Water

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Abstract:Knowledge of hydrochemical processes in groundwater helps to identify the relationship between geochemical processes and groundwater quality as well as to understand the hydrochemical evaluation of groundwater, which is important for the sustainable management of groundwater resources. This study aims to identify the chemical characteristics of groundwater in the area of Tongchuan City, China. A total of 58 groundwater samples were collected. A hierarchical cluster analysis divided samples into three clusters and six sub-clusters (cluster 1a, 1b, 2a, 2b, 3a, 3b) according to hydrochemical facies. Graphical plots of multiple ionic ratios, saturation indices, and ion exchange indices were employed to examine hydrochemical processes that result in different hydrochemical facies of each cluster. Results show the predominance of carbonate and silicate weathering in cluster 1, silicate weathering in cluster 2, and carbonate weathering in cluster 3. Ionic exchange is a ubiquitous process among all clusters. The distribution of clusters is related to the regional geology, which may result in different hydrochemical processes. Two stratigraphic sections identify the differences in hydrochemical processes resulting from complex stratum structures and varied aquifer media. Cluster 2a shows an interesting difference in water chemistry along the groundwater flow path. Further study by oxygen and hydrogen isotope indicated that mixing between Quaternary and the Permian aquifers resulting from faulting is the main reason for the distinctive characteristic of cluster 2a.

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Section: Hierarchical Cluster Analysismentioning

confidence: 99%

Groundwater Chemistry Regulated by Hydrochemical Processes and Geological Structures: A Case Study in Tongchuan, China

Qian

et al. 2018

Water

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“…The water samples were collected from bore wells after 10 minutes of pumping and transferred into precleaned polyethylene bottles. Electrical conductivity and pH were measured in the field immediately after sampling, while the determination of major cations (Na + (Wu et al 2014). In this study, the IBM® SPSS v.20 software was used to analyze the dimensional groundwater quality data of the samples including EC, pH, TDS, Ca 2+ , Mg 2+ , Na + , K + , HCO 3 − , CO 3 2− , F−, SO 4 2− , Cl−, hardness, alkalinity.…”

Section: Sample Collection and Analysismentioning

confidence: 99%

“…Two types of hierarchical cluster analysis (HCA) are usually used: Q mode HCA and R mode HCA. Q mode HCA can be used to highlight the spatial relationships among the sample points, while R mode HCA is usually applied to analyse the parameters by clustering them into groups or facies based on their similarity (Wu et al 2014). In this study, Q mode HCA was adopted.…”

Section: Sample Collection and Analysismentioning

confidence: 99%

“…Determining the number of principal components is important in PCA. It can be determined either by the total variance for which the total variance explained by the principal components should be higher than 85% or by the Kaiser criterion for which only the components with eigenvalues greater than one can be retained (Cloutier et al 2006;Wu et al 2014). In this study, the Kaiser criterion was adopted to determine the number of components.…”

Section: Sample Collection and Analysismentioning

confidence: 99%

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Assessment of Groundwater Quality of Udayagiri area, Nellore District, Andhra Pradesh, South India Using Multivariate Statistical Techniques

Nagaraju¹,

Thejaswi²,

Sreedhar³

2016

Earth sci. res. j.

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Hydrogeochemical studies were carried out in and around Udayagiri area of Andhra Pradesh in order to assess the chemistry of the groundwater and to identify the dominant hydrogeochemical processes and mechanisms responsible for the evolution of the chemical composition of the groundwater. Descriptive statistics, correlation matrices, principal component analysis (PCA), together with cluster analysis (CA) were used to gain an understanding of the hydrogeochemical processes in the study area. PCA has identified 4 main processes influencing the groundwater chemistry viz., mineral precipitation and dissolution, seawater intrusion, cation exchange, and carbonate balance. Further, three clusters C1, C2 and C3 were obtained. Samples from C1 contain high level of Cl − and may be due to the intensive evaporation and contamination from landfill leachate. Most of the samples from C2 are located closer to the sea and the high level of Na + +K + in these samples may be attributed to seawater intrusion. The geochemistry of water samples in C3 are more likely to originate from rock weathering. This has been supported by Gibbs diagram. The groundwater geochemistry in the study area is mostly of natural origin, but is influenced to some degree by human activity.Se realizaron estudios hidrogeoquímicos en y alrededor del área Udayagiri de Andhra Pradesh para evaluar la química del agua subterránea e identificar los procesos hidrogeoquímicos dominantes y los mecanismos responsables de la evolución en la composición química del agua subterránea. Se utilizaron estadísticas descriptivas, matrices de correlación, análisis de componentes principales, al igual que análisis de grupos, para obtener y entender los procesos hidrogeoquímicos en el área de estudio. Los análisis de componentes principales identificaron cuatro procesos determinantes que influenciaron la química del agua subterránea, estos son, la precipitación y disolución de minerales, l intrusión de agua marina, el intercambio de cationes y el equilibrio de carbonatos. De esta forma se obtuvieron tres grupos, C1, C2, y C3. Las muestras del grupo C1 contienen un alto nivel de Cl -, lo que podría deberse a la intensa evaporación y contaminación de los lixiviados de rellenos sanitarios. Muchas de las muestras del grupo C2 se ubican cerca del mar y el alto nivel de Na++K+ podría atriburise a la intrusión de agua marina. La geoquímica de las muestras de agua en el grupo C3 probablemente se desprende de la meteorización de rocas. El diagrama de Gibbs valida estos resultados. La geoquímica del agua subterránea en el área de estudio es principalmente de origen natural pero también está influenciado por la actividad humana en algún grado. EARTH SCIENCES RESEARCH JOURNALEarth Sci. Res. J. Vol. 20, No. 4 (December, 2016): E1-E7 ABSTRACT RESUMEN

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“…This means that multiple factors are typically needed for comprehensive determinations. Water quality and isotopic analysis have been used to determine water inflow sources, with the assistance of multivariate statistics (Wu et al 2014). The statistical methods used have included cluster analysis and discrimination analysis, and non-linear analysis methods such as grey relational analysis, fuzzy mathematics, and artificial neural networks (Li et al 2010(Li et al , 2015Pan et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Groundwater Source Discrimination and Proportion Determination of Mine Inflow Using Ion Analyses: A Case Study from the Longmen Coal Mine, Henan Province, China

Sun

Gao

et al. 2018

Mine Water Environ

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Complex hydrogeological conditions in China's coal mines have contributed to frequent mine water disasters. A simple and effective method to determine water inflow sources and paths is therefore essential. The Longmen Mine, located in Henan Province, in central China was used as a case study. A Piper diagram and cluster analysis were used to screen the characteristic values of 18 water samples from potential aquifers. A comprehensive fuzzy evaluation of the groundwater ions was carried out to determine the main source of the total mine inflow. Then, based on conservation of ionic masses, a matrix function was established to calculate the groundwater recharge composition. Finally, using measured water inflows for the Cambrian limestone aquifer, the calculated and observed results were compared. The results showed that the Carboniferous Taiyuan Formation limestone aquifer (the L 7 limestone aquifer) accounts for 60.8% of the total mine inflow, while the Cambrian limestone and roof sandstone aquifers account for 34.8 and 4.4% of the inflow, respectively. The normal mine inflow totals about 19,200 m 3 /day, of which 6,840 m 3 /day is from the Cambrian limestone aquifer. This agrees well with the calculated value of 6,720 m 3 /day. Thus, the method is feasible and reliable.

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Using correlation and multivariate statistical analysis to identify hydrogeochemical processes affecting the major ion chemistry of waters: a case study in Laoheba phosphorite mine in Sichuan, China

Cited by 269 publications

References 20 publications

Groundwater Chemistry Regulated by Hydrochemical Processes and Geological Structures: A Case Study in Tongchuan, China

Groundwater Chemistry Regulated by Hydrochemical Processes and Geological Structures: A Case Study in Tongchuan, China

Assessment of Groundwater Quality of Udayagiri area, Nellore District, Andhra Pradesh, South India Using Multivariate Statistical Techniques

Groundwater Source Discrimination and Proportion Determination of Mine Inflow Using Ion Analyses: A Case Study from the Longmen Coal Mine, Henan Province, China

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