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Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China

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Title: Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China
Authors: Huang, J, Chen, K, Jiao, JJ, Huang, R
Source: Environmental Pollution. 147:771-780
Publisher Information: Elsevier BV, 2007.
Publication Year: 2007
Subject Terms: Anions, China, Chemical - analysis, 0207 environmental engineering, Fresh Water, 02 engineering and technology, Principal Component Analysis - methods, 01 natural sciences, Anions - analysis, Environmental Monitoring - methods, Oxygen - analysis, Cluster Analysis, Water Pollutants, Water Pollutants, Chemical - analysis, 14. Life underwater, 0105 earth and related environmental sciences, Principal Component Analysis, Statistical, Fresh Water - analysis - chemistry, 6. Clean water, Trace Elements, Oxygen, Trace Elements - analysis, Seasons, Factor Analysis, Statistical, Factor Analysis, Oxidation-Reduction, Water Pollutants, Chemical, Environmental Monitoring
Description: Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies.
Document Type: Article
Language: English
ISSN: 0269-7491
DOI: 10.1016/j.envpol.2006.09.002
Access URL: https://pubmed.ncbi.nlm.nih.gov/17134805
https://core.ac.uk/display/37905191
http://hub.hku.hk/handle/10722/72725
https://inis.iaea.org/Search/search.aspx?orig_q=RN:39002698
https://hydro.geo.ua.edu/jiao/research/fullpaper/chenkp1.pdf
http://www.sciencedirect.com/science/article/pii/S0269749106005318
https://www.sciencedirect.com/science/article/pii/S0269749106005318
http://hdl.handle.net/10722/72725
Rights: Elsevier TDM
Accession Number: edsair.doi.dedup.....3910832788fec95b70a12381a1a37d07
Database: OpenAIRE
Description
Abstract:Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies.
ISSN:02697491
DOI:10.1016/j.envpol.2006.09.002