DocumentCode
3331037
Title
Notice of Retraction
Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of the Lakes in Wuhan, China
Author
Yang Yang ; Liang Shengwen ; Fang Jiande ; Zhang Mailang ; Zuo Guoxing ; Zhang Can ; Yang Xue ; He Zhen ; Hu Xiaojing ; Zhong Qiu ; Guo Jia ; Xiong Li ; Liu Deli
Author_Institution
Res. Center of Hydrobiology, Jinan Univ., Guangzhou, China
fYear
2011
fDate
10-12 May 2011
Firstpage
1
Lastpage
4
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Multivariate statistical techniques, such as principal component analysis (PCA) , cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of the lakes in Wuhan, generated in 2009 monitoring of 21 parameters at 70 different lakes (1470 observations) located at the 7 core districts of Wuhan, Hubei Province, China. Results reveal that Potassium Permanganate Index, Biochemical oxygen demand (BOD), Ammonical nitrogen NH4-N, Total Phosphate TP, Total nitrogen, Chl-a were the parameters that are the most important ones in assessing variations of water quality in the lake. Hierarchical cluster analysis grouped 70 lakes into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) lakes, based on the similarity of water quality characteristics. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective lakes water quality management.This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Multivariate statistical techniques, such as principal component analysis (PCA) , cluster analysis (CA) were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of the lakes in Wuhan, generated in 2009 monitoring of 21 parameters at 70 different lakes (1470 observations) located at the 7 core districts of Wuhan, Hubei Province, China. Results reveal that Potassium Permanganate Index, Biochemical oxygen demand (BOD), Ammonical nitrogen NH4-N, Total Phosphate TP, Total nitrogen, Chl-a were the parameters that are the most important ones in assessing variations of water quality in the lake. Hierarchical cluster analysis grouped 70 lakes into three clusters, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) lakes, based on the similarity of water quality characteristics. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective lakes water quality management.This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.
Keywords
data analysis; lakes; principal component analysis; water pollution; water quality; AD 2009; China; Hubei Province; KMnO4; Wuhan Lake; ammonical nitrogen NH4-N analysis; biochemical oxygen demand; cluster analysis; complex data analysis; high polluted lake; lake water quality management; large complex water quality data; medium polluted lake; multivariate statistical technique; pollution source identification; potassium permanganate index; principal component analysis; spatial variation evaluation; surface water quality assessment; surface water quality monitoring station; total nitrogen analysis; total phosphate analysis; water quality characteristics; Board of Directors; Lakes; Principal component analysis; Rivers; Water pollution; Water resources;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Biomedical Engineering, (iCBBE) 2011 5th International Conference on
Conference_Location
Wuhan
ISSN
2151-7614
Print_ISBN
978-1-4244-5088-6
Type
conf
DOI
10.1109/icbbe.2011.5780731
Filename
5780731
Link To Document