Using integrated multivariate statistics to assess the hydrochemistry of surface water quality, Lake Taihu basin, China

  • Xiangyu Mu Syracuse University, United States.
  • James Brower Syracuse University, United States.
  • Donald I. Siegel | Syracuse University, United States.
  • Anthony J. Fiorentino II Syracuse University, United States.
  • Shuqing An Nanjing University, China.
  • Ying Cai Nanjing University, China.
  • Delin Xu Nanjing University, China.
  • Hao Jiang Nanjing University, China.


Natural factors and anthropogenic activities both contribute dissolved chemical loads to  lakes and streams.  Mineral solubility,  geomorphology of the drainage basin, source strengths and climate all contribute to concentrations and their variability. Urbanization and agriculture waste-water particularly lead to aquatic environmental degradation. Major contaminant sources and controls on water quality can be asssessed by analyzing the variability in proportions of major and minor solutes in water coupled to mutivariate statistical methods.   The demand for freshwater needed for increasing crop production puulation and industrialization occurs almost everywhere in in China and these conflicting needs have led to widespread water contamination. Because of heavy nutrient loadings from all of these sources, Lake Taihu (eastern China) notably suffers periodic hyper-eutrophication and drinking water deterioration, which has led to shortages of freshwater for the City of Wuxi and other nearby cities. This lake, the third largest freshwater body in China, has historically beeen considered a cultural treasure of China, and has supported long-term fisheries. The is increasing pressure to remediate the present contamination which compromises both aquiculture and the prior economic base centered on tourism.  However, remediation cannot be effectively done without first characterizing the broad nature of the non-point source pollution. To this end, we investigated the hydrochemical setting of Lake Taihu to determine how different land use types influence the variability of surface water chemistry in different water sources to the lake. We found that waters broadly show wide variability ranging from  calcium-magnesium-bicarbonate hydrochemical facies type to mixed sodium-sulfate-chloride type. Principal components analysis produced three principal components that explained 78% of the variance in the water quality and reflect three major types of water chemistry. Agricultural land use is associated with greater concentrations of nutrients; urban areas with high concentrations of sodium, chloride, sulfate, fluoride and potassium; and natural weathering with calcium, magnesium and bicarbonate. Discriminant analysis and hierarchical cluster analysis produce complementary and similar results. Broadly speaking, future remediation to reduce nutrient loadings to the lake or industrial contamination can now be focused on specific land use practices, which are readily identifiable by using statistics in conjunction with GIS.  



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Author Biographies

Xiangyu Mu, Syracuse University
Department of Earth Sciences
James Brower, Syracuse University
Department of Earth Sciences
Donald I. Siegel, Syracuse University
Department of Earth Sciences
Anthony J. Fiorentino II, Syracuse University
Department of Earth Sciences
Shuqing An, Nanjing University
Department of Life Science
Ying Cai, Nanjing University
Department of Life Science
Delin Xu, Nanjing University
Department of Life Science
Original Articles
Principal component analysis, discriminant analysis, hierarchical cluster analysis, geographical information system (GIS), surface water quality, water-rock interaction, land use types.
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How to Cite
Mu X, Brower J, Siegel DI, Fiorentino II AJ, An S, Cai Y, Xu D, Jiang H. Using integrated multivariate statistics to assess the hydrochemistry of surface water quality, Lake Taihu basin, China. J Limnol [Internet]. 2014Sep.24 [cited 2021May7];74(2). Available from: