Water Science & Technology: Water Supply Vol 12 No 4 pp 439–450 © IWA Publishing 2012 doi:10.2166/ws.2012.007

Characterization and variations of dissolved organic matter in the Lake Taihu area of China

Yong Qiu, Hanchang Shi, He Jing, Rui Liu, Qiang Cai, Minoru Takemura and Satoshi Haraguchi

Lake Taihu in China is a eutrophicated lake surrounded by industrial and urbanized zones, thus its water quality often suffers from organic and nutrient contaminants. In this paper, a 1 year water quality survey was conducted around the lake and statistical analysis tools were used to characterize the variations of organic pollutants. Analysis of variance (ANOVA), cluster analysis and principal component analysis (PCA) confirm the seasonal and spatial variations of surface water quality in Lake Taihu. Surface water quality is better during the wet season and worse downstream during the dry season. The dissolved organic matter was further analyzed using a parallel factor analysis (PARAFAC) model with three-dimensional excitation-emission fluorescence matrices. Four components were extracted from the fluorescence data, namely, two autochthonous biodegradation products (C1: amino acids, C4: protein-like materials) and two humic-like substances (C2: from microbial processing, C3: terrestrial). C1 and C4 were dominant in the chromophoric dissolved organic matter (CDOM) fluorophores; this result is similar to those of other inland water bodies in China. The CDOM fluorophores showed similar seasonal and spatial variations with common water quality indices, with the exception of the seasonal responses of C2 in winter. Bivariance correlations between the organic and nutrient concentrations and the fluorescence intensities of the CDOM fluorophores imply possible common sources of the different contaminants. This paper exemplifies advanced statistical methods as a useful tool in understanding the behavior of contaminants in inland fresh water systems.

Keywords: dissolved organic matter; Lake Taihu area; parallel factor analysis; statistical data analysis

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