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• W2069703065 endingPage "1471" @default.
• W2069703065 startingPage "1460" @default.
• W2069703065 abstract "Multivariate statistical techniques, cluster analysis, non-parametric tests, and factor analysis were applied to analyze a water quality dataset including 13 parameters at 37 sites of the Three Gorges area, China, from 2003-2008 to investigate spatio-temporal variations and identify potential pollution sources. Using cluster analysis, the twelve months of the year were classified into three periods of low-flow (LF), normal-flow (NF), and high-flow (HF); and the 37 monitoring sites were divided into low pollution (LP), moderate pollution (MP), and high pollution (HP). Dissolved oxygen (DO), potassium permanganate index (COD(Mn)), and ammonia-nitrogen (NH4(+)-N) were identified as significant variables affecting temporal and spatial variations by non-parametric tests. Factor analysis identified that the major pollutants in the HP region were organic matters and nutrients during NF, heavy metals during LF, and petroleum during HF. In the MP region, the identified pollutants primarily included organic matter and heavy metals year-around, while in the LP region, organic pollution was significant during both NF and HF, and nutrient and heavy metal levels were high during both LF and HF. The main sources of pollution came from domestic wastewater and agricultural activities and runoff; however, they contributed differently to each region in regards to pollution levels. For the HP region, inputs from wastewater treatment plants were significant; but for MP and LP regions, water pollution was more likely from the combined effects of agriculture, domestic wastewater, and chemical industry. These results provide fundamental information for developing better water pollution control strategies for the Three Gorges area." @default.
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• W2069703065 date "2011-09-01" @default.
• W2069703065 modified "2023-09-27" @default.
• W2069703065 title "Multivariate analysis of surface water quality in the Three Gorges area of China and implications for water management" @default.
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• W2069703065 doi "https://doi.org/10.1016/s1001-0742(10)60599-2" @default.
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