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Water 2016, 8(3), 86; doi:10.3390/w8030086

Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China)

1
Department of Water Environmental Planning, Chinese Academy for Environmental Planning, Beijing 100012, China
2
Institute of Integrated Watershed Management, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
3
Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4
Center for Environmental Science, University of Maryland, Cambridge, MD 21613, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Martin Søndergaard
Received: 8 December 2015 / Revised: 24 January 2016 / Accepted: 29 February 2016 / Published: 4 March 2016
(This article belongs to the Special Issue Lake Restoration and Management in a Climate Change Perspective)
View Full-Text   |   Download PDF [2033 KB, uploaded 4 March 2016]   |  

Abstract

Various multivariate methods were used to analyze datasets of river water quality for 11 variables measured at 20 different sites surrounding Lake Taihu from 2006 to 2010 (13,200 observations), to determine temporal and spatial variations in river water quality and to identify potential pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into two periods (May to November, December to the next April) and the 20 sampling sites into two groups (A and B) based on similarities in river water quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only three variables (water temperature, dissolved oxygen (DO) and five-day biochemical oxygen demand (BOD5)) to correctly assign about 94% of the cases and five variables (petroleum, volatile phenol, dissolved oxygen, ammonium nitrogen and total phosphorus) to correctly assign >88.6% of the cases. In addition, principal component analysis (PCA) identified four potential pollution sources for Clusters A and B: industrial source (chemical-related, petroleum-related or N-related), domestic source, combination of point and non-point sources and natural source. The Cluster A area received more industrial and domestic pollution-related agricultural runoff, whereas Cluster B was mainly influenced by the combination of point and non-point sources. The results imply that comprehensive analysis by using multiple methods could be more effective for facilitating effective management for the Lake Taihu Watershed in the future. View Full-Text
Keywords: river water quality; temporal variation; spatial variation; source identification; multivariate analysis; Lake Taihu river water quality; temporal variation; spatial variation; source identification; multivariate analysis; Lake Taihu
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chen, Y.; Zhao, K.; Wu, Y.; Gao, S.; Cao, W.; Bo, Y.; Shang, Z.; Wu, J.; Zhou, F. Spatio-Temporal Patterns and Source Identification of Water Pollution in Lake Taihu (China). Water 2016, 8, 86.

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