Surface Water Quality Monitoring Site Optimization for Poyang Lake, the Largest Freshwater Lake in China


In this paper, we propose a coupled method to optimize the surface water quality monitoring sites for a huge freshwater lake based on field investigations, mathematical analysis, and numerical simulation tests. Poyang Lake, the largest freshwater lake in China, was selected as the research area. Based on the field investigated water quality data in the 5 years from 2008 to 2012, the water quality inter-annual variation coefficients at all the present sites and the water quality correlation coefficients between adjacent sites were calculated and analyzed to present an optimization scheme. A 2-D unsteady water quality model was established to get the corresponding water quality data at the optimized monitoring sites, which were needed for the rationality test on the optimized monitoring network. We found that: (1) the water quality of Piaoshan (No. 10) fluctuated most distinguishably and the inter-annual variation coefficient of NH3-N and TP could reach 99.77% and 73.92%, respectively. The four studied indexes were all closely related at Piaoshan (No. 10) and Tangyin (No. 11), and the correlation coefficients of COD and NH3-N could reach 0.91 and 0.94 separately. (2) It was suggested that the present site No. 10 be removed to avoid repeatability, and it was suggested that the three sites of Changling, Huzhong, and Nanjiang be added to improve the representativeness of the monitoring sites. (3) According to the rationality analysis, the 21 optimized water quality monitoring sites could scientifically replace the primary network, and the new monitoring network could better reflect the water quality of the whole lake.

DOI: 10.3390/ijerph111111833

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@inproceedings{Wang2014SurfaceWQ, title={Surface Water Quality Monitoring Site Optimization for Poyang Lake, the Largest Freshwater Lake in China}, author={Hua Wang and Mengan Wu and Yanqing Deng and Chunyan Tang and Rui Yang}, booktitle={International journal of environmental research and public health}, year={2014} }