Title |
Research on self-purification capacity of Lake Taihu
|
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Published in |
Environmental Science and Pollution Research, December 2014
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DOI | 10.1007/s11356-014-3920-6 |
Pubmed ID | |
Authors |
Tao Han, Hongju Zhang, Weiping Hu, Jiancai Deng, Qinqin Li, Guie Zhu |
Abstract |
An effective measure to cope with eutrophication of lakes is to remove nutrients that can cause algal blooming by taking advantage of natural water purification processes. Here, the term "purification" is defined, in a wide sense, as the potential role of a water body to contribute to the reduction of pollutants and thus controlling eutrophication. Also regarded as a kind of ecological regulating services, biological purification involves various processes concerning seasonal nutrient fixation, such as uptake by aquatic macrophyte, biofouling onto foliage substrates, feeding by organisms in higher trophic level, and eternal loss or removal of substance from the water. In order to evaluate the water purification ability, a numerical lake ecosystem model (EcoTaihu) was developed and applied to Lakes Taihu. The model includes the biological interactions between pelagic compartments (phytoplankton and zooplankton, detritus, dissolved organic matter, fish, and nutrients). Under dynamic forcing of meteorological and hydrological parameters, the model was run over years to evaluate the annual nutrient cycles and purification functions. The reproducibility of the model was validated for water body by comparison with the field data from the water quality monitoring campaign. Numerical results revealed that self-purification capacity of nitrogen of Lake Taihu in years 2006, 2008, and 2010 is 4.00 × 10(4), 4.27 × 10(4), and 4.11 × 10(4) ton, respectively, whereas self-purification capacity of phosphorus of Lake Taihu in years 2006, 2008, and 2010 is 1.56 × 10(3), 1.80 × 10(3), and 1.71 × 10(3) ton, respectively. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 6 | 25% |
Student > Ph. D. Student | 3 | 13% |
Researcher | 2 | 8% |
Student > Doctoral Student | 1 | 4% |
Professor | 1 | 4% |
Other | 3 | 13% |
Unknown | 8 | 33% |
Readers by discipline | Count | As % |
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Environmental Science | 7 | 29% |
Agricultural and Biological Sciences | 3 | 13% |
Engineering | 2 | 8% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Chemistry | 1 | 4% |
Other | 1 | 4% |
Unknown | 9 | 38% |