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Variations in the Distribution of Chl-a and Simulation Using a Multiple Regression Model

文献类型： 外文期刊

作者： Deng, Jiancai 1 ; Chen, Fang 2 ; Hu, Weiping 1 ; Lu, Xin 3 ; Xu, Bin 2 ; Hamilton, David P. 4 ;

作者机构： 1.Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Jiangsu, Peoples R China

2.Monitoring Ctr Hydrol & Water Resources Taihu Bas, Wuxi 214024, Jiangsu, Peoples R China

3.Jiangsu Acad Agr Sci, Inst Agr Resources & Environm, Nanjing 210014, Jiangsu, Peoples R China

4.Griffith Univ, Australian Rivers Inst, Nathan, Qld 4111, Australia

关键词： Chl-a concentrations; water quality; spatiotemporal distribution; simulation; Lake Taihu; eutrophication

期刊名称：INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH （ 影响因子：3.39； 五年影响因子：3.789 ）

ISSN：

年卷期： 2019 年 16 卷 22 期

页码：

收录情况： SCI

摘要： Chlorophyll a (Chl-a) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl-a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl-a in the bay was investigated, and a statistical model to relate the Chl-a concentration to key driving variables was also developed. The monthly Chl-a concentration in Meiliang Bay changed from 2.6 to 330.0 mu g/L, and the monthly mean Chl-a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl-a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl-a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH (r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO3--N; r = -0.28, p < 0.01), dissolved oxygen (DO; r = -0.12, p < 0.01), and Secchi depth (ln(SD); r = -0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl-a concentrations was established (R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl-a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.

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