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Analysis of Spatial-Temporal Variations of Chlorophyll a Concentration and Related Environmental Factors in Chaohu Lake

机译:巢湖叶绿素a含量时空变化及相关环境因子分析。

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Multivariate statistical techniques,such as cluster analysis (CA),correlation analysis and stepwise regression analysis were applied to the data set on water quality of the Chaohu Lake,China,generated during eight years (2000-2007) monitoring at 12 routine national water quality sampling sites.The spatial-temporal variations of chlorophyll a concentration were first explored by using ArcGIS-based kriging method.Throughout the whole lake,the concentration distribution showed a pattern of decreasing from the northwest to the southeast.CA was used to category the 12 sampling sites,by means of the Ward's method.Three significant groups,Group Ⅰ (including Site-2),Group Ⅱ (including Sites-1 and 3) and Group Ⅲ (including Sites-4 to 12) were obtained on the basis of similarity between them.The relationships between chlorophyll a and related environmental factors were analyzed and the Pearson correlation coefficients were obtained through correlation analysis.Stepwise multiple regression analysis was adopted to examine the controlling role of any particular parameter or group of parameters on phytoplankton biomass,using chlorophyll a concentration as the dependent variable,while related physicochemical parameters as the independent variables,the best-fit linear multiple-regression equations for chlorophyll a concentration prediction were established,and the dominating factors for chlorophyll a were simultaneously identified.These results will help researchers and decision-makers to better understand the influence of environmental factors on phytoplankton and to manage eutrophication adaptively in Chaohu Lake.
机译:将巢湖水质数据集采用聚类分析,相关分析和逐步回归分析等多元统计技术,在2000年至2007年的八年间对全国12个常规水质进行监测首先采用基于ArcGIS的克里格法对叶绿素a浓度的时空变化进行了研究。在整个湖泊中,浓度分布呈从西北向东南递减的模式.CA用于对12种植物进行分类在此基础上,获得了三个显着组:Ⅰ组(包括Site-2),Ⅱ组(包括Site-1和3)和Ⅲ组(包括Site-4至12)。分析了叶绿素a与相关环境因子之间的关系,并通过相关分析获得了皮尔森相关系数。逐步多元回归分析采用叶绿素a浓度为因变量,以相关理化参数为自变量,以最合适的线性多元回归方程为参数,考察了任一参数或一组参数对浮游植物生物量的控制作用。建立浓度预测值,并同时确定叶绿素a的主要影响因素。这些结果将有助于研究人员和决策者更好地了解环境因素对浮游植物的影响,并自适应地管理巢湖富营养化。

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