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Exploring the Spatial-Seasonal Dynamics of Water Quality, Submerged Aquatic Plants and Their Influencing Factors in Different Areas of a Lake

机译:探索湖泊不同区域水质,水生植物及其影响因素的时空动态

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The degradation of water quality in lakes and its negative effects on freshwater ecosystems have become a serious problem worldwide. Exploring the dynamics in the associated factors is essential for water pollution management and control. GIS interpolation, principal component analysis (PCA) and multivariate statistical techniques were used to identify the main pollution sources in different areas of Honghu Lake. The results indicate that the spatial distribution of the concentrations of total nitrogen (TN), total phosphate (TP), ammonia nitrogen (NH 4 + –N), and permanganate index (COD Mn ) have similar characteristics and that their values gradually increased from south to north during the three seasons in Honghu Lake. The major influencing factors of water quality varied across the different areas and seasons. The relatively high concentrations of TN and TP, which might limit the growth of submerged aquatic plants, were mainly caused by anthropogenic factors. Our work suggests that spatial analyses combined with PCA are useful for investigating the factors that influence water quality and submerged aquatic plant biomass in different areas of a lake. These findings provide sound information for the future water quality management of the lake or even the entire lake basin.
机译:湖泊水质的下降及其对淡水生态系统的负面影响已成为世界范围内的严重问题。探索相关因素的动态对于水污染的管理和控制至关重要。利用GIS插值,主成分分析(PCA)和多元统计技术来识别洪湖不同地区的主要污染源。结果表明,总氮(TN),总磷酸盐(TP),氨氮(NH 4 + -N)和高锰酸盐指数(COD Mn)的空间分布具有相似的特征,并且它们的值从在洪湖三个季节中,从南到北。水质的主要影响因素因地区和季节而异。 TN和TP的相对较高浓度可能会限制淹没水生植物的生长,主要是由人为因素引起的。我们的工作表明,结合PCA进行空间分析对于调查影响湖泊不同区域水质和水下水生植物生物量的因素非常有用。这些发现为该湖泊乃至整个湖盆的未来水质管理提供了可靠的信息。

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