湖泊型水库水化学成分变异性的结构

D. Nokhrin, M. Derkho, L. Mukhamedyarova, A. Zhivetina
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引用次数: 1

摘要

本文对湖泊型水库水化学参数进行了定性和定量分析,以确定影响其时空变化的因素。根据GOST R 51592-2000的要求,在2019年和2020年的春季(4月)、夏季(7月)和秋季(9月)的每月第一周的平均水平上分三段采集水样。第一个目标(1)为浅层上部(深度2 ~ 4 m);第二个目标(2)是中心部分(深度为5 ~ 7 m),第三个目标(3)是近坝部分(深度为12.2 m)。使用无限主成分分析(PCA)技术和有限冗余分析(RDA)技术对所获得的数据进行统计分析。在P<0.05时,认为这些影响具有统计学意义,并且在P<0.10时可用于讨论。结果发现,尽管洪水增加了水库水中的化学成分水平,但除铁、铜、锰、锌、镍和铅超过MPCVR的1.1倍至45.0倍外,大多数化学成分满足捕捞水域的要求。用主成分分析方法估计,水库水化学成分的总变率与季节的相关性为71.4%。用RDA方法在单回归量模型中得到了类似的结果。当在RDA模型中考虑所有因素时,水化学成分的变异性受年度季节的影响为74.3%,研究年份的影响为11.1%,目标地点的影响为1.9%。在PCA和RDA方法中,水的不明原因变异比例的主要指标是锰、碳酸氢盐、铅和铝以及pH。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
THE STRUCTURE OF THE VARIABILITY OF THE HYDROCHEMICAL COMPOSITION OF WATER IN LAKE-TYPE RESERVOIR
A qualitative and quantitative analysis of hydrochemical parameters of water is given in order to identify the factors that determine their spatial and temporal changes in a lake-type reservoir. Water samples were taken in 2019 and 2020 from the average level in spring (April), summer (July) and autumn (September) in the first week of the month in accordance with the requirements of GOST R 51592-2000 in three sections. The first target (1) is the shallow upper part (depth from 2 to 4 m); the second target (2) is the central part (depth from 5 to 7 m) and the third target (3) is the near – dam part (depth up to 12.2 m). Statistical analysis of the obtained data was performed using the unlimited Principal component analysis (PCA) technique and the limited redundancy analysis (RDA) technique. The effects were considered statistically significant at P<0.05, and useful for discussion-at P<0.10. It was found that, despite the flood increase in the level of chemical components in the water of the reservoir, most of them meet the requirements for fishing waters, with the exception of iron, copper, manganese, zinc, nickel and lead, which exceed the MPCVR from 1.1 to 45.0 times. The total variability of the hydrochemical composition of water in the reservoir, estimated by the PCA method, depends on the season of the year by 71.4 %. A similar result was obtained by the RDA method in a model with a single regressor. When all factors are taken into account in the RDA model, the variability of the water chemical composition is affected by the season of the year by 74.3 %, the year of research by 11.1 %, and the location of the target by 1.9 %. The primary indicators of water for the proportion of unexplained variability in both the PCA and RDA methods are manganese, bicarbonates, lead and aluminum, and pH.
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