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Effects Of Wuliangsuhai Water Environmental Factors On Nutritional Status Of Lakes

Posted on:2021-02-03Degree:MasterType:Thesis
Country:ChinaCandidate:J L ZhouFull Text:PDF
GTID:2381330605473582Subject:Engineering
Abstract/Summary:PDF Full Text Request
The climate in the north of China is arid and cold,and freshwater resources are particularly precious.Wuliangsuhai is located in the Inner Mongolia Autonomous Region and plays an irreplaceable role in the economic development of the Hetao region.It is the largest multifunctional freshwater lake in the world at the same latitude.However,due to the destruction of human activities,the problem of eutrophication of the Wuliangsuhai has become increasingly serious,which has attracted great attention from environmental scholars,and extensive research has been carried out.Wuliangsuhai is located in a region with high latitudes and clear seasons,and the freeze period is long.Therefore,the nutritional status and physical and chemical characteristics of the lake must be different from other lakes in southern China.This article uses Wuliangsuhai as a research area.Based on the indicators of water nutrition status and water environmental factors in different seasons of the lake,the relationship between the water nutrition status and water environmental factors in different seasons,the change of this relationship under different nutrition status were studied.This paper selects the research period from 2015 to 2019,aim to lay the foundation for the following research.the temporal and spatial variation characteristics of the eutrophication state of Wuliangsuhai in different seasons was analyzed,the eutrophication status of lake water in recent years was defined,and the characteristics of launching environmental factors in different seasons was analyzed.The most suitable regression model was established by panel regression analysis method.Through ST AT A software programming,eight water environmental factors such as electrical conductivity(EC),salinity(SAL),total dissolved solid(TDS),dissolved oxygen(DO),water depth(H),redox potential(ORP),pH value and water temperature(WT)were selected as explanatory variables.The key water environmental factors affecting the explained variables(comprehensive nutrition index TLI)in different seasons were selected.the results showed that electrical conductivity(EC),salinity(SAL)and total dissolved solid(TDS)were the key water environmental factors affecting lake nutrition status in different seasons,and dissolved oxygen(DO),was the characteristic water environment factor that had great influence on lake nutrition status in spring.While,salinity(SAL),in summer,pH value in autumn,water depth(H)in winter plays the major role as well,respectively.On the basis of the above,quantile regression was carried out to quantile the nutritional status of the lake and study the change of the absolute value of the correlation coefficient(the influence of water environmental factors on the nutritional status of the lake).However,the regression of spring,summer,autumn and winter has come to a consistent conclusion,that is,with the increase of lake nutritional status,the influence of water environmental factors on comprehensive nutritional status index decreases.Taking spring as an example,the correlation coefficient of DO gradually decreases from 1.151?1.083?0.965?0.825 when the nutrient status of lakes gradually increases with the 10%quantile,25%quantile,50%quantile and 75%quantile of the comprehensive nutrient status index.Then a stratified regression was carried out with the TLI value of 60,and it was found that when the lake was in the state of moderate eutrophication or above,the influence of water environmental factors on the nutritional status was weak or non-existent.Finally,combined with the actual situation of water quality in recent years,the fundamental,targeted and lasting measurements to improve the nutritional status of Wuliangsuhai lake are put forward.
Keywords/Search Tags:Wuliangsuhai Lake, Trophic state of lake, Water environmental factors, Panel regression, Quantile Regression, Hierarchical regression
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