韩国15个大型淡水湖水质分析及叶绿素a的多元统计预测

Dong Hwi Lee, Eue-tae Kang, J. Joo, Hyeon Woo Go, C. Ahn, Yong Hyeon Bae, Kwang-Duck Song
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引用次数: 0

摘要

目的:大型淡水湖是淡水湖中储水量在500万吨以上的淡水湖。它位于流域的末端,复杂多样的污染物从宽阔的流域流入,具有在湖中滞留时间较长、蓄积时间较大的特点。由于淡水湖附近农业用水的反复回用,以及复杂的用水方式和已发生的排水结构,导致农业用面源不断流入和积累,水污染日益加剧。本研究利用各种水质因子进行多元统计分析,得出大型淡水湖的主要污染源,为淡水湖的有效管理提供依据。通过对未来淡水湖藻类生长特征的分析,预测未来淡水湖藻类的叶绿素-a(Chl-a),找出影响藻类生长的主要因素,并以此为依据制定有效的水质改善对策。方法:以环境部运营的水环境信息系统中的甘月(GW)、金刚(GG)、锦湖(GeH)、南阳(NY)、大湖(DH)、富南(BN)、五桥(SG)、峨山(AS)、永山(YS)、永南(YA)为例,收集2011年1月至2020年12月10年的逐月水质数据。以高兴(GoH)、郡内(GN)、布沙(BS)、石门(SM)、海南(HN)为例,从2015年4月到2019年3月的5年时间里,每季度收集一次湖泊的水质数据。结果与讨论:通过韩国营养状态指数(TSIko)分析,西海中部大部分大型淡水湖为富营养型,西海南部所有大型淡水湖均为富营养型。根据因子分析和主成分分析的结果,应根据大型淡水湖的类型,制定有机质、养分和藻华的防治对策。对Chl-a预测进行多元线性回归分析发现,西海中部大尺度淡水湖的解释率高于西海南部大尺度淡水湖,但分析结果均在0.8以下,精度不高。结论:西海中部淡水湖的BOD、COD、TOC、T-N和T-P浓度比西海南部淡水湖的水质恶化程度更严重,需要对有机污染物和营养物质进行管理。通过因子分析,判断出有机质&藻类类型、营养类型,并进行复杂类型分类,需要针对每种类型采取相应的水质管理措施。所有用于预测Chl-a的回归模型均以p<0.05进行分析,各预测公式的自变量对解释Chl-a有意义。Chl-a受各种水质因素的复杂作用而变化,同时判断还应考虑降水、日照时间、流量等水质外部产品的影响。今后,如果将较长时期的水质因素与外部因素进行综合回归,通过增大R2值,可以得到解释率稍高的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water Quality Analysis and Chl-a Prediction of 15 Large-scale Freshwater Lakes in Korea by Multivariate Statistical Analysis
Objectives:A large-scale freshwater lake is a freshwater lake with a storage of 5,000,000 tons or more among freshwater lakes. It is located at the end of the basin, so complex and various pollutants flow in from a wide basin, and it has a characteristic of longer retention and greater accumulation for a long time in the lake. Since the continuous inflow and accumulation of non-point pollution sources for agricultural purpose occurred due to the repeated reuse of agricultural water near freshwater lakes, and the complex water use and occurred drainage structure, water pollution is aggravating. In this study, the main pollution sources of large-scale freshwater lakes were derived through multivariate statistical analysis using various water quality factors for efficient freshwater lake management. The analysis is performed out to predict the future Chlorophyll-a(Chl-a) and to identify major factors affecting algal growth, and to use them as establish effective countermeasures to improve water quality based on the characteristics of freshwater lakes algal growth the future.Methods:In the case of GanWol(GW), GeumGang(GG), GeumHO(GeH), NamYang(NY), DaeHo(DH), BuNam(BN), SapGyo(SG), ASan(AS), YoungSan(YS), YoungAm(YA) in the water environment information system operated by the Ministry of Environment, water quality data on a monthly basis for 10 years from January, 2011 to December, 2020 were collected from three point measurement located in the lake. In the case of GoHeung(GoH), GunNae(GN), Busa(BS), SeokMoon(SM), and HaeNam(HN), water quality data were collected on a quarterly basis for 5 years from April 2015 to March 2019 from one point measurement located in the lake.Results and Discussion:As a result of Korean trophic state index (TSIko) analysis, most of the large-scale freshwater lakes in the central region of the West Sea were classified as Hypertrophic, and all of the large-scale freshwater lakes in the southern region of the West Sea were classified as Eutrophic. Based on the results from factor analysis and principal component analysis, countermeasures against organic matters, nutrients, and algal bloom in terms of type of large-scale freshwater lake should be prepared. As a result of multiple linear regression analysis for Chl-a prediction, large-scale freshwater lakes in the central region of the West Sea had higher explanatory rates than large-scale freshwater lakes in the southern region of the West Sea, but all were analyzed below 0.8, suggesting that the accuracy was not high.Conclusions:BOD, COD, TOC, T-N, and T-P concentration of freshwater lakes in the central region of the West Sea have more deterioating water quality than freshwater lakes in the southern region of the West Sea, suggesting that organic pollutants and nutrients need to be managed. As a result of the factor analysis, it is judged that organic matter & algae type nutrient type, and complex type classification, and appropriate water quality management measures for each type are required. All regression models used for prediction of Chl-a were analyzed as p<0.05, and the independent variables of each prediction formula were meaningful in explaining Chl-a. Chl-a is changed by the complex effects of various water quality factors, and it is also judged that the influence of water quality external products such as precipitation, sunshine time, and flow rate should also be considered. In the future, if the regression is performed by integrating the water quality factors and external factors of a longer period, it is possible to create a model with a slightly higher explanatory rate by increasing the R2 value.
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