摘要
基于2023年黄河流域青海段5个监测断面的水质数据,对pH、浊度、高锰酸盐指数、总磷、总氮5个水质监测指标开展主成分分析得出各断面主成分综合得分,再经spearman秩相关分析法得出各断面水质变化趋势,并对断面污染载荷及水质指标对主成分的方差贡献进行分析,得出贡献率较高的因子,分析其因子浓度在枯水期、平水期、丰水期的变化。结果表明:黄河流域青海段主要水质指标存在较小变异。5个断面水质污染由重到轻排名依次为扎马隆>润泽桥>塔尔桥>金滩>甘冲口。金滩水质呈平稳趋势,其余断面水质污染浓度随季节有所上升。断面污染贡献率最高的指标为总磷、总氮、高锰酸盐指数,丰水期总磷浓度、高锰酸盐指数浓度最高,总磷浓度总体上秋季高于春季。总氮浓度在丰水期、平水期和枯水期分布各不相同。
Based on the water quality data of five monitoring sections in the Qinghai section of the Yellow River Basin in 2023,the principal component analysis of the five water quality monitoring indicators of pH,turbidity,permanganate index,total phosphorus and total nitrogen was carried out to calculate the comprehensive score of the principal component of sections.Water quality change trend of sections was obtained by Spearman rank correlation analysis method.Pollution load and variance contribution of index to the principal component of each section was analyzed,then factors with higher contribution rate were obtained.In dry season concentration variation of the factors was analyzed,as well as in normal season and wet season.The results showed that water quality factors have a small variation in Qinghai Yellow River Basin.The five sections of water pollution ranked from heavy to light,Zamalong>Runze Bridge>Thar Bridge>Jintan>Ganchongkou.Except the water quality of Jintan showed a stable trend,and the concentration of water pollution in other sections increased with season.The indicators with the highest contribution rate to pollution were total phosphorus,total nitrogen and permanganate index.The total phosphorus concentration and permanganate index concentration was generally higher in autumn than in spring.The distribution of total nitrogen concentration in normal season,wet season and dry season were different.
作者
罗晶
Luo Jing(Qinghai Research Institute of Transportation,Xining 810016,China)
出处
《青海交通科技》
2024年第3期65-72,共8页
Qinghai Transportation Science and Technology
基金
青海省科技计划项目(2023-ZJ-793)。
关键词
黄河流域
主成分分析
枯水期
丰水期
spearman秩相关系数法
Yellow River
principal component analysis
dry season
wet season
spearman rank correlation coefficient method