摘要
根据2013年5月(春季)和8月(夏季)莱州湾海水环境要素的调查资料,通过主成分分析提取了3个主成分,累计表达原数据矩阵78.57%的信息,然后分别利用监测要素和主成分综合得分的层次聚类分析筛选出监控指标,并确定了水质污染状况及主要污染区域.结果表明,2013年莱州湾水质春季和夏季较差,导致水质状况恶化的主要要素是溶解无机氮,水质污染程度在时间和空间上存在较大差异,总体上春季海水污染程度高于夏季,莱州湾西侧和南侧处于严重污染或中度污染水平,因此常规监测中应重点关注化学需氧量、无机氮、叶绿素a和石油类这4个指标.
On basis of field data of seawater environmental parameters in the Laizhou Bay in May (on behalf of spring) and August (on behalf of summer) of 2013, we extracted 3 principal components by principal components analysis (PCA) according to their eigenvalues (the amount of weight or importance assigned to), and could interpret and kccount for 78.57% of the total original information. After dimensionality reduction, we computed each score of above 3 principal components and their integrated scores in spring and summer in order to assess seawater quality. Then monitoring indicators were selected as well as contamination status and areas were identified, applying hierarchical clustering of cluster analysis (CA) to 8 environmental parameters and to 3 principal components scores of 20 sampling sites in spring and summer, respectively. The results showed that seawater quality of the Laizhou Bay is comparatively poor in spring and summer, owing to the dissolved inorganic nitrogen (DIN) concentration in spring as well as DIN and petroleum concentrations in summer. Thus, DIN is the major contamination parameter. There are great spatial and temporal variations in water quality, which is worse in spring than in summer, and the seawater is moderately or heavily contaminated in the west and south of the Laizhou Bay. Therefore, more attentions should be paid to 4 monitoring indicators, involving chemical oxygen demand (COD), DIN, chlorophyll-a (Chl-a) and petroleum.
出处
《数学的实践与认识》
北大核心
2016年第1期110-117,共8页
Mathematics in Practice and Theory
基金
国家海洋公益性行业专项经费项目(201105006
201205001)
山东省科技发展计划(2014GSF117030)
山东省软科学研究计划项目(2014RKB 12003)
关键词
主成分分析
聚类分析
海水水质
莱州湾
principal components analysis
cluster analysis
seawater quality
Lalzhou Bay