为建立适用于近岸海域水质的评价模型,依照《国家海水水质标准》构建了1个包含13种水质指标信息、共计400个假设采样站位的数据样本。通过计算其KMO统计量、球形检验及相关矩阵发现,各水质指标间存在较大相关性,故可利用主成分分析方法进行分析。针对13个水质指标,仅前2个特征根大于1的主成分是有效的,且它们可以代表原假设数据81.25%的信息。利用前2个主成分建立了可完全区分四类水质的自动分类图版,即水质评价模型。根据上述水质评价模型绘制了2010年雷州半岛近岸海域的水质类型专题图。分析表明,雷州半岛的湛江港湾、鉴江口海域及铁山港区为第四类水质,东海岛西南、鉴江口外海、徐闻东北角海域、流沙湾及江洪港海域为三类水质,其它区域为一、二类水质。本研究较好地反应了雷州半岛近岸海域水质分布状况,可为该海域海洋环境综合治理及利用提供一定参考。
Abstract
In this study, a data sample has been established including 13 sorts of water quality index and 400 stations of hypothesis sampling according to the <National Water Quality Standard (NWQS)> for the evaluation model of coastal water quality. By calculating the KMO, sphericity test as well as the correlation matrix of all indices, we found that there was a good correlation among indexes, which indicated that the principal component analysis (PCA) could be applied to extract information. For 13 indexes of water quality, only the first two principal components with eigenvalues ??greater than 1 are effective, which could represent 81.25% information of the samples, as a result, a quality automatic classification layout (evaluation model) which could accurately classify four kinds of waters has been set up. A thematic map of water quality classification in the coastal waters of Leizhou Peninsula in 2010 was drawn according to the water quality automatic evaluation method. In this map, case IV waters were in the Zhanjiang harbor, Jianjiang Estuary and Tieshan harbor sea area, and case III waters were mainly in the southwest of Donghai Island, offshore area of Jianjiang estuary, northeast of Xuwen, Liusha Bay and Jianghong harbor sea areas, and on the other sea area of Leizhou Peninsula, they were case I and II waters in 2010. This study provides a better distribution in water quality environment of the coastal waters of Leizhou Peninsula, which offers some references for the comprehensive treatment and utilization in this area.
关键词
雷州半岛海域 /
近海水质 /
自动评价模型 /
主成分分析
Key words
Leizhou Peninsula /
coastal water quality /
automatic evaluation model /
principal component analysis
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基金
国家海洋重大公益专项项目资助(201305019);浙江省博士后基金项目资助(BSH1301015);国家海洋局第二海洋研究所博士后基金项目资助(JG1319);卫星海洋环境动力学国家重点实验室开放基金项目资助(SOED1202);广东省社会科学规划项目资助(GD12YGL04);广东省高校优秀青年创新计划项目资助(2012WYM_0077);广东海洋大学博士启动基金项目资助(E11043, E11332, E11097)
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