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利用统计分析方法识别南海大亚湾近岸海域水质。

Identification of coastal water quality by statistical analysis methods in Daya Bay, South China Sea.

机构信息

Key Laboratory of Tropical Marine Environmental Dynamics, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China.

出版信息

Mar Pollut Bull. 2010 Jun;60(6):852-60. doi: 10.1016/j.marpolbul.2010.01.007. Epub 2010 Feb 13.

Abstract

In this paper, cluster analysis (CA), principal component analysis (PCA) and the fuzzy logic approach were employed to evaluate the trophic status of water quality for 12 monitoring stations in Daya Bay in 2003. CA grouped the four seasons into four groups (winter, spring, summer and autumn) and the sampling sites into two groups (cluster DA: S1, S2, S4-S7, S9 and S12 and cluster DB: S3, S8, S10 and S11). PCA identified the temporal and spatial characteristics of trophic status in Daya Bay. Cluster DB, with higher concentrations of TP and DIN, is located in the western and northern parts of Daya Bay. Cluster DA, with the low Secchi, is located in the southern and eastern parts of Daya Bay. The fuzzy logic approach revealed more information about the temporal and spatial patterns of the trophic status of water quality. Chlorophyll a, TP and Secchi may be major factors for deteriorating water quality.

摘要

本文采用聚类分析(CA)、主成分分析(PCA)和模糊逻辑方法,对 2003 年大亚湾 12 个监测站的水质营养状况进行了评价。CA 将四季分为四组(冬季、春季、夏季和秋季),将采样点分为两组(聚类 DA:S1、S2、S4-S7、S9 和 S12 和聚类 DB:S3、S8、S10 和 S11)。PCA 确定了大亚湾营养状况的时空特征。TP 和 DIN 浓度较高的聚类 DB 位于大亚湾的西部和北部。Secchi 较低的聚类 DA 位于大亚湾的南部和东部。模糊逻辑方法揭示了水质营养状况时空模式的更多信息。叶绿素 a、TP 和 Secchi 可能是水质恶化的主要因素。

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