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Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses

文献类型： 外文期刊

作者： Song, Ming W. 1 ; Huang, Ping 1 ; Li, Feng 2 ; Zhang, Hui 1 ; Xie, Kai Z. 3 ; Wang, Xi H. 4 ; He, Guo X. 5 ;

作者机构： 1.Sun Yat Sen Univ, Dept Environm Sci, Sch Environm Sci & Engn, Guangzhou 510275, Guangdong, Peoples R China

2.S China Univ Technol, Sch Environm Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China

3.Guangdong Acad Agr Sci, Soil & Fertilizer Inst, Guangzhou 510640, Peoples R China

4.Jinan Engn Vocat Tech Coll, Dept Architecture Engn, Jinan 250200, Peoples R China

5.Qingyuan Municipal Environm Protect Bur, Qingyuan 511515, Peoples R China

关键词： Beijiang River;Water quality;Principal component analysis (PCA);Multivariate linear regression of absolute principal component scores (MLR-APCS)

期刊名称：ENVIRONMENTAL MONITORING AND ASSESSMENT （ 影响因子：2.513； 五年影响因子：2.871 ）

ISSN： 0167-6369

年卷期： 2011 年 172 卷 1-4 期

页码：

收录情况： SCI

摘要： Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.

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[1]Multivariate assessment of polycyclic aromatic hydrocarbons in surface sediments of the Beijiang, a tributary of the Pearl River in Southern China. Gao, Min,Wang, Panfei,Zhang, Hui,Xie, Kaizhi,Zhang, Hui.

[2]STUDY ON A MICROBIAL PREPARATION FOR IMPROVING WATER QUALITY IN AQUACULTURE. Yang, Jing,Zhu, Jiangwei,Qiu, Jiangping,Fang, Wei,Liu, Hailin.

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