Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses
Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses
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中国南方珠江支流北江水质：多元统计分析的意义
DOI：
10.1007/s10661-010-1358-y
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发表时间：
2011
影响因子：
3
通讯作者：
He, Guo X.
中科院分区：
文献类型：
--
作者：
Song, Ming W.;Huang, Ping;Li, Feng;Zhang, Hui;Xie, Kai Z.;Wang, Xi H.;He, Guo X.
对中国广东珠江支流北江的水质信息进行分析，以概述主要农业/农村地区和工业/城市地区的水化学功能。 2005年至2006年，每月对13个地点的18个水质参数进行了调查。采用双变量相关分析评价水质参数的区域相关性，同时采用主成分分析（PCA）技术提取对河流水质区域变化影响最大的变量。 PCA 提取了 6 个主成分，分别解释了农业/农村和工业/城市地区总方差的 78% 以上和 84% 以上。理化因素、有机污染、污水污染、地理因素、农业面源污染、农药累积使用量被确定为农业/农村潜在污染源，工业废水污染、矿产污染、地理因素、城市污水污染、化工污染、水交通污染为工业/城镇潜在污染源。使用绝对主成分得分的多元线性回归 (MLR-APCS) 技术来估计所有已确定的污染源对每个水质参数的贡献。回归方程的高决定系数表明MLR-APCS模型适用于北江流域大部分水质参数的来源估算。
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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影响因子：
2.8
作者：
Medina-Gómez, I;Herrera-Silveira, JA
通讯作者：
Herrera-Silveira, JA
影响因子：
3.9
作者：
Shurong Zhang;Xixi Lu;D. Higgitt;C. Chen;Jingtai Han;Huiguo Sun
通讯作者：
Shurong Zhang;Xixi Lu;D. Higgitt;C. Chen;Jingtai Han;Huiguo Sun
影响因子：
6.4
作者：
Bouza-Deano, R.;Ternero-Rodriguez, M.;Fernandez-Espinosa, A. J.
通讯作者：
Fernandez-Espinosa, A. J.
影响因子：
12.8
作者：
Kowalkowski, T;Zbytniewski, R;Buszewski, B
通讯作者：
Buszewski, B
影响因子：
12.8
作者：
F. Zhou;Gordon H. Huang;Huaicheng Guo;Wei Zhang-;Zejia Hao
通讯作者：
F. Zhou;Gordon H. Huang;Huaicheng Guo;Wei Zhang-;Zejia Hao