文献类型：期刊文献

英文题名：Tracing the potential pollution sources of the coastal water in Hong Kong with statistical models combining APCS-MLR

作者：Liu, Lili[1,2];Tang, Zhou[1];Kong, Ming[3];Chen, Xin[1];Zhou, Chunchun[1];Huang, Kai[1];Wang, Zhiping[4]

机构：[1]East China Univ Sci & Technol, State Environm Protect Key Lab Environm Risk Asse, Sch Resource & Environm Engn, Shanghai 200237, Peoples R China;[2]Shanghai Acad Environm Sci, Shanghai 200233, Peoples R China;[3]Minist Environm Protect, Nanjing Inst Environm Sci, 8 Jiang Wang Miao St, Nanjing 210042, Jiangsu, Peoples R China;[4]Shanghai Jiao Tong Univ, Sch Environm Sci & Technol, Shanghai 200240, Peoples R China

年份：2019

卷号：245

起止页码：143

外文期刊名：JOURNAL OF ENVIRONMENTAL MANAGEMENT

收录：;EI(收录号：20233314556517);WOS:【SCI-EXPANDED(收录号:WOS:000473380300016)】；

基金：This work was sponsored by the National Natural Science Foundation of China (41771513, 41001316, 51108262), and Major Science and Technology Program for Water Pollution Control and Treatment in China (2017ZX07202006, 2017ZX07206004), and National Key Research and Development Program (2018YFC1901005). This research work is supported by the open database of Environmental Protection Department (EPD) of Hong Kong.

语种：英文

外文关键词：Coastal water; APCS-MLR model; Network analysis; Water quality; Hong Kong

摘要：In this study, variety of statistical methods were performed to reveal the spatiotemporal distribution characteristics of pollutants and parsing pollution sources of the coastal water in Hong Kong. The temporal-spatial distribution characteristics of the water pollution were various among the three distinct areas, which might be ascribed to the different dominant pollution sources. Cluster and network analysis showed preliminary pollution sources in these areas, and also indicated the temporal characteristics of Deep Bay water pollution, which could divided into two parts before and after 2010. According to the principal component analysis/factor analysis results, three factors in Deep Bay, Tolo Harbour and Victoria Harbour could explained 68.72%, 54.87% and 72.28% of the total variances, respectively. The contribution rate of different pollution source on water quality variables in each area had calculated by absolute principal component score-multiple linear regression model. The contribution rate was roughly ranked as: point source pollution > non-point source pollution > overland runoff > river input. It is the first time to combine multivariate statistical methods, network analysis and regression model to profoundly analyze spatiotemporal variation of seawater quality and parsing the pollution sources. This novel analysis method can provide reference for the water quality evaluation and management of other water bodies.