文献类型：期刊文献

英文题名：Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

作者：Liu, Lili[1,2];Dong, Yongcheng[1];Kong, Ming[3];Zhou, Jian[4];Zhao, Hanbin[1];Tang, Zhou[1];Zhang, Meng[1];Wang, Zhiping[5]

机构：[1]East China Univ Sci & Technol, Sch Resource & Environm Engn, State Environm Protect Key Lab Environm Risk Asse, Shanghai 200237, Peoples R China;[2]Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China;[3]Minist Environm Protect, Nanjing Inst Environm Sci, Nanjing 210042, Peoples R China;[4]Chinese Acad Sci, Nanjing Inst Geog & Limnol, Nanjing 210008, Peoples R China;[5]Shanghai Jiao Tong Univ, Sch Environm Sci & Technol, Shanghai 200240, Peoples R China

年份：2020

卷号：242

外文期刊名：CHEMOSPHERE

收录：;EI(收录号：20194507637677);WOS:【SCI-EXPANDED(收录号:WOS:000509786600031)】；

基金：This work was sponsored by the Major Science and Technology Program for Water Pollution Control and Treatment in China (2017ZX07202006, 2017ZX07206004), National Natural Science Foundation of China (41771513, 41001316, 51108262) and National Key Research and Development Program (2018YFC1901000). This research work is supported by the Taihu Laboratory for Lake Ecosystem Research, Chinese Academy of Science.

语种：英文

外文关键词：Water quality; Source apportionment; Positive matrix factorization model; Absolute principal component score-multiple linear regression model; Lake Taihu

摘要：Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (CODMn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu. (C) 2019 Elsevier Ltd. All rights reserved.