文献类型：期刊文献

英文题名：Using EEM-PARAFAC to identify and trace the pollution sources of surface water with receptor models in Taihu Lake Basin, China

作者：Wang, Xu[1];Zhang, Meng[1];Liu, Lili[1,3];Wang, Zhiping[2];Lin, Kuangfei[1]

机构：[1]East China Univ Sci & Technol, Sch Resources & Environm Engn, State Environm Protect Key Lab Environm Risk Asses, Shanghai 200237, Peoples R China;[2]Shanghai Jiao Tong Univ, Sch Environm Sci & Technol, Shanghai 200240, Peoples R China;[3]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

年份：2022

卷号：321

外文期刊名：JOURNAL OF ENVIRONMENTAL MANAGEMENT

收录：;EI(收录号：20233414611064);WOS:【SCI-EXPANDED(收录号:WOS:000848606100004)】；

基金：Acknowledgment This work was sponsored by the Major Science and Technology Program for Water Pollution Control and Treatment in China (2017ZX07202006) , and National Natural Science Foundation of China (41771513) .

语种：英文

外文关键词：Surface water; Source apportionment; Parallel factor analysis; Absolute principal component score -multiple; linear regression; Positive matrix factorization

摘要：The identification and apportionment of the multiple pollution sources are essential and crucial for improving the effectiveness of surface water resources management. In this study, the surface water samples were collected from Taihu Lake Basin, and the optimal water quality parameters for the receptor models were selected firstly with multivariate statistical analyses. In order to identify the potential pollution sources in surface water, dis-solved organic matter (DOM) was analyzed with the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Through the Pearson correlation analysis of water quality parameters and DOM components, the pollution sources were further verified, i.e., agricultural activities, domestic sewage, phyto-plankton growth/terrestrial input and industrial sources. In addition, principal component analysis (PCA) combined with the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were employed to quantify pollution sources. Compared with PCA-APCS-MLR model, PMF model resulted in higher performance on evaluation statistics and lower proportion of unexplained variability, thus showed more realistic and robust representation. The results of PMF showed that agricultural activities (42.08%) and domestic sewage (21.16%) were identified as the dominant pollution sources of surface water in the study area. This study highlights the effectiveness of EEM-PARAFAC in identifying the pollution sources, and the applicability of PMF in apportioning the contributions of each potential pollution source in surface water.