文献类型：期刊文献

英文题名：Identification of Surface Water Pollution Source by Eem-Parafac Combined with Microbial Traceability Models in Wujin District, China

作者：Wang, Zhiping[1]; Peng, Yuanjun[1]; Liu, Lili[2]; Wang, Xu[2]; Teng, Guoliang[1]; Fu, Anqing[1]

机构：[1] School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; [2] State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

外文期刊名：SSRN

收录：EI(收录号：20230239929)

语种：英文

外文关键词：Biomarkers - Discriminant analysis - Factor analysis - Fluorescence - Forestry - Maximum principle - Multivariant analysis - Pollution control - River pollution - Water quality

摘要：A technical framework for pollution tracing based on three-dimensional fluorescence and microbial traceability model is proposed, which can help in identifying and calculating the contributions of multiple pollution sources. The dissolved organic matter (DOM) is analyzed by fluorescence excitation-emission matrix (EEM) coupled with parallel factor analysis (PARAFAC), and the Tucker congruence coefficient of fluorescent components between surface water and pollution sources is compared, thus the potential pollution sources are listed. Water quality parameters and fluorescent components are further analyzed using Spearman correlation to verify the main pollution sources: aquaculture, mechanical, chemical, and textile wastewater. At the same time, the influence of environmental factors on microorganisms is determined using a random forest model and Spearman correlation analysis, and the source biomarkers are identified using linear discriminant analysis (LEfSe). Network analysis is then used to reveal the relationship between fluorescent components and pollution source biomarkers, thus the impact of different pollution sources on surface water is proposed with the biomarkers-based primary coordinate analysis (PCoA). Finally, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). The microbial traceability model shows more accurate pollution source identification ability in complex pollution environments than PCA-APCS-MLR, and better reflects the pollution differences at various points. FEAST has a more sensitive potential source identification ability and shorter calculation time than SourceTracker. The similar results of fluorescent components and the microbial traceability model indicate the credibility of source identification, which can be used to guide precise regional pollution control and improve the effectiveness of surface water resources management. ? 2023, The Authors. All rights reserved.