文献类型：期刊文献

英文题名：Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

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

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

年份：2024

卷号：346

外文期刊名：ENVIRONMENTAL POLLUTION

收录：;EI(收录号：20240915636320);WOS:【SCI-EXPANDED(收录号:WOS:001198909600001)】；

基金：This work was supported by the Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07202006) .

语种：英文

外文关键词：Surface water; Pollution sources apportionment; Fluorescence excitation-emission matrix; FEAST; SourceTracker

摘要：Further improving the quality of surface water is becoming more difficult after the control of main point -sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectationmaximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.