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    SHAO Yihong, LAI Xin, LIU Fengyu, et al. Source Apportionment of Shangtang River (Haining Section) Based on Three-Dimensional Fluorescence Spectroscopy and Positive Matrix Factorization ModelJ. Environmental Monitoring in China, 2026, 42(2): 217-225. DOI: 10.19316/j.issn.1002-6002.2026.02.22
    Citation: SHAO Yihong, LAI Xin, LIU Fengyu, et al. Source Apportionment of Shangtang River (Haining Section) Based on Three-Dimensional Fluorescence Spectroscopy and Positive Matrix Factorization ModelJ. Environmental Monitoring in China, 2026, 42(2): 217-225. DOI: 10.19316/j.issn.1002-6002.2026.02.22

    Source Apportionment of Shangtang River (Haining Section) Based on Three-Dimensional Fluorescence Spectroscopy and Positive Matrix Factorization Model

    • The Jiangsu and Zhejiang regions are characterized by dense plain river networks,making it difficult to clarify the pollution sources of composite polluted rivers using a single tracing method.Based on data from water quality,hydro-meteorology,and sediments,the study employed three-dimensional fluorescence spectroscopy (EEM) and parallel factor analysis (PARAFAC) to identify the main sources and components of fluorescent organic matter in the water.This approach optimized the outputs of the Positive Matrix Factorization (PMF) model,enabling qualitative and quantitative analysis of pollution sources.The results indicated that among the four major fluorescent components of water bodies in the study area,C1 and C4 humic substances were related to the discharge of nitrogen and phosphorus nutrients and organic pollution indicators from urban point sources,industrial point sources and agricultural non-point sources.C2 and C3 protein substances were associated with nitrogen and phosphorus emissions from production and domestic activities.The contribution ratios of various pollution sources were as follows: urban point sources (25.2%),mixed source of agricultural non-point-internal mixed sources (23.2%),mixed sources of urban living,agricultural production and industrial emissions (19.8%),internal sources (16.4%),and agricultural non-point sources (15.4%).
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