A novel approach combining self-organizing map and parallel factor analysis for monitoring water quality of watersheds under non-point source pollution

High content of organic matter in the downstream of watersheds underscored the severity of non-point source (NPS) pollution. The major objectives of this study were to characterize and quantify dissolved organic matter (DOM) in watersheds affected by NPS pollution, and to apply self-organizing map (SOM) and parallel factor analysis (PARAFAC) to assess fluorescence properties as proxy indicators for NPS pollution and labor-intensive routine water quality indicators. Water from upstreams and downstreams was sampled to measure dissolved organic carbon (DOC) concentrations and excitation-emission matrix (EEM). Five fluorescence components were modeled with PARAFAC. The regression analysis between PARAFAC intensities (Fmax) and raw EEM measurements indicated that several raw fluorescence measurements at target excitation-emission wavelength region could provide similar DOM information to massive EEM measurements combined with PARAFAC. Regression analysis between DOC concentration and raw EEM measurements suggested that some regions in raw EEM could be used as surrogates for labor-intensive routine indicators. SOM can be used to visualize the occurrence of pollution. Relationship between DOC concentration and PARAFAC components analyzed with SOM suggested that PARAFAC component 2 might be the major part of bulk DOC and could be recognized as a proxy indicator to predict the DOC concentration.