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Over the past three decades, fluorescence spectroscopy has been conventionally interpreted through peak picking, fluorescence regional integration, and parallel factor analysis to analyze aquatic dissolved organic matter. However, there is a growing need for advances in analytical toolkits to unlock the full potential of fluorescence spectra to tackle pressing challenges in smart water surveillance such as real-time surface water monitoring and wastewater source tracing. To this end, we established two types of easily implementable regression models. Through weighted linear regression (WLR), we constructed a novel correlation map for fluorescence excitation–emission matrices (EEMs) and dissolved organic carbon (DOC) based on 191 surface water samples from diverse aquatic environments. This map reveals that humic-like fluorescence intensity (FI) at excitation/emission wavelengths of 300–380/440–490 nm serves as a reliable indicator of aquatic DOC. In addition, a multivariable linear regression (MLR) model was developed to identify pharmaceutical wastewater blended into diverse surface water matrices, achieving fitting errors of 10–20%, despite fluorescence quenching at short excitation wavelengths. This work demonstrates that the regression-based toolkits developed herein can advance the application of fluorescence spectra for protecting aquatic environments.
Tang et al. (Thu,) studied this question.