Tracking the sources of dissolved organic matter under bio- and photo-transformation conditions using fluorescence spectrum-based machine learning techniques

Abstract

Dissolved organic matter (DOM) from various sources can lead to environmental issues such as eutrophication in agricultural watersheds. Effective source-tracking tools are needed to implement proper management practices. Fluorescence excitation–emission matrix (EEM) spectroscopy has been widely used to probe DOM composition. We explored optimal fluorescence EEM-based machine learning (ML) tools to quantify the proportions of different DOM sources in mixture samples under natural transformation conditions. Bulk DOM samples were prepared from soil and compost at various ratios and treated to simulate biogeochemical transformations. ML models based on all the EEM data outperformed those based on defined fluorescence indices. The trained support vector regression model (SVR) outperformed the conventional source tracking method of end-member mixing analysis (EMMA) with an R2 of 0.88 versus 0.83. Among the five suitable ML algorithms tested, SVR explained 90% and 85% of the variability in the proportions of soil and compost sources in the DOM mixture, with the mean squared errors of 0.004 and 0.007, respectively. The predicted capacity revealed a close relationship or causality between the specific mixing ratios of the bulk samples and the EEM spectra. The ML technique with EEM data was not constrained by the identification of all major sources, which is a required condition for the EMMA method. This study highlights the significant potential of EEM-based ML for tracing the source of DOM and establishes a basis for the future development of EEM data-driven models capable of tracking multiple DOM sources, even in the absence of all possible end-members.