Work

Spectral indicator development using excitation–emission matrix fluorescence and deep learning for quantifying organic pollution in mixed land-use watersheds

Year: 2025

Type: article

Abstract: Achieving reliable quantification of individual organic pollution sources remains a persistent challenge in mixed land-use watersheds, where multiple sources often co-occur and interact in complex, nonlinear ways. Conventional statistical approaches, which rely on a limited set of fluorescence indices or chemical tracers, are insufficient to resolve the spectral overlaps and intricate source mixing that characterize these environments. In this study, we present a novel, data-driven framework that leverages full high-dimensional information contained in Excitation–Emission Matrix (EEM) fluorescence images to directly estimate the proportional contributions of multiple organic pollution sources. River water samples and representative and representative source materials (e.g., soil, vegetation, livestock excreta) were collected from a mixed land-use watershed to construct a comprehensive EEM image dataset. By integrating these full-spectrum EEM images with a deep learning-based analytical system, we achieved robust classification and quantitative estimation of pollution source contributions in riverine samples. The model attained a weighted F1-score of 0.91 for source classification and a mean absolute error of 5.62% for source contribution estimation. Predicted source contributions closely matched spatial patterns observed in the watershed, demonstrating the practical reliability of the approach for identifying major contributors. These deep learning-derived source contribution estimates serve as source-specific pollution indicators that capture nonlinear mixing patterns and address key limitations of conventional index- or tracer-based approaches. These indicators reflect the relative influence of diverse land-use origins on organic pollution, offering a scalable and interpretable decision-support tool for water quality assessment, management, and mitigation in heterogeneous watershed systems.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.114278

Authors: Jimin Lee, Soyoung Lee, Eu Gene Chung, Jin Hur, Eun Hye Na, Kyunghyun Kim

Institutions: National Institute of Environmental Research, Sejong University

Language: English

Topic: Water Quality Monitoring and Analysis

Subfield: Waste Management and Disposal

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,650