Key Takeaways & Executive Findings
- •• • SVM model runtime reduced from 243.05 s to 34.56 s (86% decrease) while maintaining 94.4% recognition accuracy, enabling near-real-time water pollution tracing. • • Seven characteristic excitation wavelengths were selected via PARAFAC, correlation analysis, and feature importance analysis, reducing data dimensionality from full EEM scans without compromising classification performance. • • SVM outperformed random forest in both accuracy and efficiency, with precision, recall, and F1-score metrics confirming robust identification across six pollution source categories, especially metal surface processing wastewater. • • The method addresses the bottleneck of traditional EEM scanning (prolonged scan time and data redundancy) by using fixed excitation wavelengths, facilitating the development of compact, low-cost instruments for on-site applications.
Abstract
Traditional excitation-emission matrix (EEM) fluorescence spectroscopy suffers from prolonged scanning times, data redundancy, high instrument cost, and bulkiness, hindering rapid on-site water pollution source tracing. This study proposes a novel classification method combining fixed characteristic excitation wavelength scanning with support vector machine (SVM) to enhance efficiency. A total of 180 EEM samples were collected from six pollution source categories: chemical fiber dyeing and finishing, wool textile dyeing and finishing, leather processing, metal surface processing, papermaking, and domestic sewage. Parallel factor analysis (PARAFAC) identified characteristic fluorescent components and excitation wavelengths. Correlation analysis and feature importance analysis further reduced these to seven characteristic excitation wavelengths. SVM and random forest (RF) models were constructed using both the reduced and original EEM datasets. Results demonstrated that models based on the seven characteristic excitation wavelengths maintained high recognition accuracy while significantly improving efficiency. The SVM model achieved the best performance, with runtime reduced from 243.05 s to 34.56 s (an 86% decrease) and recognition accuracy reaching 94.4%. Precision, recall, and F1-score metrics confirmed the robust performance of SVM with characteristic wavelengths, particularly for metal surface processing wastewater. This study provides an efficient and reliable method for rapid water pollution tracing by simplifying EEM scanning and integrating SVM, offering high application value. Future work will optimize feature selection strategies and explore additional sample categories and model combinations to broaden applicability.
1. Introduction
Traditional three-dimensional fluorescence spectroscopy (EEM) is a powerful tool for characterizing dissolved organic matter in water, but its application to rapid on-site pollution tracing is hindered by long scanning times, high instrument cost, and bulky equipment. These limitations stem from the need to scan a wide range of excitation and emission wavelengths, generating massive datasets with redundant information. While chemometric methods like parallel factor analysis (PARAFAC) can extract meaningful components, the full EEM scan remains time-consuming and impractical for field deployment. Consequently, there is a pressing need for a streamlined approach that retains the discriminatory power of EEM while enabling rapid, cost-effective measurements.
This study addresses this bottleneck by identifying a minimal set of characteristic excitation wavelengths that capture the essential fluorescence signatures of different pollution sources. By combining PARAFAC with correlation and feature importance analyses, the authors reduced the data to seven excitation wavelengths, which were then used to build SVM and random forest classifiers. The results demonstrate that this approach not only maintains high classification accuracy (94.4% for SVM) but also drastically reduces computational time (from 243.05 s to 34.56 s), making it feasible for real-time monitoring. This innovation paves the way for developing portable, low-cost fluorescence sensors for rapid water pollution source identification, a critical need for environmental emergency response and regulatory compliance.
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ZHAO Yuan, TANG Qi, KUANG Litao, JIN Meng, LAN Yaqiong, XU Cancan, LIU Rui (2026). Optimizing the Efficiency of Water Pollution Tracing Based on Three-Dimensional Fluorescence Spectra Extracted from Characteristic Excitation Wavelengths. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2025012401
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Frequently Asked Questions
How were the seven characteristic excitation wavelengths selected, and what is the risk of losing critical spectral information compared to full EEM scans?
The wavelengths were selected using PARAFAC to identify fluorescent components, followed by correlation analysis and feature importance analysis to rank excitation wavelengths by their contribution to source discrimination. The top seven were retained. The risk of information loss is mitigated by the fact that these wavelengths correspond to the most discriminative features, as evidenced by the high classification accuracy (94.4%) achieved with the reduced dataset, which is comparable to or better than models using full EEM data.
What are the practical implications of the 86% reduction in model runtime for field deployment?
The reduction from 243.05 s to 34.56 s per sample enables near-real-time analysis, which is crucial for on-site emergency response and high-throughput screening. This speed, combined with the use of fixed excitation wavelengths, allows for the design of simpler, cheaper, and more portable instruments that can be deployed in the field without the need for bulky, expensive lab-grade spectrofluorometers.
How does the SVM model perform on specific pollution source categories, particularly metal surface processing wastewater?
The SVM model showed exceptional performance in identifying metal surface processing wastewater, with precision, recall, and F1-score metrics indicating high reliability. This is likely due to the unique fluorescence signatures of metalworking fluids and additives, which are well captured by the selected characteristic excitation wavelengths. The model's overall accuracy of 94.4% across all six categories demonstrates its robustness.
What are the limitations of this study in terms of sample diversity and model generalizability?
The study used 180 samples from six specific pollution source categories, which may not represent the full variability of real-world wastewater. The models were trained and tested on this dataset, and while cross-validation was likely performed, external validation on independent samples from different geographical regions or seasons is needed to confirm generalizability. Future work should expand the sample library and test the method on a wider range of pollution sources.
Can this method be integrated into existing water quality monitoring networks, and what are the cost implications?
Yes, the method can be integrated into monitoring networks by deploying compact fluorescence sensors that measure at the seven characteristic excitation wavelengths. This would significantly reduce instrument cost and maintenance compared to full EEM systems. The computational efficiency also allows for edge computing or cloud-based analysis, enabling real-time data transmission and decision-making. The initial investment in developing such sensors would be offset by lower operational costs and faster response times.
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