Study on the source tracing method of organic pollutants in large shallow eutrophic lakes based on 3D-EEM and Transformer models: A case study of Changdang Lake in China

Study on the source tracing method of organic pollutants in large shallow eutrophic lakes based on 3D-EEM and Transformer models: A case study of Changdang Lake in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study on the source tracing method of organic pollutants in large shallow eutrophic lakes based on 3D-EEM and Transformer models: A case study of Changdang Lake in China Juan Huan, Qucheng Hu, Hao Zhang, Zhenrui Li, Xiangen Xu, Chen Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7365529/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 9 You are reading this latest preprint version Abstract Organic pollution in the lake water bodies poses a serious threat to the stability of aquatic ecosystems and human health. Dissolved organic matter (DOM) is a key component of organic pollution. The analysis of its sources is crucial for pollution control. In order to accurately trace the source of organic pollution in the Changdang Lake basin, this study proposes a traceability method combining three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy technology with a deep learning model. First, water quality samples were collected from the rivers connected to Changdang Lake and surrounding industrial wastewater, agriculture, and domestic pollution sources. Raw data were obtained by 3D-EEM spectroscopy. Parallel factor analysis (PARAFAC) was used to analyze the fluorescence data. Obtain fluorescence spectral images that characterize different pollution sources. Industrial wastewater, agricultural, and domestic pollution sources were used as training data (labeled 0, 1, and 2, respectively) to build and pre-train a deep learning model. Four deep learning models (Transformer, GoogLeNet, VGG, and AlexNet) were selected for comparison. Transformer model performs better in terms of both recognition efficiency and accuracy. The fluorescence spectrum images of the rivers connected to Changdang Lake and Lake body were input into the trained Transformer model to identify the sources of pollution. Tucker Congruence (TC) coefficients are introduced to quantify and verify the recognition results. The results show that for 40 fluorescent components in the Changdang Lake basin, the identification results of 38 components are consistent with the TC coefficients, with an identification accuracy rate of 95%. Compared with the traditional manual tracing method that relies on TC coefficients, this method significantly reduces the workload. The time required has been reduced from hours to minutes. This study provides efficient and reliable technical support for tracing the source of organic pollution in lake basins. Three-dimensional excitation-emission matrix (3D-EEM) Pollutant tracing Deep learning Parallel factor analysis (PARAFAC) Dissolved organic matter (DOM) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 24 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers invited by journal 05 Sep, 2025 Editor assigned by journal 19 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7365529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510753803,"identity":"18f21614-744b-4a3c-9fae-72205c90bb7e","order_by":0,"name":"Juan 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Dissolved organic matter (DOM) is a key component of organic pollution. The analysis of its sources is crucial for pollution control. In order to accurately trace the source of organic pollution in the Changdang Lake basin, this study proposes a traceability method combining three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy technology with a deep learning model. First, water quality samples were collected from the rivers connected to Changdang Lake and surrounding industrial wastewater, agriculture, and domestic pollution sources. Raw data were obtained by 3D-EEM spectroscopy. Parallel factor analysis (PARAFAC) was used to analyze the fluorescence data. Obtain fluorescence spectral images that characterize different pollution sources. Industrial wastewater, agricultural, and domestic pollution sources were used as training data (labeled 0, 1, and 2, respectively) to build and pre-train a deep learning model. 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