Source Apportionment of Pollution in the Tianjin Haihe River Sluice Based on the TCN-APCS-MLR Model

Source Apportionment of Pollution in the Tianjin Haihe River Sluice Based on the TCN-APCS-MLR Model | 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 Source Apportionment of Pollution in the Tianjin Haihe River Sluice Based on the TCN-APCS-MLR Model Weifu Ding, Yan Wang, Xueqian Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8461003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Environmental Geochemistry and Health → Version 1 posted 7 You are reading this latest preprint version Abstract River pollution identification in complex watersheds like the Haihe River Sluice faces challenges due to temporal dynamics and nonlinear driving mechanisms. Traditional receptor models (CMB, PMF, APCS-MLR) often lack the sensitivity required for accurate, timely source tracing. This study proposes the TCN-APCS-MLR model, integrating TCN with Absolute Principal Component Score-Multiple Linear Regression.Using daily water quality data from 2022 to 2024, the model leverages TCN’s causal and dilated convolutions to extract temporal dependencies, followed by PCA for dimensionality reduction and MLR for quantitative source estimation. This approach enhances temporal feature modeling while preserving interpretability.Results indicate:Superior Performance: The TCN-APCS-MLR model outperformed traditional methods, with R² values for EC, CODMn, and TN exceeding 0.85. Error Reduction: RMSE and MAE decreased by 20%–30% compared to the standard APCS-MLR model.Feature Extraction: The cumulative variance contribution of the first four principal components reached 81.87% (an 8% improvement).Source Apportionment Results:The Haihe River Sluice is primarily influenced by:Urban domestic and industrial wastewater (26.5%);Ecological self-purification processes (26.6%);Natural hydrological disturbances (25.1%);Agricultural non-point source pollution (21.8%).By incorporating deep learning, this model effectively addresses the limitations of linear models in complex estuarine environments, providing a robust technical framework for dynamic watershed management and ecological restoration. Pollution source apportionment Contribution rate quantification TCN APCS-MLR Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2026 Read the published version in Environmental Geochemistry and Health → Version 1 posted Editorial decision: Revision requested 02 Mar, 2026 Reviews received at journal 26 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers invited by journal 05 Jan, 2026 Editor assigned by journal 28 Dec, 2025 Submission checks completed at journal 27 Dec, 2025 First submitted to journal 27 Dec, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8461003","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569428583,"identity":"84ffb279-5d8c-4958-b311-1843002ca7ef","order_by":0,"name":"Weifu 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Traditional receptor models (CMB, PMF, APCS-MLR) often lack the sensitivity required for accurate, timely source tracing. This study proposes the TCN-APCS-MLR model, integrating TCN with Absolute Principal Component Score-Multiple Linear Regression.Using daily water quality data from 2022 to 2024, the model leverages TCN\u0026rsquo;s causal and dilated convolutions to extract temporal dependencies, followed by PCA for dimensionality reduction and MLR for quantitative source estimation.\u003c/p\u003e \u003cp\u003eThis approach enhances temporal feature modeling while preserving interpretability.Results indicate:Superior Performance: The TCN-APCS-MLR model outperformed traditional methods, with R\u0026sup2; values for EC, CODMn, and TN exceeding 0.85. Error Reduction: RMSE and MAE decreased by 20%\u0026ndash;30% compared to the standard APCS-MLR model.Feature Extraction: The cumulative variance contribution of the first four principal components reached 81.87% (an 8% improvement).Source Apportionment Results:The Haihe River Sluice is primarily influenced by:Urban domestic and industrial wastewater (26.5%);Ecological self-purification processes (26.6%);Natural hydrological disturbances (25.1%);Agricultural non-point source pollution (21.8%).By incorporating deep learning, this model effectively addresses the limitations of linear models in complex estuarine environments, providing a robust technical framework for dynamic watershed management and ecological restoration.\u003c/p\u003e","manuscriptTitle":"Source Apportionment of Pollution in the Tianjin Haihe River Sluice Based on the TCN-APCS-MLR Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 03:53:34","doi":"10.21203/rs.3.rs-8461003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-02T08:26:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-26T12:17:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190314048653645243730299590159125888676","date":"2026-01-18T04:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-05T09:52:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-28T07:34:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-27T15:49:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Geochemistry and Health","date":"2025-12-27T12:15:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-geochemistry-and-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"egah","sideBox":"Learn more about [Environmental Geochemistry and Health](https://www.springer.com/journal/10653)","snPcode":"10653","submissionUrl":"https://submission.nature.com/new-submission/10653/3","title":"Environmental Geochemistry and Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2f7bd32a-22fa-4039-8eca-01c0c2823f36","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:07:08+00:00","versionOfRecord":{"articleIdentity":"rs-8461003","link":"https://doi.org/10.1007/s10653-026-03139-2","journal":{"identity":"environmental-geochemistry-and-health","isVorOnly":false,"title":"Environmental Geochemistry and Health"},"publishedOn":"2026-03-31 16:00:06","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2026-01-07 03:53:34","video":"","vorDoi":"10.1007/s10653-026-03139-2","vorDoiUrl":"https://doi.org/10.1007/s10653-026-03139-2","workflowStages":[]},"version":"v1","identity":"rs-8461003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8461003","identity":"rs-8461003","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}