Spatio–temporal patterns of river water quality based on entropy-weighted monitoring data: A case study of the Tanjiang River Basin, China

Spatio–temporal patterns of river water quality based on entropy-weighted monitoring data: A case study of the Tanjiang River Basin, 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 Spatio–temporal patterns of river water quality based on entropy-weighted monitoring data: A case study of the Tanjiang River Basin, China Chuangang Li, Juan Huang, Shuzhi Nie, Bozi Cen, Weitao Chen, Zihui Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8611570/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Reliable river water quality assessment at the basin scale requires the joint interpretation of long-term monitoring data across time, space, and multiple indicators. However, most routine assessment approaches still analyze these dimensions separately, which limits their ability to identify priority pollution periods and critical river sections for management.In this study, a spatio--temporal--indicator assessment framework was developed to support integrated interpretation of monitoring data. Monthly observations from 82 monitoring sections in the Tanjiang River Basin (South China) during 2019--2023 were organised into a three-dimensional data matrix and analysed using three-dimensional principal component analysis (3D-PCA) combined with entropy-weighted water quality indices (WQI-3D and WQI \((^\ast)\) ).The results show that seasonal hydrological variability and nutrient-related anthropogenic pollution represent the two dominant drivers of basin-scale water quality. Ammonia nitrogen (NH \((_3)\) –N) and total phosphorus (TP) were identified as the most influential indicators, and pollution pressure was significantly higher during the flood season (June–October). Spatial analysis revealed persistent water-quality hotspots in urban-industrial tributaries, including the Longwan, Mazongsha, and Tiansha Rivers. Compared with the conventional WQI-3D, the enhanced index WQI \((^\ast)\) more clearly highlighted spatio-temporally coincident pollution extremes, improving the identification of critical monitoring sites and periods.The proposed framework provides a practical and data-driven tool for basin-scale water-quality monitoring and management, enabling more targeted pollution control and prioritisation within existing monitoring networks. Water quality assessment Environmental monitoring Spatio--temporal analysis Entropy weighting River basin management Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8611570","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590691040,"identity":"b3a7d90f-710a-4d49-8501-6871e83d9509","order_by":0,"name":"Chuangang 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Ammonia nitrogen (NH\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_3)\\)\u003c/span\u003e\u003c/span\u003e\u0026ndash;N) and total phosphorus (TP) were identified as the most influential indicators, and pollution pressure was significantly higher during the flood season (June\u0026ndash;October). Spatial analysis revealed persistent water-quality hotspots in urban-industrial tributaries, including the Longwan, Mazongsha, and Tiansha Rivers. 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