Heavy Metal Contamination in Urban Water Systems near Landfills: Insights from WQI, GIS and Data-Driven Modeling

Heavy Metal Contamination in Urban Water Systems near Landfills: Insights from WQI, GIS and Data-Driven Modeling | 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 Heavy Metal Contamination in Urban Water Systems near Landfills: Insights from WQI, GIS and Data-Driven Modeling Tanvir Ahmed, Nazmus Sakib, Remon Mia, Al-Redwan Bijoy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8710125/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 Urban landfill activities can significantly alter the quality of nearby water systems through the release of metal-enriched leachate, raising concerns for environmental sustainability and public health in rapidly urbanizing regions. This study assesses heavy metal contamination in urban water systems located near landfill sites using an integrated approach combining Water Quality Index (WQI), GIS-based spatial analysis, and data-driven modeling. Groundwater samples were analyzed for key physicochemical parameters and selected heavy metals, including iron (Fe), manganese (Mn), and lead (Pb). Water quality conditions were evaluated using WQI, while spatial mapping was applied to identify contamination patterns and high-risk zones associated with landfill proximity. Data-driven models were employed as exploratory tools to examine inter-parameter relationships under data-limited conditions. The results indicate substantial spatial variability in water quality, with degraded WQI values and elevated heavy metal concentrations observed predominantly near landfill areas. Lead contamination emerged as the most critical concern, frequently exceeding drinking water guideline values and indicating potential public health risk. Although predictive performance of data-driven models was constrained by limited sample size, feature importance analysis provided meaningful insight into key physicochemical and anthropogenic factors influencing heavy metal distribution. Overall, the findings demonstrate that integrated risk-based assessment and spatial analysis offer reliable support for identifying contamination hotspots and informing water quality management in landfill-impacted urban environments. Environmental Engineering Landfill leachate Heavy metal contamination Water Quality Index (WQI) GIS-based spatial analysis Machine learning models Urban water systems Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryS1DataCode.zip Supplementary File S1: Data and Code for WQI–GIS–ML Analyses 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-8710125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581132333,"identity":"3f84bfdb-47cd-4b5e-a257-e9819f1f09bd","order_by":0,"name":"Tanvir 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Urban Water Systems near Landfills: Insights from WQI, GIS and Data-Driven Modeling\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Information Technology and Sciences","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Landfill leachate, Heavy metal contamination, Water Quality Index (WQI), GIS-based spatial analysis, Machine learning models, Urban water systems","lastPublishedDoi":"10.21203/rs.3.rs-8710125/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8710125/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban \u0026nbsp;\u0026nbsp;landfill activities can significantly alter the quality of nearby water \u0026nbsp;\u0026nbsp;systems through the release of metal-enriched leachate, raising concerns for \u0026nbsp;\u0026nbsp;environmental sustainability and public health in rapidly urbanizing regions. \u0026nbsp;\u0026nbsp;This study assesses heavy metal contamination in urban water systems located \u0026nbsp;\u0026nbsp;near landfill sites using an integrated approach combining Water Quality \u0026nbsp;\u0026nbsp;Index (WQI), GIS-based spatial analysis, and data-driven modeling.\u003c/p\u003e\n\u003cp\u003eGroundwater \u0026nbsp;\u0026nbsp;samples were analyzed for key physicochemical parameters and selected heavy \u0026nbsp;\u0026nbsp;metals, including iron (Fe), manganese (Mn), and lead (Pb). Water quality \u0026nbsp;\u0026nbsp;conditions were evaluated using WQI, while spatial mapping was applied to \u0026nbsp;\u0026nbsp;identify contamination patterns and high-risk zones associated with landfill \u0026nbsp;\u0026nbsp;proximity. Data-driven models were employed as exploratory tools to examine \u0026nbsp;\u0026nbsp;inter-parameter relationships under data-limited conditions.\u003c/p\u003e\n\u003cp\u003eThe \u0026nbsp;\u0026nbsp;results indicate substantial spatial variability in water quality, with \u0026nbsp;\u0026nbsp;degraded WQI values and elevated heavy metal concentrations observed \u0026nbsp;\u0026nbsp;predominantly near landfill areas. Lead contamination emerged as the most \u0026nbsp;\u0026nbsp;critical concern, frequently exceeding drinking water guideline values and \u0026nbsp;\u0026nbsp;indicating potential public health risk. Although predictive performance of \u0026nbsp;\u0026nbsp;data-driven models was constrained by limited sample size, feature importance \u0026nbsp;\u0026nbsp;analysis provided meaningful insight into key physicochemical and \u0026nbsp;\u0026nbsp;anthropogenic factors influencing heavy metal distribution.\u003c/p\u003e\n\u003cp\u003eOverall, \u0026nbsp;\u0026nbsp;the findings demonstrate that integrated risk-based assessment and spatial \u0026nbsp;\u0026nbsp;analysis offer reliable support for identifying contamination hotspots and \u0026nbsp;\u0026nbsp;informing water quality management in landfill-impacted urban environments.\u003c/p\u003e","manuscriptTitle":"Heavy Metal Contamination in Urban Water Systems near Landfills: Insights from WQI, GIS and Data-Driven Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 09:50:54","doi":"10.21203/rs.3.rs-8710125/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aff27569-f5c2-4fc8-b491-909e209d5c08","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61821194,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2026-01-28T09:50:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 09:50:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8710125","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8710125","identity":"rs-8710125","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}