Assessment of Radioactive Isotopes in Mineral Water in Arak City: Machine Learning Models for Enhancing Water Safety

Assessment of Radioactive Isotopes in Mineral Water in Arak City: Machine Learning Models for Enhancing Water Safety | 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 Article Assessment of Radioactive Isotopes in Mineral Water in Arak City: Machine Learning Models for Enhancing Water Safety Fatemeh Ranjbar, Hossein Sadeghi, Reza Pourimani, Soraya Khanmohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5306358/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially leading to cancer and genetic mutations. A study was conducted using random sampling to assess the concentrations of radioactive isotopes and heavy metals in mineral water from Arak City. Notably, specific radiation levels of Ra-226 were not detected, whereas the concentrations of Th-232, K-40, and Cs-137 were found to be below the thresholds established by the World Health Organization (WHO). The annual effective doses derived from the consumption of bottled water were significantly lower than the limits set by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), thereby reducing the risk of cancer. Furthermore, heavy metals such as lead and chromium were not present in the samples, thereby contributing to the overall safety of the water. The Machine Learning (ML) models employed in this study provided accurate predictions, ensuring reliability across various demographic groups and reinforcing the robustness of the findings. Overall, the results suggest that mineral water consumption poses minimal health risks. Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors Physical sciences/Energy science and technology Mineral water Water safety Machine learning Health risks Radioactive isotopes Potential cancer risk Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Dec, 2024 Reviews received at journal 01 Dec, 2024 Reviews received at journal 29 Nov, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 07 Nov, 2024 Editor assigned by journal 07 Nov, 2024 Editor invited by journal 07 Nov, 2024 Submission checks completed at journal 05 Nov, 2024 First submitted to journal 21 Oct, 2024 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. 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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-5306358","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379118655,"identity":"0da56568-3b76-4593-ad19-0b83a7e7e630","order_by":0,"name":"Fatemeh Ranjbar","email":"","orcid":"","institution":"Arak University","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Ranjbar","suffix":""},{"id":379118656,"identity":"6890a72d-dd58-4070-9a5e-27d2e3c8cf3b","order_by":1,"name":"Hossein Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACgwPsDxh4GBgS2NgbgNwDENED+LXwGEC08BwgXgsDWAuDRAIhtQgtjB/eVNTm8Uk+fvbhx5nDDPztBxgPV+DRYn+A/bHknDPHi9mk04xn9tw4zCBxJoHh4Bl8ttx/YCDN23YssU06wZiB58NhBoYbDAwHG/A6jMH4N+8/oBbJ458Z/wC1yBOhxUyat6EmsU2Cx5iZB+gwA8JaeMws5xw7kNjGk1PMLHMmncfwTGIDAS3sj2+8qalLnN9+fDPjm2PWcnLHDx/+iE8LFByGs4BxxEiEBgaGOmIUjYJRMApGwUgFALkNVzYUpbuDAAAAAElFTkSuQmCC","orcid":"","institution":"Arak University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Sadeghi","suffix":""},{"id":379118657,"identity":"4d1f157e-b16e-471b-a0fb-bb83b4e39074","order_by":2,"name":"Reza Pourimani","email":"","orcid":"","institution":"Arak University","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Pourimani","suffix":""},{"id":379118658,"identity":"02d9c757-1a3b-4ea1-95c2-d84a041d7dca","order_by":3,"name":"Soraya Khanmohammadi","email":"","orcid":"","institution":"Tarbiat Modares University","correspondingAuthor":false,"prefix":"","firstName":"Soraya","middleName":"","lastName":"Khanmohammadi","suffix":""}],"badges":[],"createdAt":"2024-10-21 17:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5306358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5306358/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-88431-4","type":"published","date":"2025-01-30T15:57:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75351277,"identity":"9dbc303e-e69b-4def-a94b-fe1b9be3532c","added_by":"auto","created_at":"2025-02-03 16:08:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646695,"visible":true,"origin":"","legend":"","description":"","filename":"WaterMLCor1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5306358/v1_covered_35cf32ef-138e-4f15-8a39-d1f3de802a6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Radioactive Isotopes in Mineral Water in Arak City: Machine Learning Models for Enhancing Water Safety","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mineral water, Water safety, Machine learning, Health risks, Radioactive isotopes, Potential cancer risk","lastPublishedDoi":"10.21203/rs.3.rs-5306358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5306358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHumans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. 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