Spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Marmara Region based on GIS and space–time cube analysis

Spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Marmara Region based on GIS and space–time cube analysis | 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 Spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Marmara Region based on GIS and space–time cube analysis Arif Çağdaş Aydınoğlu, Ali Doğan Gümüşsoy, Arzu Erener This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8899011/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 Air pollution remains one of the most critical environmental problems affecting human health, ecosystems, and sustainable urban development, particularly in rapidly urbanizing and industrialized regions. Particulate matter (PM10) and sulfur dioxide (SO₂) are among the most critical air pollutants because of their adverse effects on human health, atmospheric processes, and ecosystem integrity. The increasing availability of long-term, high-frequency air quality monitoring data has introduced a big data dimension to environmental studies, necessitating advanced spatial and spatiotemporal analytical approaches to effectively capture the pollution dynamics. This study investigated the spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Eastern Marmara Region for the periods 2014, 2020, and 2024. Daily air quality measurements were aggregated into seasonal and annual averages to assess the long-term changes in pollution levels. Geographic Information System (GIS)-based spatial analysis techniques were employed to generate continuous pollution surfaces. In addition, space–time cube modeling was applied to integrate spatial and temporal dimensions, enabling the identification of emerging trends, persistent hotspots, and temporal shifts in the air pollution levels. The results revealed distinct spatial contrasts and seasonal variations across the study area, with elevated concentrations generally associated with industrial zones and major transportation corridors, whereas lower levels were observed in coastal and less urbanized areas. The integration of GIS-based methods and space–time cube analysis demonstrates the effectiveness of big-data-driven spatiotemporal approaches in understanding complex air-pollution dynamics. It supports informed decision-making for regional air-quality management. PM10 SO₂ air pollution spatiotemporal analysis GIS-based analysis space–time cube big data 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-8899011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594079567,"identity":"03273cbe-6283-4e2f-89d2-97f948f8c04c","order_by":0,"name":"Arif Çağdaş Aydınoğlu","email":"","orcid":"","institution":"Gebze Technical University","correspondingAuthor":false,"prefix":"","firstName":"Arif","middleName":"Çağdaş","lastName":"Aydınoğlu","suffix":""},{"id":594079568,"identity":"9008a4f9-c765-42e9-83d0-571d408b9e6b","order_by":1,"name":"Ali Doğan Gümüşsoy","email":"","orcid":"","institution":"Gebze Technical University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Doğan","lastName":"Gümüşsoy","suffix":""},{"id":594079569,"identity":"801bfdaa-8b28-4b95-8178-9ef184bd164c","order_by":2,"name":"Arzu Erener","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3Rv0rEMBzA8fwIpEu9W3MI7Su0HPgH8e5VchTqLMJRQTAg5JaKa8WXiE9gpdAuoXNvUbt0PjcXxVx1sz06OuS7/LJ8+IUEIZPpnwc13w4L9CDDCPZbgn+JPYCQyQ9pz/1k/FA0FEUvzqF121wm0cwdY8zRZpmh+X7aSWjJQorUxfQ4Lg7WUgX+/Q1wSMoM2SPWvUahnIJgC1mFZF2LFGQGHO8JTXpu5ioQFL7YtXxtyLkm86ct+dxBPIUJBc6YVxECjyJdSP1iGHYQXxF8xHLmSxXiSaKCINFbnuPyzLZVN3GUVVebK+Z6RQ7vcTQ7vVut6reP5Yljxd2k7c/LpGjYT5pMJpOpp29BsVg3D+U3JgAAAABJRU5ErkJggg==","orcid":"","institution":"Kocaeli University","correspondingAuthor":true,"prefix":"","firstName":"Arzu","middleName":"","lastName":"Erener","suffix":""}],"badges":[],"createdAt":"2026-02-17 08:25:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8899011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8899011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104357677,"identity":"44ffd87c-c6a1-4ecf-91ec-fd607b529685","added_by":"auto","created_at":"2026-03-10 23:23:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2143673,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscripttimeboxERENERrev.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8899011/v1_covered_e9ec7a79-0922-4dd2-b236-17d900522fa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Marmara Region based on GIS and space–time cube analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"PM10, SO₂, air pollution, spatiotemporal analysis, GIS-based analysis, space–time cube, big data","lastPublishedDoi":"10.21203/rs.3.rs-8899011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8899011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAir pollution remains one of the most critical environmental problems affecting human health, ecosystems, and sustainable urban development, particularly in rapidly urbanizing and industrialized regions. Particulate matter (PM10) and sulfur dioxide (SO₂) are among the most critical air pollutants because of their adverse effects on human health, atmospheric processes, and ecosystem integrity. The increasing availability of long-term, high-frequency air quality monitoring data has introduced a big data dimension to environmental studies, necessitating advanced spatial and spatiotemporal analytical approaches to effectively capture the pollution dynamics. This study investigated the spatiotemporal patterns and trends of PM10 and SO₂ concentrations in the Eastern Marmara Region for the periods 2014, 2020, and 2024. Daily air quality measurements were aggregated into seasonal and annual averages to assess the long-term changes in pollution levels. Geographic Information System (GIS)-based spatial analysis techniques were employed to generate continuous pollution surfaces. 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