Spatiotemporal Variation and Driving Mechanisms of Methane Concentration in Tianjin Based on the DCNM-GTWR Hybrid Model

Spatiotemporal Variation and Driving Mechanisms of Methane Concentration in Tianjin Based on the DCNM-GTWR Hybrid 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 Article Spatiotemporal Variation and Driving Mechanisms of Methane Concentration in Tianjin Based on the DCNM-GTWR Hybrid Model Minghui Liu, Jincong Sun, Yixuan Chen, Rundong Zhang, Xiwen Duan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8169343/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 To examine the spatiotemporal dynamics of methane in Tianjin and their interactions with multiple drivers, this study employed the DCNM and GTWR models to identify determinants of concentration variability. The findings indicate that methane concentrations showed a significant upward trend, with a citywide mean of 1,907.93ppb during 2019–2024 and an average annual increase of 11.94ppb. Concentrations exhibited a clear seasonal cycle—lower in spring and higher in autumn—with the minimum in March and the maximum in October. Elevated concentrations occurred primarily in coastal areas, heavy-industrial zones, and densely populated districts; values in the 1,915–1,930ppb range represented 54.68% of observations. Spatially, summer and autumn displayed higher concentrations in the north and lower in the south, whereas winter showed higher concentrations in the center and lower toward the periphery. DCNM results indicate that methane concentration variability is primarily associated with trends in D-LST, LAI, and the NDVI. The majority of variables exhibit pronounced non-stationarity and seasonal characteristics, substantiating that D-LST and the NDVI play a decisive role in the spatiotemporal variation of methane concentration. The GTWR model achieved a strong fit (R² = 0.822). It identified lagged D-LST and N-LST, NDVI, and prior methane concentration as key predictors; the corresponding coefficients display significant spatial non-stationarity. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Tianjin methane concentration DCNM GTWR spatial and temporal distribution 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. 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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-8169343","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583342670,"identity":"4d3bd929-4569-4a8e-bb22-da2345c04401","order_by":0,"name":"Minghui Liu","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Liu","suffix":""},{"id":583342671,"identity":"5ccf6d69-8e96-44a1-8215-965f21f53f76","order_by":1,"name":"Jincong Sun","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jincong","middleName":"","lastName":"Sun","suffix":""},{"id":583342672,"identity":"636b9f53-d9cb-4dc8-a216-04b461973130","order_by":2,"name":"Yixuan Chen","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Chen","suffix":""},{"id":583342673,"identity":"f50dd34c-eb02-4f14-906b-d163ee823b8d","order_by":3,"name":"Rundong Zhang","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Rundong","middleName":"","lastName":"Zhang","suffix":""},{"id":583342674,"identity":"20729df8-3dd3-4e46-8c7b-811d7b2e1be5","order_by":4,"name":"Xiwen Duan","email":"","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiwen","middleName":"","lastName":"Duan","suffix":""},{"id":583342675,"identity":"bd462493-5c36-4cc3-8ac2-67670466e13b","order_by":5,"name":"Jianxiong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYLACCQYbKIuNeC1ppGphYDhMghaD42cPv7BsO5+4nf2MAcOHssMM/LMbCGg5k5dmIXHmduLOnhwDxhnnDjNI3DmAX4vZgRwzA4mK24kbDuQYMPO2HWYwkEggoOX8G6AWg3OJG86/MWD+S5SWGznGDyQqDiRuuAG0hZEYLfY33pgxSJxJNt5w41nBwZ5z6TwSNwhokezPMf4s2WYnu+F88sYHP8qs5fhnENACBGzSEgwMjg1A1gEg5iGoHgiYP34AOpAYlaNgFIyCUTBCAQBHdEcZQ1T9ZwAAAABJRU5ErkJggg==","orcid":"","institution":"Yunnan Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Jianxiong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-11-21 04:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8169343/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8169343/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108013106,"identity":"09150bfa-3644-4f7b-971c-002d350931d8","added_by":"auto","created_at":"2026-04-28 13:17:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1824809,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript..pdf","url":"https://assets-eu.researchsquare.com/files/rs-8169343/v1_covered_2bc32d78-1687-4c5b-9215-e1329ef2832e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal Variation and Driving Mechanisms of Methane Concentration in Tianjin Based on the DCNM-GTWR Hybrid Model","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":"Tianjin, methane concentration, DCNM, GTWR, spatial and temporal distribution","lastPublishedDoi":"10.21203/rs.3.rs-8169343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8169343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo examine the spatiotemporal dynamics of methane in Tianjin and their interactions with multiple drivers, this study employed the DCNM and GTWR models to identify determinants of concentration variability. The findings indicate that methane concentrations showed a significant upward trend, with a citywide mean of 1,907.93ppb during 2019\u0026ndash;2024 and an average annual increase of 11.94ppb. Concentrations exhibited a clear seasonal cycle\u0026mdash;lower in spring and higher in autumn\u0026mdash;with the minimum in March and the maximum in October. Elevated concentrations occurred primarily in coastal areas, heavy-industrial zones, and densely populated districts; values in the 1,915\u0026ndash;1,930ppb range represented 54.68% of observations. Spatially, summer and autumn displayed higher concentrations in the north and lower in the south, whereas winter showed higher concentrations in the center and lower toward the periphery. DCNM results indicate that methane concentration variability is primarily associated with trends in D-LST, LAI, and the NDVI. The majority of variables exhibit pronounced non-stationarity and seasonal characteristics, substantiating that D-LST and the NDVI play a decisive role in the spatiotemporal variation of methane concentration. The GTWR model achieved a strong fit (R\u0026sup2; = 0.822). It identified lagged D-LST and N-LST, NDVI, and prior methane concentration as key predictors; the corresponding coefficients display significant spatial non-stationarity.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Variation and Driving Mechanisms of Methane Concentration in Tianjin Based on the DCNM-GTWR Hybrid Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 22:51:01","doi":"10.21203/rs.3.rs-8169343/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":"10f9490f-0167-480f-a7e7-c2d82f4c6d27","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62067090,"name":"Earth and environmental sciences/Climate sciences"},{"id":62067091,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-04-28T13:16:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 22:51:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8169343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8169343","identity":"rs-8169343","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}