PM2.5 Concentration Prediction Using a CNN-Optimized LSTM-GRU Integrated Model

PM2.5 Concentration Prediction Using a CNN-Optimized LSTM-GRU Integrated 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 Research Article PM2.5 Concentration Prediction Using a CNN-Optimized LSTM-GRU Integrated Model Yue-xin GENG, Shu Xu, Si-yang LIU, Jiao-nei WU, Hong-xi TAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9121418/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 With the intensification of air pollution issues, accurate prediction of pollutant concentration variations such as PM2.5 has become critical for air quality management. However, single models exhibit limited predictive capabilities when handling complex time-series data. To address this, this paper proposes a CNN-optimized LSTM-GRU integrated model to enhance PM2.5 prediction accuracy. The model synergistically combines the advantages of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). First, CNN extracts local features from time-series data to identify short-term dependencies. Subsequently, bidirectional LSTM processes the CNN-extracted features to capture long-term trends. Finally, GRU further refines the LSTM outputs, simplifying computational processes while strengthening temporal dependency modeling. Through this hierarchical integration, the model comprehensively captures the characteristics of pollutant concentration variations. Experiments were conducted on PM2.5 prediction in Dalian City, with comparisons to existing research models. Results demonstrate that the CNN-optimized LSTM-GRU integrated model achieves a significantly lower Root Mean Square Error (RMSE) than other models, indicating superior predictive performance. The proposed model exhibits high accuracy and stability in PM2.5 prediction tasks, validating its advantages in processing time-series data. Compared to traditional single models, this integrated approach demonstrates enhanced temporal feature-capturing capabilities, providing novel insights and tools for future air quality prediction and related applications. Artificial Intelligence and Machine Learning Deep learning LSTM GRU CNN Time series prediction Full Text Additional Declarations The authors declare no competing interests. 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-9121418","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606033441,"identity":"270a42d3-30fa-4260-90dc-97508edded89","order_by":0,"name":"Yue-xin GENG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYDADNgbmAyRrYUsg2R4eA+LUybv3Hn7N22aT2Cfd8/HGGwY7Od0GAloMz5xLs+ZtSzNmkzm72XIOQ7Kx2QFCWmbkmBnzth2WY5PI3SbNw3AgcRtBLfPfgLT852GTyHlGnBZ5CR7jx7xtB4C25LARp8WAJ8eMcc65ZGM2iTRjyzkGRPhFvv2M8Yc3ZXaJ82ckP7zxpsJOjqAWgwMMbFI8UI4EUVEj38DA/PEHXAsROkbBKBgFo2DkAQAv+DvQv6WQ/QAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-5385-2126","institution":"Dalian Ocean University","correspondingAuthor":true,"prefix":"","firstName":"Yue-xin","middleName":"","lastName":"GENG","suffix":""},{"id":606037118,"identity":"d1d4d996-ece1-479b-8a3a-305a9c6cb5de","order_by":1,"name":"Shu Xu","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Xu","suffix":""},{"id":606037119,"identity":"241b618b-fa10-411c-82fe-5f8111342d3d","order_by":2,"name":"Si-yang LIU","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Si-yang","middleName":"","lastName":"LIU","suffix":""},{"id":606037120,"identity":"b2a8faa3-61af-464e-9648-2c12f1b3f378","order_by":3,"name":"Jiao-nei WU","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Jiao-nei","middleName":"","lastName":"WU","suffix":""},{"id":606037121,"identity":"3c6b44f6-a30f-44a4-85cc-16bf1c3ddd37","order_by":4,"name":"Hong-xi TAO","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Hong-xi","middleName":"","lastName":"TAO","suffix":""}],"badges":[],"createdAt":"2026-03-14 09:41:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9121418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9121418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105751773,"identity":"1e18d506-c7ab-4f60-ba71-980fbf7ab912","added_by":"auto","created_at":"2026-03-30 15:43:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":554941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptpreprint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9121418/v1_covered_5ab7ae28-46d8-4ff3-9139-ad78e8192d7e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePM2.5 Concentration Prediction Using a CNN-Optimized LSTM-GRU Integrated Model\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Dalian Ocean University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Deep learning, LSTM, GRU, CNN, Time series prediction","lastPublishedDoi":"10.21203/rs.3.rs-9121418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9121418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the intensification of air pollution issues, accurate prediction of pollutant concentration variations such as PM2.5 has become critical for air quality management. 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