Catalyzing Air Quality Index Prediction via Machine Learning: Unraveling the Influence of Urban Land Use

Catalyzing Air Quality Index Prediction via Machine Learning: Unraveling the Influence of Urban Land Use | 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 Catalyzing Air Quality Index Prediction via Machine Learning: Unraveling the Influence of Urban Land Use Nusrat Ullah Hasani, Faiyaz Ahmad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7819940/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Currently, air pollution is an immense challenge for urban areas, directly affecting both human health and the environment. The capacity to accurately predict the Air Quality Index (AQI) is crucial for understanding and managing pollution. This work presents a Machine learning (ML) approach to predict AQI by utilising statistical metrics of air quality data alongside temporal attributes such as year, month, and day. The utilised dataset had 282 items, with attributes including the interquartile range (IQR), median AQI, and 25th percentile values (p25). Linear regression (LR) and random forest (RF) models were employed. The R² values for both models exceeded 98 percent, indicating the reliability of the approach and the models' capacity for precise predictions. The analysis of feature importance revealed that statistical characteristics, specifically p25 and IQR, were the most significant factors in predicting the air quality index (AQI). Temporal features also provided significant insights for the prediction. The results demonstrate that machine learning can serve as an effective tool for aiding city planners and policymakers in the control and oversight of air quality in urban environments. Air Quality Index Machine Learning Ozone Prediction Random Forest Linear Regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 23 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 06 Apr, 2026 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-7819940","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638865587,"identity":"e0843396-adf5-4e1c-afa7-bc1341874667","order_by":0,"name":"Nusrat Ullah Hasani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACCQgpx8/eAKQNLIjXYizZcwCkRYJoLQyJG24kIHHxAXPp5scffu6wSGy4+fzqhh8FEgz87d0JeLVYzjlmJtl7RsK4cXZO2c0eoMMkzpzdgFeLwY0EMwbeNgnZZumctBs8QC0GErmEtKR//vi3TYKxTfJM2s0/xGnJMZAG2qLYI8F+7DaRtuSUScu2SRhL8OSw3ZYxkOAhwi/pmz++bauTsz9+/NnNN39s5Pjbe/FrQQI8BmCSWOUgwP6AFNWjYBSMglEwggAAOCJHiDDHn7UAAAAASUVORK5CYII=","orcid":"","institution":"Integral University","correspondingAuthor":true,"prefix":"","firstName":"Nusrat","middleName":"Ullah","lastName":"Hasani","suffix":""},{"id":638865588,"identity":"18272e5a-eb33-4aa0-b36b-d60d73d27210","order_by":1,"name":"Faiyaz Ahmad","email":"","orcid":"","institution":"Integral University","correspondingAuthor":false,"prefix":"","firstName":"Faiyaz","middleName":"","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2025-10-09 17:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7819940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7819940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109405574,"identity":"8d041010-6cb8-4e8e-bdab-b5e774307e1f","added_by":"auto","created_at":"2026-05-17 13:19:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9460854,"visible":true,"origin":"","legend":"","description":"","filename":"NusratPaper06042026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7819940/v1_covered_b14566f6-243c-416f-b053-254da7fb8a4a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Catalyzing Air Quality Index Prediction via Machine Learning: Unraveling the Influence of Urban Land Use","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Air Quality Index, Machine Learning, Ozone Prediction, Random Forest, Linear Regression","lastPublishedDoi":"10.21203/rs.3.rs-7819940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7819940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Currently, air pollution is an immense challenge for urban areas, directly affecting both human health and the environment. 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