Dynamic Calibration of Low-Cost PM2.5 Sensors Using Trust-Based Consensus Mechanisms

Dynamic Calibration of Low-Cost PM2.5 Sensors Using Trust-Based Consensus Mechanisms | 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 Dynamic Calibration of Low-Cost PM2.5 Sensors Using Trust-Based Consensus Mechanisms Sachit Mahajan, Dirk Helbing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6048236/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jul, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted 10 You are reading this latest preprint version Abstract Low-cost particulate matter (PM) sensors enable high-resolution urban air quality monitoring but face challenges from offsets, scaling mismatches, and drift. We propose an adaptive trust-based calibration framework that first corrects systematic errors and then dynamically adjusts model complexity based on sensor reliability. Extensive simulations and real-world deployment in Zurich, Switzerland validate the approach. Each sensor’s trust score integrates four indicators: accuracy, stability, responsiveness, and consensus alignment. High-trust sensors receive minimal correction, preserving baseline accuracy, while low-trust sensors leverage expanded wavelet-based features and deeper models. Results show mean absolute error (MAE) reductions of up to 68% for poorly performing sensors and 35-38% for reliable ones, outperforming conventional calibration methods. By using trust-weighted consensus, the framework reduces dependence on large training datasets and frequent re-calibrations, ensuring scalability. These findings demonstrate that dynamic, trust-driven calibration can substantially enhance low-cost sensor network accuracy across both controlled scenarios and complex real-world environments. Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Jul, 2025 Read the published version in npj Climate and Atmospheric Science → Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 04 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 02 May, 2025 Submission checks completed at journal 02 May, 2025 First submitted to journal 24 Apr, 2025 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-6048236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451598099,"identity":"9bff8922-f094-4018-a0ce-deb5ce961ca9","order_by":0,"name":"Sachit Mahajan","email":"data:image/png;base64,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","orcid":"","institution":"ETH Zurich","correspondingAuthor":true,"prefix":"","firstName":"Sachit","middleName":"","lastName":"Mahajan","suffix":""},{"id":451598100,"identity":"51c9a3b3-756d-408a-8e1c-e2d47114b908","order_by":1,"name":"Dirk Helbing","email":"","orcid":"","institution":"ETH Zurich","correspondingAuthor":false,"prefix":"","firstName":"Dirk","middleName":"","lastName":"Helbing","suffix":""}],"badges":[],"createdAt":"2025-02-17 13:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6048236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6048236/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-025-01145-2","type":"published","date":"2025-07-05T15:58:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86179585,"identity":"c0de263a-f71f-4aa3-8e8b-04fb634416a2","added_by":"auto","created_at":"2025-07-07 16:17:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5655316,"visible":true,"origin":"","legend":"","description":"","filename":"consensusiot.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6048236/v1_covered_e2d3be55-4156-4a5d-804c-4352b8dd391d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Calibration of Low-Cost PM2.5 Sensors Using Trust-Based Consensus Mechanisms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6048236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6048236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Low-cost particulate matter (PM) sensors enable high-resolution urban air quality monitoring but face challenges from offsets, scaling mismatches, and drift. 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