Reliability-Aware PM2.5 Mapping in Africa via Satellite–Reanalysis Fusion and Sparse Monitors

Reliability-Aware PM2.5 Mapping in Africa via Satellite–Reanalysis Fusion and Sparse Monitors | 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 Reliability-Aware PM 2.5 Mapping in Africa via Satellite–Reanalysis Fusion and Sparse Monitors Yaw Osei Adjei, Davis Opoku, Ephraim Abotsi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8735605/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 Africa’s sparse ground-based monitoring limits exposure assessment for fine particulate matter (PM2.5) and delays evidence-based air-quality interventions. We present a pan-African PM2.5 mapping pipeline that fuses public ground observations with satellite and reanalysis covariates and produces reliability-aware uncertainty for decision support. Using 2,068,901 quality-controlled PM2.5 records from 404 monitoring locations across 29 African countries (2016–2025), we integrate aerosol optical thickness, satellite NO2, planetary boundary layer height, meteorology, and population density. We evaluate generalization with leakage-resistant 5-fold location-grouped spatial cross-validation that holds out entire monitoring locations. Under this protocol, LightGBM achieves RMSE 30.83 +/- 5.07 ug/m3 and R^2 0.134 +/- 0.023 and provides stronger balance for AQI-style category prediction, while XGBoost yields slightly better regression accuracy. We quantify uncertainty with split-conformal prediction targeting 90% marginal coverage and demonstrate substantial regional heterogeneity, including severe degradation in East Africa consistent with covariate shift. We operationalize these findings with deterministic reliability flags, an uncertainty-and-population-based monitor prioritization score, and out-of-fold SHAP analyses to communicate when and why predictions should not be trusted. Environmental Engineering Artificial Intelligence and Machine Learning PM2.5 air quality Africa satellite reanalysis machine learning LightGBM XGBoost spatial cross-validation conformal prediction uncertainty quantification covariate shift SHAP reliability 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. 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-8735605","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582715428,"identity":"7ca86269-8571-4853-92da-b079ce252217","order_by":0,"name":"Yaw Osei Adjei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYFCChASGBCjzwAcgwcZOipaDM0BamAlrQTCZecAkAQ387QkPHzxss0vs5z988LDNr23yfMwMjB8+5uDWInHmQbJBYlty4swZaQmHc/tuG7YxMzBLztyGx5obCWkSiW3MuRtu8Bgczu25zQjUwsbMi0eL/I2E9B+JbfW5G86f/3DYsue2PUEtBkBbGBLbDuduOJDDcJjhx+1EgloMgX6RSDh3vB7oF4ODvQ23k9uYGZvx+kXueE7ixx9l1cb8/Icff/jx57bt/Pbmgx8+4vM+A08CAyMblM3YBiYb8KkHAvYDDAx/YJw/eBSOglEwCkbBiAUAdxla7GrxcooAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-3027-1246","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yaw","middleName":"Osei","lastName":"Adjei","suffix":""},{"id":582715543,"identity":"2828204e-a154-45c3-a41b-6a394f1739c8","order_by":1,"name":"Davis Opoku","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Davis","middleName":"","lastName":"Opoku","suffix":""},{"id":582715544,"identity":"4832c583-8715-4db4-90ac-490004bf25cd","order_by":2,"name":"Ephraim Abotsi","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ephraim","middleName":"","lastName":"Abotsi","suffix":""}],"badges":[],"createdAt":"2026-01-29 23:29:37","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-8735605/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8735605/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101942762,"identity":"a5b76edb-31c5-4f52-b851-25f61cc21f60","added_by":"auto","created_at":"2026-02-05 09:37:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2758169,"visible":true,"origin":"","legend":"","description":"","filename":"view.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8735605/v1_covered_2eb4d860-a3b3-4ece-bc57-2f48de364ba4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eReliability-Aware PM\u003csub\u003e2.5\u003c/sub\u003e Mapping in Africa via Satellite–Reanalysis Fusion and Sparse Monitors\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kwame Nkrumah University of Science and Technology","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":"PM2.5, air quality, Africa, satellite, reanalysis, machine learning, LightGBM, XGBoost, spatial cross-validation, conformal prediction, uncertainty quantification, covariate shift, SHAP, reliability","lastPublishedDoi":"10.21203/rs.3.rs-8735605/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8735605/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAfrica’s sparse ground-based monitoring limits exposure assessment for fine particulate matter (PM2.5) and delays evidence-based air-quality interventions. 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