Satellite-Based Estimation and Long-Term Trends of PM₂.₅ in Southern Nepal Using AOD and Meteorological Reanalysis

Satellite-Based Estimation and Long-Term Trends of PM₂.₅ in Southern Nepal Using AOD and Meteorological Reanalysis | 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 Satellite-Based Estimation and Long-Term Trends of PM₂.₅ in Southern Nepal Using AOD and Meteorological Reanalysis Govinda Prasad Lamichhane, Nabina Maharjan, Niroj Timalsina This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8733880/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 Fine particulate matter (PM₂.₅) poses a significant threat to public health and environmental quality in Nepal’s Tarai and Dune Valley regions. This study characterizes the spatiotemporal variability of PM₂.₅ from 2019 to 2021 by integrating ground-based observations, satellite-derived Aerosol Optical Depth (AOD), gaseous pollutants, and meteorological data. Daily PM₂.₅ measurements from six monitoring stations were analyzed alongside MODIS AOD, TROPOMI CO, NO₂, and SO₂ data, and ERA5 meteorological variables including temperature, relative humidity, and wind components. Correlation and regression analyses revealed strong associations between PM₂.₅ and AOD, CO, and temperature, while relative humidity and wind components exhibited moderate effects. Single linear regression explained 34–38% of PM₂.₅ variability, whereas multiple regression and Random Forest models improved predictive performance (R² ≈ 0.54–0.65, RMSE ≈ 22–26 µg/m³), accurately capturing seasonal and regional differences. Simulated PM₂.₅ concentrations reconstructed long-term trends (2000–2023), highlighting increasing levels in eastern regions (Jhumka) and relatively stable or declining trends in western regions (Dang, Bhimdatta). Seasonal analysis showed the highest concentrations during winter and pre-monsoon periods, and substantial reductions during monsoon months due to rainfall-driven pollutant washout. The results underscore the importance of integrating satellite and ground-based data with statistical modeling to assess historical air quality, identify pollution hotspots, and inform evidence-based mitigation strategies. This framework provides a robust basis for air quality management and public health planning in regions with limited monitoring infrastructure. PM₂.₅ Aerosol Optical Depth Random Forest Spatiotemporal trends Southern Nepal Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Editor assigned by journal 15 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 29 Jan, 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. 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-8733880","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585959321,"identity":"54c5b5a8-aece-4714-9e22-df15ad768faa","order_by":0,"name":"Govinda Prasad Lamichhane","email":"","orcid":"","institution":"Ministry of Forests and Environment","correspondingAuthor":false,"prefix":"","firstName":"Govinda","middleName":"Prasad","lastName":"Lamichhane","suffix":""},{"id":585959322,"identity":"298b63a4-84d7-4b22-a839-80ee94823613","order_by":1,"name":"Nabina Maharjan","email":"","orcid":"","institution":"Ministry of Forests and Environment","correspondingAuthor":false,"prefix":"","firstName":"Nabina","middleName":"","lastName":"Maharjan","suffix":""},{"id":585959324,"identity":"ab80727f-b283-4dc0-96dc-1f66886708c1","order_by":2,"name":"Niroj Timalsina","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYDACZiCWAGI29gYgaWBBghY+ngMgLRIk2CYnkcAA1U0AmLPzPnxgUXEnsU3y+dUNPwokGPjbuxPwarFsZjc2kDjzLLFNOqfsZg/QYRJnzm7Aq8XgMBubhGTbYWM26Zy0GzxALQYSucRo+QfUInkm7eYf4rU0HJZjk2A/dptYW5gNJI4BtfDksN2WMZDgIeyX88cYH0vUHOaRbz/+7OabPzZy/O29+LWAADMkMngMwCRB5SDA+AFMsT8gSvUoGAWjYBSMPAAAQgk+vdMoCqcAAAAASUVORK5CYII=","orcid":"","institution":"Tribhuvan University","correspondingAuthor":true,"prefix":"","firstName":"Niroj","middleName":"","lastName":"Timalsina","suffix":""}],"badges":[],"createdAt":"2026-01-29 16:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8733880/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8733880/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101949997,"identity":"cd36cdf4-0a29-41e6-9123-9d154ce04589","added_by":"auto","created_at":"2026-02-05 10:28:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1098567,"visible":true,"origin":"","legend":"","description":"","filename":"SatelliteBasedEstimationandLongTermTrendsofPM.inSouthernNepalManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8733880/v1_covered_c171eb95-c3d8-4858-88ea-ba69da72e934.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Satellite-Based Estimation and Long-Term Trends of PM₂.₅ in Southern Nepal Using AOD and Meteorological Reanalysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"PM₂.₅, Aerosol Optical Depth, Random Forest, Spatiotemporal trends, Southern Nepal","lastPublishedDoi":"10.21203/rs.3.rs-8733880/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8733880/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFine particulate matter (PM₂.₅) poses a significant threat to public health and environmental quality in Nepal\u0026rsquo;s Tarai and Dune Valley regions. This study characterizes the spatiotemporal variability of PM₂.₅ from 2019 to 2021 by integrating ground-based observations, satellite-derived Aerosol Optical Depth (AOD), gaseous pollutants, and meteorological data. Daily PM₂.₅ measurements from six monitoring stations were analyzed alongside MODIS AOD, TROPOMI CO, NO₂, and SO₂ data, and ERA5 meteorological variables including temperature, relative humidity, and wind components. Correlation and regression analyses revealed strong associations between PM₂.₅ and AOD, CO, and temperature, while relative humidity and wind components exhibited moderate effects. Single linear regression explained 34\u0026ndash;38% of PM₂.₅ variability, whereas multiple regression and Random Forest models improved predictive performance (R\u0026sup2; \u0026asymp; 0.54\u0026ndash;0.65, RMSE\u0026thinsp;\u0026asymp;\u0026thinsp;22\u0026ndash;26 \u0026micro;g/m\u0026sup3;), accurately capturing seasonal and regional differences. Simulated PM₂.₅ concentrations reconstructed long-term trends (2000\u0026ndash;2023), highlighting increasing levels in eastern regions (Jhumka) and relatively stable or declining trends in western regions (Dang, Bhimdatta). Seasonal analysis showed the highest concentrations during winter and pre-monsoon periods, and substantial reductions during monsoon months due to rainfall-driven pollutant washout. The results underscore the importance of integrating satellite and ground-based data with statistical modeling to assess historical air quality, identify pollution hotspots, and inform evidence-based mitigation strategies. 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