Statistical and Machine Learning Analysis of PM2.5 Concentrations and Meteorological Influences

Statistical and Machine Learning Analysis of PM2.5 Concentrations and Meteorological Influences | 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 Statistical and Machine Learning Analysis of PM2.5 Concentrations and Meteorological Influences Md. iftekharuzzaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8288070/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 Fine particulate matter (PM2.5) poses a significant public health risk in densely populated urban areas like Dhaka, Bangladesh. This study presents a comprehensive analysis of PM2.5 concentrations and their relationship with meteorological variables from 2019 to 2024. We employed a robust methodological framework, beginning with advanced data imputation using a Kalman filter to handle missing values while preserving temporal structure [ 1 ]. A suite of statistical and machine learning models—including Gradient Boosting, Elastic Net regression, Generalized Linear Models ( GLMs ), and an Autoregressive (AR) model—were developed to predict PM2.5 levels and identify key drivers. Our results indicate that while meteorological variables like rainfall and wind speed have statistically significant cleansing effects, they are insufficient for accurate daily PM2.5 prediction when used in isolation, as demonstrated by the low explanatory power (R² ≈ 0) of the machine learning models [ 2 ]. This underscores the complexity of air pollution in Dhaka, suggesting a stronger influence from non-meteorological factors such as transboundary pollution and anthropogenic activities. In contrast, the AR(15) model effectively captured the strong temporal persistence of PM2.5. The selection of this model was validated through Autocorrelation ( ACF) & Partial Autocorrelation ( PACF ) analysis, which revealed strong temporal persistence & informed the optimal lag structure. The study successfully translates these findings into a health risk assessment using WHO Air Quality Index( AQI ) categories, clearly identifying winter as the most polluted season and noting a general improving trend in air quality from 2019 to 2024 [ 3 ], [ 4 ]. This work highlights the limitations of meteorological-based daily forecasting and emphasizes the need for models that integrate a broader range of predictors to effectively inform public health policy. Climate Analysis and Modeling PM2.5 Air Quality Forecasting Machine Learning Time Series Analysis Meteorological Influences Dhaka Health Risk Assessment Autoregressive Model 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-8288070","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555825676,"identity":"d716a35f-1c4d-43b1-ba56-17c9eccc9c25","order_by":0,"name":"Md. 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Influences\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Dhaka","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 Forecasting, Machine Learning, Time Series Analysis, Meteorological Influences, Dhaka, Health Risk Assessment, Autoregressive Model","lastPublishedDoi":"10.21203/rs.3.rs-8288070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8288070/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 public health risk in densely populated urban areas like Dhaka, Bangladesh. This study presents a comprehensive analysis of PM2.5 concentrations and their relationship with meteorological variables from 2019 to 2024. We employed a robust methodological framework, beginning with advanced data imputation using a \u003cb\u003eKalman\u003c/b\u003e filter to handle missing values while preserving temporal structure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A suite of statistical and machine learning models\u0026mdash;including Gradient Boosting, Elastic Net regression, Generalized Linear Models (\u003cb\u003eGLMs\u003c/b\u003e), and an Autoregressive (AR) model\u0026mdash;were developed to predict PM2.5 levels and identify key drivers. Our results indicate that while meteorological variables like rainfall and wind speed have statistically significant cleansing effects, they are insufficient for accurate daily PM2.5 prediction when used in isolation, as demonstrated by the low explanatory power \u003cb\u003e(R\u0026sup2; \u0026asymp; 0)\u003c/b\u003e of the machine learning models [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This underscores the complexity of air pollution in Dhaka, suggesting a stronger influence from non-meteorological factors such as transboundary pollution and anthropogenic activities. In contrast, the \u003cb\u003eAR(15)\u003c/b\u003e model effectively captured the strong temporal persistence of PM2.5. The selection of this model was validated through Autocorrelation (\u003cb\u003eACF)\u003c/b\u003e \u0026amp; Partial Autocorrelation (\u003cb\u003ePACF\u003c/b\u003e) analysis, which revealed strong temporal persistence \u0026amp; informed the optimal lag structure. The study successfully translates these findings into a health risk assessment using WHO Air Quality Index(\u003cb\u003eAQI\u003c/b\u003e) categories, clearly identifying winter as the most polluted season and noting a general improving trend in air quality from 2019 to 2024 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This work highlights the limitations of meteorological-based daily forecasting and emphasizes the need for models that integrate a broader range of predictors to effectively inform public health policy.\u003c/p\u003e","manuscriptTitle":"Statistical and Machine Learning Analysis of PM2.5 Concentrations and Meteorological Influences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 11:07:39","doi":"10.21203/rs.3.rs-8288070/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"5e09d98b-4991-4228-8c3c-48f4f42b7311","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59158114,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2025-12-08T11:07:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 11:07:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8288070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8288070","identity":"rs-8288070","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}