Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023)

doi:10.21203/rs.3.rs-8123019/v1

Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023)

2026 · doi:10.21203/rs.3.rs-8123019/v1

preprint OA: closed

Full text JSON View at publisher

Abstract

Abstract This study provides a comprehensive assessment of air quality variability in Dhaka, Bangladesh, from 2017 to 2023, with emphasis on the pre-lockdown, lockdown, and post-lockdown phases of the COVID-19 pandemic. Long-term records of major pollutants (PM\((_{2.5})\), PM\((_{10})\), NO\((_2)\), CO, and O\((_3)\)) from the Department of Environment (DoE) were integrated with meteorological data from the Bangladesh Meteorological Department (BMD) to evaluate the combined impacts of anthropogenic activity and atmospheric conditions. Descriptive statistics, trend analysis, and multiple linear regression (MLR) models were applied to characterize pollutant dynamics. The lockdown period exhibited substantial reductions in NO\((_2)\) (50.26%), PM\((_{2.5})\) (35.04%), PM\((_{10})\) (27.99%), and CO (50.23%), attributed primarily to decreased vehicular, industrial, and construction emissions. Conversely, O\((_3)\) increased by 14.55% due to weakened NO titration and enhanced photochemistry. Post-lockdown increases across pollutants indicated that air-quality improvements were temporary. Seasonal analysis identified winter as the most polluted period, with particulate concentrations nearly three times higher than during the monsoon. Regression models showed strong meteorological influence, with temperature exerting pronounced negative effects on PM\((_{2.5})\) (\((\beta = -9.181)\)) and PM\((_{10})\) (\((\beta = -10.415)\)). The strengthened NO\((_2)\)--traffic correlation post-lockdown (\((r = 0.664)\)) underscores vehicular emissions as a dominant source. These findings highlight the need for meteorologically informed, data-driven strategies to manage air quality in rapidly urbanizing megacities.

Full text 14,304 characters · extracted from preprint-html · click to expand

Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023) | 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 Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023) Golam Kibria, Shuaib Ibne Salam, MD Emon Miah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8123019/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 This study provides a comprehensive assessment of air quality variability in Dhaka, Bangladesh, from 2017 to 2023, with emphasis on the pre-lockdown, lockdown, and post-lockdown phases of the COVID-19 pandemic. Long-term records of major pollutants (PM \((_{2.5})\) , PM \((_{10})\) , NO \((_2)\) , CO, and O \((_3)\) ) from the Department of Environment (DoE) were integrated with meteorological data from the Bangladesh Meteorological Department (BMD) to evaluate the combined impacts of anthropogenic activity and atmospheric conditions. Descriptive statistics, trend analysis, and multiple linear regression (MLR) models were applied to characterize pollutant dynamics. The lockdown period exhibited substantial reductions in NO \((_2)\) (50.26%), PM \((_{2.5})\) (35.04%), PM \((_{10})\) (27.99%), and CO (50.23%), attributed primarily to decreased vehicular, industrial, and construction emissions. Conversely, O \((_3)\) increased by 14.55% due to weakened NO titration and enhanced photochemistry. Post-lockdown increases across pollutants indicated that air-quality improvements were temporary. Seasonal analysis identified winter as the most polluted period, with particulate concentrations nearly three times higher than during the monsoon. Regression models showed strong meteorological influence, with temperature exerting pronounced negative effects on PM \((_{2.5})\) ( \((\beta = -9.181)\) ) and PM \((_{10})\) ( \((\beta = -10.415)\) ). The strengthened NO \((_2)\) --traffic correlation post-lockdown ( \((r = 0.664)\) ) underscores vehicular emissions as a dominant source. These findings highlight the need for meteorologically informed, data-driven strategies to manage air quality in rapidly urbanizing megacities. Air quality COVID-19 lockdown Meteorological influence Multiple linear regression (MLR) Seasonal variation Traffic emissions Urban air pollution management. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Dec, 2025 Editor assigned by journal 29 Nov, 2025 Submission checks completed at journal 29 Nov, 2025 First submitted to journal 15 Nov, 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. 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-8123019","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554951362,"identity":"0122af85-69ce-49eb-b5ed-642d7ea9dc07","order_by":0,"name":"Golam Kibria","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACCWYeBoYEhhoexv7G5gcfgCJs7MRpOSbDPOPwMcMZIC3MhLQw8IAoZhv2hrQEaQibgBbJdt5jHx7UsPHwNpwxMLb5tU2ej5mB8cPHHNxapJn5kmckHJPhkWzuMXic23fbsI2ZgVly5jbcWuSYeYwZEtjYeAxBtuT23GYEamFj5iWo5R8zj/2BHANpy57b9gS1SIO0JLYx8zCCvM/w43YiQS2SzXzJDIl9x3gYQYHc23A7uY2ZsRmvXyTOnz3M+ONbjT04Kn/8uW07v7354IePeLSgAsY2MNlArHoQ+EOK4lEwCkbBKBgpAADxc0u6CZGZZQAAAABJRU5ErkJggg==","orcid":"","institution":"Ahsania Mission University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Golam","middleName":"","lastName":"Kibria","suffix":""},{"id":554951363,"identity":"4ec5d3d5-2c1c-4b55-810d-4d8c42d3628d","order_by":1,"name":"Shuaib Ibne Salam","email":"","orcid":"","institution":"Rajshahi University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuaib","middleName":"Ibne","lastName":"Salam","suffix":""},{"id":554951364,"identity":"0b1e41ac-95ad-40e6-b803-31a68c4ec4d8","order_by":2,"name":"MD Emon Miah","email":"","orcid":"","institution":"Rajshahi University of Engineering \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"MD","middleName":"Emon","lastName":"Miah","suffix":""}],"badges":[],"createdAt":"2025-11-15 15:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8123019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8123019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102295637,"identity":"7b020ff5-feec-433e-98a3-8bd298c5a412","added_by":"auto","created_at":"2026-02-10 10:13:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1515710,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmainmanuscriptwithlinenumbersclinicaltrialstatementfundingstatement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8123019/v1_covered_bc8bc5f6-82f2-4b81-b790-fb6d22ed3153.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023)","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":"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":"Air quality, COVID-19 lockdown, Meteorological influence, Multiple linear regression (MLR), Seasonal variation, Traffic emissions, Urban air pollution management.","lastPublishedDoi":"10.21203/rs.3.rs-8123019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8123019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study provides a comprehensive assessment of air quality variability in Dhaka, Bangladesh, from 2017 to 2023, with emphasis on the pre-lockdown, lockdown, and post-lockdown phases of the COVID-19 pandemic. Long-term records of major pollutants (PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{2.5})\\)\u003c/span\u003e\u003c/span\u003e, PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{10})\\)\u003c/span\u003e\u003c/span\u003e, NO\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_2)\\)\u003c/span\u003e\u003c/span\u003e, CO, and O\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_3)\\)\u003c/span\u003e\u003c/span\u003e) from the Department of Environment (DoE) were integrated with meteorological data from the Bangladesh Meteorological Department (BMD) to evaluate the combined impacts of anthropogenic activity and atmospheric conditions. Descriptive statistics, trend analysis, and multiple linear regression (MLR) models were applied to characterize pollutant dynamics. The lockdown period exhibited substantial reductions in NO\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_2)\\)\u003c/span\u003e\u003c/span\u003e (50.26%), PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{2.5})\\)\u003c/span\u003e\u003c/span\u003e (35.04%), PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{10})\\)\u003c/span\u003e\u003c/span\u003e (27.99%), and CO (50.23%), attributed primarily to decreased vehicular, industrial, and construction emissions. Conversely, O\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_3)\\)\u003c/span\u003e\u003c/span\u003e increased by 14.55% due to weakened NO titration and enhanced photochemistry. Post-lockdown increases across pollutants indicated that air-quality improvements were temporary. Seasonal analysis identified winter as the most polluted period, with particulate concentrations nearly three times higher than during the monsoon. Regression models showed strong meteorological influence, with temperature exerting pronounced negative effects on PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{2.5})\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\beta = -9.181)\\)\u003c/span\u003e\u003c/span\u003e) and PM\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_{10})\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\\beta = -10.415)\\)\u003c/span\u003e\u003c/span\u003e). The strengthened NO\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((_2)\\)\u003c/span\u003e\u003c/span\u003e--traffic correlation post-lockdown (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((r = 0.664)\\)\u003c/span\u003e\u003c/span\u003e) underscores vehicular emissions as a dominant source. These findings highlight the need for meteorologically informed, data-driven strategies to manage air quality in rapidly urbanizing megacities.\u003c/p\u003e","manuscriptTitle":"Meteorological Adjustment and Regression Modeling of Air Pollutants in Dhaka, Bangladesh: Insights from Pre-, During-, and Post-Lockdown Periods (2017–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 17:10:18","doi":"10.21203/rs.3.rs-8123019/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-04T01:09:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-29T08:03:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-29T08:01:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-11-15T15:23:22+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"3f3b21f7-dc91-4c83-b385-0244fda4fd0a","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T12:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 17:10:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8123019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8123019","identity":"rs-8123019","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: preprint-html ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2026) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc: last seen: 2026-05-20T01:45:00.602351+00:00