Exploring regional air pollution transition dynamics: A multi-state markov model approach

bracu.type.groupResearch Publications
datacite.rightsOpen Access
dc.contributor.authorHossain, Md. Ismail
dc.contributor.authorSarkar, Shuvongkar
dc.contributor.authorMethun, Md. Injamul Haq
dc.contributor.authorRahman, Azizur
dc.contributor.departmentDepartment of Mathematics and Natural Sciences
dc.date.accessioned2026-08-20T09:52:28Z
dc.date.available2026-08-20T09:52:28Z
dc.date.issued2025-10-01
dc.description.abstractBackground Air pollution, commonly measured by the Air Quality Index (AQI), is a significant global health risk, yet its transition dynamics remain poorly understood. This study aims to investigate the regional air pollution transition dynamics across different air quality states. Materials and methods We analyzed weekly average Air Quality Index (AQI) data from January to September 2024 for 19 countries across Asia, Africa, and Europe, collected from an open-access air quality monitoring platform. According to international standards, AQI was categorized into three states (Good, Unhealthy, Very Unhealthy). We applied a multi-state Markov model to assess weekly transitions between these states and estimate the average time spent in one state before transition. Results Findings indicate that in Asia and Africa, air quality tends to deteriorate more frequently than it improves, with low transition rates from “Very Unhealthy” to better states. Transitions from Unhealthy to Good were less frequent in Asia (HR: 0.09, 95% CI: 0.04, 0.19) and Africa (HR:0.25, 95% CI: 0.11, 0.55) compared to Europe, where air quality showed more stability and improvement. The Good and Unhealthy states in Asia had similar sojourn times of 6.80 (±1.77) and 6.64 (±1.38) weeks, while the Very Unhealthy state lasted 3.36 (±0.98) weeks. The Very Unhealthy state persisted for 0.95 (±0.48) weeks in Africa. Europe maintained the “Good” state longest at 7.68 (±1.98) weeks, with shorter durations for Unhealthy and Very Unhealthy states. Conclusion The study highlights lengthy pollution incidents in Asia and Africa, while Europe demonstrates effective pollution control. These insights can guide policymakers in formulating strategies to mitigate pollution based on regional AQI transition trends. © 2025 Hossain et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.description.versionPublished
dc.format.extent12 pages
dc.identifier.citationHossain MI, Sarkar S, Methun MIH, Rahman A (2025) Exploring regional air pollution transition dynamics: A multi-state markov model approach. PLoS One 20(10): e0333849. https://doi.org/10.1371/journal.pone.0333849
dc.identifier.doi10.1371/journal.pone.0333849
dc.identifier.other2-s2.0-105017932355
dc.identifier.urihttps://hdl.handle.net/10361/29388
dc.language.isoen_US
dc.publisherPublic Library of Science
dc.relation.hasversion10.1371/journal.pone.0333849
dc.relation.ispartofPlos One
dc.relation.ispartofseriesPlos One
dc.relation.journalPLOS ONE
dc.relation.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0333849
dc.rightstrue
dc.subjectAfrica
dc.subjectAir pollutants
dc.subjectAir pollution
dc.subjectAsia
dc.subjectEnvironmental monitoring
dc.subjectEurope
dc.subjectHumans
dc.subjectMarkov chains
dc.subject.lcshAir quality.
dc.subject.lcshAir--Pollution.
dc.subject.lcshMarkov processes.
dc.subject.lcshEnvironmental monitoring--Statistical methods.
dc.subject.lcshAtmospheric diffusion--Mathematical models.
dc.titleExploring regional air pollution transition dynamics: A multi-state markov model approach
dc.typeArticle
oaire.citation.issue10 October
oaire.citation.volume20
person.affiliation.nameBRAC University
person.affiliation.nameBRAC University
person.affiliation.nameBangladesh Institute of Governance and Management
person.affiliation.nameJahangirnagar University
person.identifier.orcid0000-0002-5645-2095
person.identifier.orcid0009-0000-6733-2235
person.identifier.scopus-author-id59727393600
person.identifier.scopus-author-id58276680100
person.identifier.scopus-author-id57219361658
person.identifier.scopus-author-id57201439081

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