Unsupervised machine learning to investigate the joint effects of SO2, NO2, O3, PM2.5 and PM10 on respiratory and cardiovascular hospital admissions in the Vaal Triangle Airshed Priority Area, South Africa

dc.contributor.authorMwase, Nandi Sisasenkosi
dc.contributor.authorKebalepile, Moses
dc.contributor.authorJunger, Washington
dc.contributor.authorWichmann, Janine
dc.date.accessioned2026-02-05T05:07:13Z
dc.date.issued2026-01
dc.descriptionDATA AVAILABILITY : Data will be made available on request.
dc.description.abstractPlease read abstract in the article. HIGHLIGHTS • Unsupervised machine learning can be used as a dimension-reduction tool in air epidemiology. • Clustering methods allow to investigate multiple air pollutants (5≤) effects on hospital admissions. • There are noticeable limitations in using unsupervised machine learning in air pollution epidemiology studies.
dc.description.departmentSchool of Health Systems and Public Health (SHSPH)
dc.description.embargo2026-11-25
dc.description.librarianhj2026
dc.description.sdgSDG-03: Good health and well-being
dc.description.sdgSDG-11: Sustainable cities and communities
dc.description.urihttp://www.elsevier.com/locate/atmosenv
dc.identifier.citationMwase, N.S., Kebalepile, M., Junger, W. & Wichmann, J. 2026, 'Unsupervised machine learning to investigate the joint effects of SO2, NO2, O3, PM2.5 and PM10 on respiratory and cardiovascular hospital admissions in the Vaal Triangle Airshed Priority Area, South Africa', Atmospheric Environment, vol. 366, art. 121660, pp. 1-10, doi : 10.1016/j.atmosenv.2025.121660.
dc.identifier.issn1352-2310 (print)
dc.identifier.issn1873-2844 (online)
dc.identifier.other10.1016/j.atmosenv.2025.121660
dc.identifier.urihttp://hdl.handle.net/2263/107848
dc.language.isoen
dc.publisherElsevier
dc.rights© 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Notice : this is the author’s version of a work that was accepted for publication in Atmospheric Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Atmospheric Environment, vol. 366, art. 121660, pp. 1-10, 2026, doi : 10.1016/j.atmosenv.2025.121660.
dc.subjectAir pollution
dc.subjectRespiratory disease
dc.subjectCardiovascular disease (CVD)
dc.subjectMachine learning
dc.subjectClustering
dc.subjectAir pollution epidemiology
dc.subjectAdverse health effects
dc.subjectSouth Africa (SA)
dc.titleUnsupervised machine learning to investigate the joint effects of SO2, NO2, O3, PM2.5 and PM10 on respiratory and cardiovascular hospital admissions in the Vaal Triangle Airshed Priority Area, South Africa
dc.typePostprint Article

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