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
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Date
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Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
Please 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.
Description
DATA AVAILABILITY : Data will be made available on request.
Keywords
Air pollution, Respiratory disease, Cardiovascular disease (CVD), Machine learning, Clustering, Air pollution epidemiology, Adverse health effects, South Africa (SA)
Sustainable Development Goals
SDG-03: Good health and well-being
SDG-11: Sustainable cities and communities
SDG-11: Sustainable cities and communities
Citation
Mwase, 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.