Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

Arowosegbe, Oluwaseyi OlalekanRoeoesli, MartinKuenzli, NinoSaucy, ApollineAdebayo-Ojo, Temitope ChristinaSchwartz, JoelKebalepile, Moses MogakolodiJeebhay, Mohamed FareedDalvie, Mohamed AqielDe Hoogh, Kees2023-06-142023-06-142022-10Arowosegbe, O.O., Röösli, M., Künzli, N. et al. 2022, Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa', Environmental Pollution, vol. 310, art. 119883, pp. 1-10, doi : 10.1016/j.envpol.2022.119883.0269-7491 (print)1873-6424 (online)10.1016/j.envpol.2022.119883http://hdl.handle.net/2263/91120DATA AVAILABILITY : Data will be made available on request.Please read abstract in the article.en© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Ensemble averagingParticulate matter (PM2.5)Satellite observationsMachine learningSDG-11: Sustainable cities and communitiesEnsemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South AfricaArticle