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

dc.contributor.authorArowosegbe, Oluwaseyi Olalekan
dc.contributor.authorRoeoesli, Martin
dc.contributor.authorKuenzli, Nino
dc.contributor.authorSaucy, Apolline
dc.contributor.authorAdebayo-Ojo, Temitope Christina
dc.contributor.authorSchwartz, Joel
dc.contributor.authorKebalepile, Moses Mogakolodi
dc.contributor.authorJeebhay, Mohamed Fareed
dc.contributor.authorDalvie, Mohamed Aqiel
dc.contributor.authorDe Hoogh, Kees
dc.date.accessioned2023-06-14T09:57:43Z
dc.date.available2023-06-14T09:57:43Z
dc.date.issued2022-10
dc.descriptionDATA AVAILABILITY : Data will be made available on request.en_US
dc.description.abstractPlease read abstract in the article.en_US
dc.description.departmentEducation Innovationen_US
dc.description.librarianhj2023en_US
dc.description.sponsorshipThis study is part of the Joint South Africa and Swiss Chair in Global Environmental Health (SARChI), funded by the South African National Research Foundation and the Swiss State Secretariat for Education, Research, and Innovation. O.O.A. is a recipient of a Swiss Government Excellence Scholarship.en_US
dc.description.urihttps://www.elsevier.com/locate/envpolen_US
dc.identifier.citationArowosegbe, 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.en_US
dc.identifier.issn0269-7491 (print)
dc.identifier.issn1873-6424 (online)
dc.identifier.other10.1016/j.envpol.2022.119883
dc.identifier.urihttp://hdl.handle.net/2263/91120
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 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/).en_US
dc.subjectEnsemble averagingen_US
dc.subjectParticulate matter (PM2.5)en_US
dc.subjectSatellite observationsen_US
dc.subjectMachine learningen_US
dc.subjectSDG-11: Sustainable cities and communitiesen_US
dc.titleEnsemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africaen_US
dc.typeArticleen_US

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