Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africa

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dc.contributor.authorArowosegbe, Oluwaseyi Olalekan
dc.contributor.authorRöösli, Martin
dc.contributor.authorKünzli, Nino
dc.contributor.authorSaucy, Apolline
dc.contributor.authorAdebayo-Ojo, Temitope Christina
dc.contributor.authorJeebhay, Mohamed F.
dc.contributor.authorDalvie, Mohammed Aqiel
dc.contributor.authorde Hoogh, Kees
dc.date.accessioned2021-10-15T07:22:27Z
dc.date.available2021-10-15T07:22:27Z
dc.date.issued2021-03-24
dc.date.updated2021-04-09T13:47:34Z
dc.description.abstractGood quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM<sub>10</sub>) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM<sub>10</sub> concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM<sub>10</sub> data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM<sub>10</sub> concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM<sub>10</sub> concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.en_US
dc.identifier10.3390/ijerph18073374
dc.identifier.citationInternational Journal of Environmental Research and Public Health 18 (7): 3374 (2021)
dc.identifier.citationInternational Journal of Environmental Research and Public Health 18 (7): 3374 (2021)
dc.identifier.urihttp://hdl.handle.net/11427/35257
dc.language.isoenen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceInternational Journal of Environmental Research and Public Healthen_US
dc.source.journalissue7en_US
dc.source.journalvolume18en_US
dc.source.pagination3374en_US
dc.source.urihttps://www.mdpi.com/journal/ijerph
dc.titleComparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010–2017 in South Africaen_US
dc.typeJournal Articleen_US
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