Gaussian Process Models for Low Cost Air Quality Monitoring

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dc.contributor.authorSmith, Michael T.
dc.contributor.authorSsematimba, Joel
dc.contributor.authorAlvarez, Mauricio A.
dc.contributor.authorBainomugisha, Engineer
dc.date.accessioned2023-01-26T20:15:01Z
dc.date.available2023-01-26T20:15:01Z
dc.date.issued2021
dc.description.abstractAir pollution contributes to over three million deaths [1] each year. Kampala has one of the highest concentrations of fine particulate matter (PM 2.5) of any African city [2]. Unfortunately, with the exception of the US Embassy, there is no programme for monitoring air pollution in the city due to the high cost of the equipment required. Hence we know little about its distribution or extent. Lower cost devices do exist, but these do not, on their own, provide the accuracy required for decision makers. We propose that using a coregionalised Gaussian process to combine the low cost sensors with the embassy’s high quality results provides sufficiently accurate estimates of pollution across the city.en_US
dc.identifier.citationSmith, M. T., Ssematimba, J., Alvarez, M. A., & Bainomugisha, E. Gaussian Process Models for Low Cost Air Quality Monitoring.en_US
dc.identifier.issnhttp://www.michaeltsmith.org.uk/www.michaeltsmith.org.uk/other/manchester_air.pdf
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/7293
dc.language.isoenen_US
dc.publisherUniversity of Makerereen_US
dc.subjectGaussian Process Modelsen_US
dc.subjectLow Costen_US
dc.subjectAir Quality Monitoringen_US
dc.titleGaussian Process Models for Low Cost Air Quality Monitoringen_US
dc.typeArticleen_US
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