Using Machine Learning to Estimate Global PM(2.5) for Environmental Health Studies

With the increasing awareness of health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground-level airborne particulate matter (PM(2.5)). Here we use a suite of remote sensing and meteorological data products toget...

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Detalles Bibliográficos
Autores principales: Lary, D. J., Lary, T., Sattler, B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Libertas Academica 2015
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431482/
https://www.ncbi.nlm.nih.gov/pubmed/26005352
http://dx.doi.org/10.4137/EHI.S15664

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4431482/
https://www.ncbi.nlm.nih.gov/pubmed/26005352
http://dx.doi.org/10.4137/EHI.S15664

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