Cargando…
Insights Into the Morphology of the East Asia PM(2.5) Annual Cycle Provided by Machine Learning
The abundance of airborne particulate matter with an aerodynamic equivalent diameter of 2.5 µm or less (PM(2.5)) is a significant environmental and health issue. Many tools have been used to examine the relationship between PM(2.5) abundance and meteorological variables, but some of the relationship...
Autores principales: | Wu, Daji, Zewdie, Gebreab K, Liu, Xun, Kneen, Melanie Anne, Lary, David John |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5392107/ https://www.ncbi.nlm.nih.gov/pubmed/28469447 http://dx.doi.org/10.1177/1178630217699611 |
Ejemplares similares
-
Using machine learning to estimate atmospheric Ambrosia pollen concentrations in Tulsa, OK
por: Liu, Xun, et al.
Publicado: (2017) -
Using Machine Learning to Estimate Global PM(2.5) for Environmental Health Studies
por: Lary, D. J., et al.
Publicado: (2015) -
Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen
por: Zewdie, Gebreab K., et al.
Publicado: (2019) -
An Assessment of Annual Mortality Attributable to Ambient PM(2.5) in Bangkok, Thailand
por: Fold, Nathaniel R., et al.
Publicado: (2020) -
Longitudinal Trends of the Annual Exposure to PM(2.5) Particles in European Countries
por: Alikhani Faradonbeh, Mahdiyeh, et al.
Publicado: (2021)