A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information

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A machine learning method to estimate PM 2.5 concentrations across China with remote sensing, meteorological and land use information

Background: Machine learning algorithms have very high predictive ability. However, no study has used machine learning to estimate historical concentrations of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) at daily time scale in China at a national level. Objectives: To estimate dai...

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Bibliographic Details
Main Authors:	Chen, Gongbo, Li, Shanshan, Knibbs, Luke D., Hamm, Nicholas A.S., Cao, Wei, Li, Tiantian, Guo, Jianping, Ren, Hongyan, Abramson, Michael J., Guo, Yuming
Format:	Article
Published:	Elsevier 2018
Subjects:	PM2.5 Aerosol optical depth Random forests Machine learning China
Online Access:	https://eprints.nottingham.ac.uk/53028/

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