Ensemble-Based Deep Learning for Estimating PM(2.5) over California with Multisource Big Data Including Wildfire Smoke

INTRODUCTION: Estimating PM(2.5) concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic...

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Detalhes bibliográficos
Publicado no:Environ Int
Main Authors: Li, Lianfa, Girguis, Mariam, Lurmann, Frederick, Pavlovic, Nathan, McClure, Crystal, Franklin, Meredith, Wu, Jun, Oman, Luke D., Breton, Carrie, Gilliland, Frank, Habre, Rima
Formato: Artigo
Idioma:Inglês
Publicado em: 2020
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7643812/
https://ncbi.nlm.nih.gov/pubmed/32980736
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.envint.2020.106143
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