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...

पूर्ण विवरण

में बचाया:
ग्रंथसूची विवरण
में प्रकाशित:Environ Int
मुख्य लेखकों: Li, Lianfa, Girguis, Mariam, Lurmann, Frederick, Pavlovic, Nathan, McClure, Crystal, Franklin, Meredith, Wu, Jun, Oman, Luke D., Breton, Carrie, Gilliland, Frank, Habre, Rima
स्वरूप: Artigo
भाषा:Inglês
प्रकाशित: 2020
विषय:
Article
ऑनलाइन पहुंच: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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