Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke
Introduction: Estimating PM2.5 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 e...
Main Authors: | Lianfa Li, Mariam Girguis, Frederick Lurmann, Nathan Pavlovic, Crystal McClure, Meredith Franklin, Jun Wu, Luke D. Oman, Carrie Breton, Frank Gilliland, Rima Habre |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2020-12-01
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Series: | Environment International |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S0160412020320985 |
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