Ensemble-based deep learning for estimating PM2.5 over California with multisource big data including wildfire smoke

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...

Full description

Bibliographic Details
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
Format: Article
Language:English
Published: Elsevier 2020-12-01
Series:Environment International
Subjects:
PM2.5
Machine learning
Air pollution exposure
Wildfires
Remote sensing
California
Online Access:http://www.sciencedirect.com/science/article/pii/S0160412020320985

Internet

http://www.sciencedirect.com/science/article/pii/S0160412020320985

Similar Items