Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation

Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation

Abstract Machine learning models can emulate chemical transport models, reducing computational costs and enabling more experimentation. We developed emulators to predict annual−mean fine particulate matter (PM2.5) and ozone (O3) concentrations and their associated chronic health impacts from changes...

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Bibliographic Details
Main Authors: Luke Conibear, Carly L. Reddington, Ben J. Silver, Ying Chen, Christoph Knote, Stephen R. Arnold, Dominick V. Spracklen
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2022-06-01
Series:GeoHealth
Subjects:
emulator
machine learning
air quality
health impact assessment
China
particulate matter
Online Access:https://doi.org/10.1029/2021GH000570

Internet

https://doi.org/10.1029/2021GH000570

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