Research Data Leeds Repository

Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation

Conibear, Luke and Reddington, Carly L. and Silver, Ben J. and Chen, Ying and Knote, Christoph and Arnold, Stephen R. and Spracklen, Dominick V. (2022) Supplementary Data: Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China using Machine Learning Emulation. University of Leeds. [Dataset] https://doi.org/10.5518/1055

Dataset description

The trained emulators per grid cell in China that support the findings of this study. The emulators predict ambient fine particulate matter (PM2.5) and ozone (O3) concentrations from emission changes in five anthropogenic sectors. The README.txt file explains how to open and use these emulators.

Keywords: Air pollution, Public health, Air Quality, Emulator, Machine learning, China, Particulate Matter, Health Impact Assessment, Emissions, Gaussian Process
Divisions: Faculty of Environment > School of Earth and Environment
Related resources:
LocationType
https://doi.org/10.1029/2021GH000570Publication
https://eprints.whiterose.ac.uk/187346/Publication
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited: 05 May 2022 15:21
URI: http://archive.researchdata.leeds.ac.uk/id/eprint/957

Files

Documentation

Data

Research Data Leeds Repository is powered by EPrints
Copyright © 2022 University of Leeds