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

Conibear, L orcid.org/0000-0003-2801-8862, Reddington, CL orcid.org/0000-0002-5990-4966, Silver, BJ orcid.org/0000-0003-0395-0637 et al. (4 more authors) (2022) Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation. GeoHealth, 6 (6). e2021GH000570. ISSN 2471-1403

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: emulator; machine learning; air quality; health impact assessment; China; particulate matter
Dates:
  • Accepted: 16 May 2022
  • Published (online): 6 June 2022
  • Published: June 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 27 May 2022 10:31
Last Modified: 27 Jul 2022 09:12
Status: Published
Publisher: American Geophysical Union
Identification Number: https://doi.org/10.1029/2021GH000570
Related URLs:

Share / Export

Statistics