Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation-Reference-Cited by-同舟云学术

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

Published:2022-06 Issue:6 Volume:6 Page:
ISSN:2471-1403
Container-title:GeoHealth
language:en
Short-container-title:GeoHealth

Author:

Conibear Luke¹^ORCID,Reddington Carly L.¹^ORCID,Silver Ben J.¹^ORCID,Chen Ying²^ORCID,Knote Christoph³^ORCID,Arnold Stephen R.¹^ORCID,Spracklen Dominick V.¹^ORCID

Affiliation:

1. School of Earth and Environment Institute for Climate and Atmospheric Science University of Leeds Leeds UK

2. College of Engineering Mathematics and Physical Sciences University of Exeter Exeter UK

3. Faculty of Medicine University of Augsburg Augsburg Germany

Funder

Natural Environment Research Council

Publisher

American Geophysical Union (AGU)

Subject

Health, Toxicology and Mutagenesis,Management, Monitoring, Policy and Law,Public Health, Environmental and Occupational Health,Pollution,Waste Management and Disposal,Water Science and Technology,Epidemiology,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021GH000570

Reference137 articles.

1. Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model

2. Emulation and Sensitivity Analysis of the Community Multiscale Air Quality Model for a UK Ozone Pollution Episode

3. Towards Physically-Consistent, Data-Driven Models of Convection

4. Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land

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