Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical <i>a Priori</i> Information in Deep Learning-Reference-Cited by-同舟云学术

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Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Published:2024-03-27 Issue:5 Volume:1 Page:332-345
ISSN:2837-1402
Container-title:ACS ES&T Air
language:en
Short-container-title:ACS EST Air

Author:

Shen Siyuan¹^ORCID,Li Chi¹^ORCID,van Donkelaar Aaron¹^ORCID,Jacobs Nathan²,Wang Chenguang²,Martin Randall V.¹²

Affiliation:

1. Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States

2. Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States

Funder

National Institute of Environmental Health Sciences

Central Washington University

National Aeronautics and Space Administration

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsestair.3c00054

Reference51 articles.

1. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

2. Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019

3. No one knows which city has the highest concentration of fine particulate matter

4. Data Integration for the Assessment of Population Exposure to Ambient Air Pollution for Global Burden of Disease Assessment

5. Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

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