High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.

Autor: Hu M; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China., Lu X; Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China. Electronic address: xingchenglu2011@gmail.com., Chen Y; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China., Chen W; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China., Guo C; Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, China., Xian C; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China., Fung JCH; Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China; Division of Environment and Sustainability, The Hong Kong University of Science and Technology, Hong Kong, China.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Dec; Vol. 371, pp. 123096. Date of Electronic Publication: 2024 Nov 01.
DOI: 10.1016/j.jenvman.2024.123096
Abstrakt: Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O 3 ). This study aims to elucidate global CO pollution in relation to health risks and the influence of natural events like wildfires. Utilizing artificial intelligence (AI) big data techniques, we developed a high-performance Convolutional Neural Network (CNN)-based Residual Network (ResNet) model to estimate daily global CO concentrations at a high spatial resolution of 0.07° from June 2018 to May 2021. Our model integrated the global TROPOMI Total Column of atmospheric CO (TCCO) product and reanalysis datasets, achieving desirable estimation accuracies with R-values (correlation coefficients) of 0.90 and 0.96 for daily and monthly predictions, respectively. The analysis reveals that the CO concentrations were relatively high in northern and central China, as well as northern India, particularly during winter months. Given the significant role of wildfires in increasing surface CO levels, we examined their impact in the Indochina Peninsula, the Amazon Rain Forest, and Central Africa. Our results show increases of 60.0%, 28.7%, and 40.8% in CO concentrations for these regions during wildfire seasons, respectively. Additionally, we estimated short-term mortality cases related to CO exposure in 17 countries for 2019, with China having the highest mortality cases of 23,400 (95% confidence interval: 0-99,500). Our findings highlight the critical need for ongoing monitoring of CO levels and their health implications. The daily surface CO concentration dataset is publicly available and can support future relevant sustainable studies, which is accessible at https://doi.org/10.5281/zenodo.11806178.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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