气候变化领域集成服务门户(CCPortal): Climate-driven deterioration of future ozone pollution in Asia predicted bymachine learning with multi-source data

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DOI	10.5194/acp-23-1131-2023
	Climate-driven deterioration of future ozone pollution in Asia predicted bymachine learning with multi-source data
	Li, Huimin; Yang, Yang; Jin, Jianbing; Wang, Hailong; Li, Ke; Wang, Pinya; Liao, Hong
发表日期	2023
ISSN	1680-7316
EISSN	1680-7324
起始页码	1131
结束页码	1145
卷号	23 期号:2 页码:15
英文摘要	Ozone (O3) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. O3 has aggravated in Asia in recent decades and will vary in the future. In this study, to quantify the impacts of future climate change on O3 pollution, near-surface O3 concentrations over Asia in 2020-2100 are projected using a machine learning (ML) method along with multi-source data. The ML model is trained with combined O3 data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future O3 with meteorological fields from multi-model simulations under various climate scenarios. The near-surface O3 concentrations are projected to increase by 5 %-20 % over South China, Southeast Asia, and South India and less than 10 % over North China and the Gangetic Plains under the high-forcing scenarios in the last decade of 21st century, compared to the first decade of 2020-2100. The O3 increases are primarily owing to the favorable meteorological conditions for O3 photochemical formation in most Asian regions. We also find that the summertime O3 pollution over eastern China will expand from North China to South China and extend into the cold season in a warmer future. Our results demonstrate the important role of a climate change penalty on Asian O3 in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000921371100001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273631
作者单位	Nanjing University of Information Science & Technology; United States Department of Energy (DOE); Pacific Northwest National Laboratory
推荐引用方式 GB/T 7714	Li, Huimin,Yang, Yang,Jin, Jianbing,et al. Climate-driven deterioration of future ozone pollution in Asia predicted bymachine learning with multi-source data[J],2023,23(2):15.
APA	Li, Huimin.,Yang, Yang.,Jin, Jianbing.,Wang, Hailong.,Li, Ke.,...&Liao, Hong.(2023).Climate-driven deterioration of future ozone pollution in Asia predicted bymachine learning with multi-source data.ATMOSPHERIC CHEMISTRY AND PHYSICS,23(2),15.
MLA	Li, Huimin,et al."Climate-driven deterioration of future ozone pollution in Asia predicted bymachine learning with multi-source data".ATMOSPHERIC CHEMISTRY AND PHYSICS 23.2(2023):15.