Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data

Abstract 3) 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Climate-driven deterioration of future ozone pollution in Asia predicted by machine learning with multi-source data ; volume:23 ; number:2 ; year:2023 ; pages:1131-1145 ; extent:15 ; Atmospheric chemistry and physics ; 23, Heft 2 (2023), 1131-1145 (gesamt 15)

Contributors, Places and Time

• Creator: Li, Huimin ; Yang, Yang ; Jin, Jianbing ; Wang, Hailong ; Li, Ke ; Wang, Pinya ; Liao, Hong

Further information

• DOI: 10.5194/acp-23-1131-2023
• URN: urn:nbn:de:101:1-2023033008143529647089
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:56 AM CEST