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

Ozone (Oinline-formula₃) is a secondary pollutant in the atmosphere formed by photochemical reactions that endangers human health and ecosystems. Oinline-formula₃ 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 Oinline-formula₃ pollution, near-surface Oinline-formula₃ 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 Oinline-formula₃ data from a global atmospheric chemical transport model and real-time observations. The ML model is then used to estimate future Oinline-formula₃ with meteorological fields from multi-model simulations under various climate scenarios. The near-surface Oinline-formula₃ 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 Oinline-formula₃ increases are primarily owing to the favorable meteorological conditions for Oinline-formula₃ photochemical formation in most Asian regions. We also find that the summertime Oinline-formula₃ 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 Oinline-formula₃ in the future, which provides implications for environmental and climate strategies of adaptation and mitigation.