Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process‐Based Interpretable Machine Learning Method

Abstract

Abstract Ozone (O3) pollution is a severe air quality issue in China, posing a threat to human health and ecosystems. The climate change will affect O3 levels by directly changing physical and chemical processes of O3 and indirectly changing natural emissions of O3 precursors. In this study, near‐surface O3 concentrations in China in 2030 and 2060 are predicted using the process‐based interpretable Extreme Gradient Boosting (XGBoost) model integrated with multi‐source data. The results show that the climate‐driven O3 levels over eastern China are projected to decrease by more than 0.4 ppb in 2060 under the carbon neutral scenario (SSP1‐1.9) compared with the high emission scenario (SSP5‐8.5). Among this reduction, 80% is attributed to the changes in physical and chemical processes of O3 related to a cooler climate, while the remaining 20% is attributed to the reduced biogenic isoprene emissions.

Keywords

• ozone
• climate change
• machine learning
• GEOS‐Chem
• CMIP6
• carbon neutrality