Meteorological influence on surface ozone trends in China: assessing uncertainties caused by multi-dataset and multi-method

作者：Xueqing Wang, Jia Zhu, Guanjie Jiao, Xi Chen, Zhenjiang Yang, Lei Chen, Xipeng Jin, Hong Liao · 发表于：Atmospheric chemistry and physics · 年份：2025 · DOI：10.5194/acp-25-13863-2025 · 被引用次数：11 · 研究领域：Atmospheric chemistry and aerosols、Air Quality Monitoring and Forecasting、Air Quality and Health Impacts

China has witnessed notable increases in surface ozone (O 3 ) concentrations since 2013, with meteorology identified as a critical driver. However, meteorological contributions vary with different meteorological datasets and analytical methods, and their uncertainties remain unassessed. This study leveraged decadal observational maximum daily 8-hour average O 3 records (2013–2022) across China, revealing intensified nationwide O 3 pollution with increasing O 3 trends of 0.79–1.31 ppb yr −1 during four seasons. We gave special focus on uncertainties of meteorology-driven O 3 trends by using diverse meteorological datasets (ERA5, MERRA2, FNL) and diverse analytical methods (Multiple Linear Regression, Random Forest, GEOS-Chem model). A useful statistic (coefficient of variation, CV) was adopted as an uncertainty quantification metric. For multi-dataset analysis, models driven by different meteorological datasets exhibited the maximum meteorology-driven O 3 trend (+0.55 ppb yr −1 , multi-dataset mean) with the highest consistency (CV = 0.25) in spring. The FNL-driven model always obtained larger trends compared to ERA5 and MERRA2, which could be attributed to inability to accurately evaluate planetary boundary layer height in FNL dataset. For multi-method analysis, three methods demonstrated optimal consistency in winter (CV = 0.40) and the worst consistency in summer (CV = 2.00). The meteorology-driven O 3 trends obtained from GEOS-Chem model were almost smaller than those obta...