| 31 Aug 2026
Constraining the Effects of Anthropogenic Emissions and Meteorological Variabilities on summertime PM2.5 and surface ozone changes over eastern China during 2015–2024
Abstract. Severe fine particulate matter (PM2.5) and ozone pollution pose major air quality concerns in China. Chemical transport models (CTMs) are widely used to attribute their long-term trends to anthropogenic emissions and meteorological variabilities, but inherent uncertainties often yield inconsistent results across models. Here, we develop a bias correction framework that integrates a multi-layer perceptron (MLP) neural network to jointly constrain PM2.5 and ozone attributions simulated by two independent CTMs, CMAQ and GEOS-Chem, over eastern China during 2015–2024. This framework improves simulated pollutant concentrations, spatial distributions, and interannual variabilities, providing a robust foundation for attribution analysis. During 2015–2024, anthropogenic emission changes accounted for 69–83 % of the observed PM2.5 decline and 56–63 % of ozone changes across both corrected models, with meteorological variabilities contributing the remainder. Emission changes dominate the PM2.5 decreases in 2015–2019, but the benefits weaken during 2019–2024 despite continued declines in major PM2.5 precursors. For ozone, anthropogenic emissions drive increases during 2015–2019, but become effective for mitigating ozone thereafter. Meteorological variabilities exacerbate ozone increase in 2015–2019 but exert smaller impacts in later years. Crucially, the MLP correction reduces the inter-model attribution discrepancies from 136 % to 14 % for PM2.5 and from 19 % to 8 % for ozone. These findings confirm the dominant role of emission changes in decadal (2015–2024) trends in summertime PM2.5 and ozone in China, and underscore that sustained, coordinated emission reductions remain essential for mitigating both pollutants. Furthermore, they highlight the value of coupling state-of-the-art, mechanism-based CTMs with machine learning to reliably attribute long-term air quality trends.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.
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