the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 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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Status: open (until 22 Oct 2026)
- RC1: 'Comment on egusphere-2026-4864', Anonymous Referee #1, 23 Sep 2026 reply
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RC2: 'Comment on egusphere-2026-4864', Anonymous Referee #2, 23 Sep 2026
reply
Zeng et al. present a comprehensive source attribution of recent trends in major air pollutants in China. They correct biases in current CTMs using a machine learning method, which seems promising in terms of better predicting concentrations, but several points should be clarified to deliver a more robust message regarding source attribution.
- Why does the analysis only focus on summertime? Winter PM2.5 is an issue of larger concern.
- How do the authors account for the spatial representativeness when comparing grid vs observations? For example, there could be 10 observations within one grid cell where ground sites are densely located, but where the sites are sparsely located, only one station data may be used for comparison with the grid-averaged simulated value.
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Figure 2: What does the shading represent in panel a?
- Table 1: What is the rationale for selecting these input features (particularly the chemical species)?
- Equation 3: How do the authors account for background levels? Ozone has a significant regional background (which may have a temporal trend) over East Asia.
- Line 520: Please describe in more detail the relationship between PM2.5 decline and photolysis of ozone.
- Section 4.3: Please mention that the meteorological variables are from GEOS-FP and WRF in the beginning of the section. Do meteorological observations support the trends shown in the models?
- Section 4.4: The large difference in WRF-Chem and GEOS-Chem before performing MLP correction is surprising, but it is difficult to agree that reduced inter-model difference (from MLP correction) provides more robust attribution results. Can the authors provide a better reason why the bias-corrected model results give more accurate predictions on source attribution?
Citation: https://doi.org/10.5194/egusphere-2026-4864-RC2
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Ganquan Zeng
Xiang Weng
Haofan Wang
Haolin Wang
Guowen He
We develop a novel, dual-CTM bias correction framework to attribute summertime PM2.5 and ozone changes over eastern China during 2015–2024. The framework substantially reduces the CTM biases and reconciles the inter-model discrepancies in the attribution. Emission reductions dominate both the PM2.5 decline and ozone increase, but there is a marked transition of their role after 2019. Persistent unfavorable meteorological conditions contribute to ozone increase especially before 2019.
We develop a novel, dual-CTM bias correction framework to attribute summertime PM2.5 and ozone...
This manuscript develops a dual-CTM, dual-pollutant machine-learning bias-correction framework to attribute summertime PM₂.₅ and MDA8 ozone changes over eastern China during 2015–2024 to anthropogenic emission changes and meteorological variability. The authors apply a multi-output multilayer perceptron (MLP) to correct PM₂.₅ and ozone biases in CMAQ and GEOS-Chem simulations. The trained MLP models are subsequently applied to simulations in which anthropogenic emissions are fixed at the 2015 level. Changes in the corrected fixed-emission simulations are interpreted as meteorological contributions, while the residual between the observed change and the meteorological contribution is attributed to anthropogenic emission changes. The topic is important and timely. The attempt to constrain two structurally different CTMs using a consistent observational and machine-learning framework is potentially useful. I think the paper should be published after addressing the followings.