The stratospheric contribution to tropospheric ozone has long been a topic of much debate over the past few decades. In this study, we leveraged multiple datasets from surface, sounding and satellite observations to reanalysis datasets, along with a global chemical transport model (Global Nested Air Quality Prediction Modelling System, GNAQPMS) to investigate the impact of a stratospheric-to-tropospheric transport (STT) event characterized by long duration and wide range in the summer on surface high ozone episodes over heavily industrialized regions in northern China. In August 14-18, 2019, the ERA5 reanalysis datasets showed a PV tongue and a deep, upper-level trough penetrate towards 35oN over the North China Plain (NCP), indicating the occurrence of a stratospheric intrusion. From Atmospheric Infrared Sounder (AIRS) measurements, we found that the ozone-rich, stratospheric air mass had been injected into the lower altitudes. The GNAQPMS generally captured the featured layers, although there was a slight underestimation in the low troposphere. The averaged magnitudes of stratospheric contribution (O3S) and percentage (O3F) simulated by GNAQPMS were 3-20 ug/m3 and 6%-20%, respectively, while the Whole Atmosphere Community Climate Model (WACCM) indicated a higher stratospheric contribution by 3-5 ug/m3. Through this study, we give our opinions on the controversial topic of a more thorough understanding of the influence of natural processes apart from anthropogenic emissions, even in a heavily polluted region during summer.

The global atmospheric chemical transport model has become a key technology for air quality forecast and management. However, precise and rapid air quality simulations and forecast are frequently limited by the model’s computational performance. The gas-phase chemistry module is the most time-consuming module in air quality models because its traditional solution method is dynamically stiff. To reduce the solving time of the gas phase chemical module, we built an emulator based on a deep residual neural network emulator (NN) for Carbon Bond Mechanism Z (CBM-Z) mechanism implemented in Global Nested Air Quality Prediction Modeling System. A global high resolution cross-life multi-species dataset was built and trained to evaluate multi-species concentration changes at a single time step of CBM-Z. The results showed that the emulator could accelerate to approximately 300–750 times while maintaining an accuracy similar to that of CBM-Z module (the average correlation coefficient squared was 0.97) at the global scale. This deep learning-based emulator could adequately represent the stiff kinetics of CBM-Z, which involves 47 species and 132 reactions. The emulated ozone (O3), nitrogen oxides (NOx), and hydroxyl radical (OH) were consistent with those of the original CBM-Z module in different global regions, heights, and time. Our results suggest that data-driven emulations have great potential in the construction of hybrid models with process-based air quality models, particularly at larger scales.