DataSheet1_Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case.docx

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.

全球大气化学传输模型已成为空气质量预报与管理的关键技术。然而，精确且快速的空气质量模拟与预报往往受到模型计算性能的限制。气相化学模块是空气质量模型中最耗时的模块，因为其传统的解法方法具有动态刚性。为缩短气相化学模块的求解时间，我们基于深度残差神经网络仿真器（NN）构建了用于全球嵌套空气质量预测建模系统中的碳键机制Z（CBM-Z）机制的仿真器。构建并训练了一个全球高分辨率跨生命周期多物种数据集，用于评估CBM-Z单时间步长内的多物种浓度变化。结果显示，该仿真器可以将速度提升至约300至750倍，同时在全球范围内保持与CBM-Z模块相似的精度（平均相关系数平方为0.97）。基于深度学习的此仿真器能够充分表征CBM-Z的刚性动力学，涉及47种物种和132个反应。仿真的臭氧（O3）、氮氧化物（NOx）和羟基自由基（OH）在不同全球区域、高度和时间上与原始CBM-Z模块的结果一致。我们的研究结果表明，数据驱动仿真在构建基于过程的空气质量模型与混合模型构建中具有巨大的潜力，尤其是在更大尺度上。