Xing Jia, Zheng Shuxin, Li Siwei, Huang Lin, Wang Xiaochun, Kelly James T, Wang Shuxiao, Liu Chang, Jang Carey, Zhu Yun, Zhang Jia, Bian Jiang, Liu Tie-Yan, Hao Jiming
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China.
Atmos Res. 2022 Jan;265:1-11. doi: 10.1016/j.atmosres.2021.105919.
Fast and accurate prediction of ambient ozone (O) formed from atmospheric photochemical processes is crucial for designing effective O pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O formation and quantifying the O response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O concentrations are strongly influenced by the initialized O concentration, as well as emission and meteorological factors during daytime when O is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O pollution.
快速准确地预测大气光化学过程中形成的环境臭氧（O）对于在气候变化背景下设计有效的臭氧污染控制策略至关重要。化学传输模型（CTM）是臭氧预测和政策设计的基础工具，然而，现有的基于CTM的方法计算成本高昂，资源负担限制了它们在空气质量管理中的使用和有效性。在此，我们提出了一种新方法（记为DeepCTM），即利用深度学习来模拟CTM模拟，以提高光化学建模的计算效率。经过良好训练的DeepCTM利用前体排放、气象因素和初始条件的输入特征成功再现了CTM模拟的臭氧浓度。DeepCTM的优势在于其在识别臭氧形成的主要贡献因素以及量化臭氧对排放和气象变化的响应方面具有高效率。DeepCTM所隐含的排放 - 气象 - 浓度联系与已知的大气化学机制一致，这表明DeepCTM在科学上也是合理的。DeepCTM在中国的应用表明，臭氧浓度受到初始臭氧浓度以及光化学形成臭氧的白天排放和气象因素的强烈影响。短波辐射等气象因素的变化也能显著调节臭氧化学。本研究中开发的DeepCTM在有效表征复杂大气系统方面具有巨大潜力，可为政策制定者提供设计有效控制策略以减轻臭氧污染所需的迫切信息。
