Poor air quality produces detrimental effects worldwide; hence, it is vital to thoroughly characterize air pollution sources and effectively address and mitigate such effects. Due to the complexity of the underlying physical processes, and uncertainties in the available observations, the air pollution source identification problem is typically cast within a Bayesian inversion framework. The latter incorporates prior knowledge and observations to characterize a release event through the posterior distribution of the source parameters. In this study, we rely on two-dimensional (2D) pollutant concentration distributions as observations, and adopt the Wasserstein () distance to model the likelihood probability distribution for given emission parameters. Since the posterior distribution is estimated via random sampling that involves many forward model runs, the Bayesian framework can be computationally prohibitive for realistic urban air pollution problems that are driven by computationally demanding micro-scale flow simulations. Furthermore, computing the distance is resource-intensive . In this context, we develop a computationally efficient Bayesian framework by following (i) a two-stage approach that reduces the cost of the Bayesian inversion, and (ii) an artificial intelligence (AI) approximation of the distance. In the two-stage approach, a low-resolution dispersion model is run in the first stage to propose representative samples of emission parameters for final selection by the original high-resolution model in the second stage. In addition, we approximate the distance using a deep neural network (DNN) to achieve an appreciable reduction in the computational cost with negligible loss in the inversion performance. We design numerical experiments to test the sensitivity of the inverse solution to the characteristics of the approximative model. The results indicate that pairing the two-stage approach with the DNN approximation of the distance preserves the quality of the inverse solution, while achieving at least threefold reductions in the computational cost.

中文翻译：

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两步人工智能辅助贝叶斯城市规模污染源识别

恶劣的空气质量在全球范围内产生有害影响；因此，彻底描述空气污染源并有效解决和减轻这种影响至关重要。由于潜在物理过程的复杂性以及可用观测的不确定性，空气污染源识别问题通常在贝叶斯反演框架内进行。后者结合了先验知识和观察结果，通过源参数的后验分布来表征释放事件。在本研究中，我们依靠二维 (2D) 污染物浓度分布作为观测值，并采用 Wasserstein () 距离对给定排放参数的似然概率分布进行建模。由于后验分布是通过涉及许多前向模型运行的随机采样来估计的，因此贝叶斯框架对于由计算要求高的微尺度流动模拟驱动的现实城市空气污染问题可能在计算上令人望而却步。此外，计算距离是资源密集型的。在这种情况下，我们通过以下方式开发了一个计算高效的贝叶斯框架：(i) 降低贝叶斯反演成本的两阶段方法，以及 (ii) 距离的人工智能 (AI) 近似。在两阶段方法中，在第一阶段运行低分辨率色散模型，以提出发射参数的代表性样本，以便由第二阶段的原始高分辨率模型进行最终选择。此外，我们使用深度神经网络（DNN）来近似距离，以显着降低计算成本，同时反演性能的损失可以忽略不计。我们设计数值实验来测试逆解对近似模型特征的敏感性。结果表明，将两阶段方法与距离的 DNN 近似配对可以保持逆解的质量，同时实现计算成本至少三倍的减少。