We consider the problem of real-time reconstruction of urban air pollution maps. The task is challenging due to the heterogeneous sources of available data, the scarcity of direct measurements, the presence of noise, and the large surfaces that need to be considered. In this work, we introduce different reconstruction methods based on posing the problem on city graphs. Our strategies can be classified as fully data-driven, physics-driven, or hybrid, and we combine them with super-learning models. The performance of the methods is tested in the case of the inner city of Paris, France.

翻译：我们考虑城市空气污染地图的实时重建问题。由于可用数据的异质性、直接测量值的稀缺性、噪声的存在以及需考虑的大面积区域，该任务极具挑战性。在本研究中，我们引入了基于城市图建模的不同重建方法。这些策略可分为完全数据驱动、物理驱动或混合型，并与超级学习模型相结合。我们以法国巴黎内城区为例，测试了这些方法的性能。