研究者用扩散模型从少量监测站重建全城空气污染图，比传统确定性方法生成的模式更真实，还能跨时间迁移，值得关注。
该论文提出一种基于扩散生成模型的框架，用于从稀疏监测站观测数据重建巴黎的四种主要污染物（NO2、O3、PM2.5和PM10）的分布。模型在模拟的全场数据上训练，并在9至28个监测站的真实观测上评估，与确定性深度学习模型进行基准对比。实验表明，生成模型在模拟验证数据上达到高结构相似度，并通过功率谱分析生成更符合现实的污染空间模式。作者还引入数据增强方法，使模型无需重新训练即可泛化到实际观测场景。
From Deterministic to Generative Deep Learning for Urban Air Quality Reconstruction from Sparse Observations
Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making. However, the complex interactions among pollutants, hard-to-predict weather patterns, and limited monitoring station coverage make this a complex task. We apply deep learning techniques to provide fast and accurate reconstructions from sparse observations of four key pollutants: NO2, O3, PM2.5 and PM10. Models are trained on full-field simulation data and evaluated on real-world observations collected from 9 to 28 monitoring stations in the city of Paris. We introduce a diffusion-based generative framework for multi-pollutant reconstruction and benchmark its performance against deterministic deep learning models. Despite noisy observations and strong spatial variability, the models achieve high structural similarity on simulated validation data and produce realistic spatial patterns on real-world observations, as indicated by power-spectrum analysis. We introduce data augmentation methods that enable transfer to real-world observations without retraining, allowing the models to generalise beyond the training period. These findings highlight the potential of ML models for reliable real-world deployment in air pollution reconstruction tasks.