AI 中文总结
针对城市空气质量重建难题，应用深度学习技术，从四种关键污染物的稀疏观测出发，引入基于扩散的生成框架并对比确定性模型，通过数据增强实现泛化，凸显机器学习模型在空气污染重建中可靠实际部署的潜力。
AI 中文摘要
空气污染的全场重建对于评估污染暴露和支持公共卫生决策至关重要。但污染物之间复杂的相互作用、难以预测的天气模式以及监测站覆盖范围有限，使得这成为一项复杂任务。我们应用深度学习技术，从二氧化氮、臭氧、细颗粒物和可吸入颗粒物这四种关键污染物的稀疏观测中快速准确地重建。模型在全场模拟数据上训练，并在巴黎9至28个监测站收集的实际观测数据上评估。我们引入基于扩散的生成框架进行多污染物重建，并与确定性深度学习模型对比其性能。尽管观测有噪声且空间变异性强，但模型在模拟验证数据上实现了高结构相似性，在实际观测中产生了逼真的空间模式。我们还引入数据增强方法，无需重新训练就能转移到实际观测，使模型能在训练期外泛化。这些发现凸显了机器学习模型在空气污染重建任务中可靠实际部署的潜力。
英文摘要
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.
