想用CAMS数据做高分辨率PM2.5预测？这篇论文提出了一个不依赖时间序列的降尺度框架，融合多种地理信息，用OpenAQ观测做伪监督，效果不错。
该论文提出一个站指导框架，将粗分辨率CAMS大气成分场超分辨率（×40，约1km）并偏差校正为局地PM2.5变化。框架利用多尺度Transformer网络，结合人类活动、土地覆盖、高程、卫星气溶胶观测和风场等异构信息，无需时间序列建模。为解决稀疏原位数据密集监督问题，引入时间无关传播策略，通过空间高斯混合插值OpenAQ观测。在欧洲的定性和站点评估显示，模型能恢复细粒度空间结构并有效缓解CAMS局部偏差。
Air Quality Downscaling with Station-Guided Pseudo-Supervision
Super-resolving coarse atmospheric fields to local PM$_{2.5}$ variations is uniquely challenged by a mismatch in spatial support: while pixels represent regional averages, ground-truth observations are discrete, unaligned samples of a continuous spatial signal. To bridge this gap, we present a station-guided framework for high-resolution PM$_{2.5}$ downscaling over Europe. Taking coarse CAMS atmospheric composition fields alongside heterogeneous side information (i.e., human activity, land cover, elevation, satellite aerosol observations, and wind fields) our framework jointly super-resolves ($\times 40$, $\approx$ 1 km) and bias-corrects CAMS rasters, without relying on temporal sequence modelling. To address the challenge of densely supervising our multi-scale transformer network with sparse in-situ data, we introduce a time-agnostic propagation strategy that utilises spatial Gaussian blending of interpolated OpenAQ observations. Extensive qualitative and station-level evaluations across Europe demonstrate that our model recovers fine-grained spatial structures and effectively mitigates localised CAMS biases.