发表机构
ColorfulClouds Technology Co., Ltd.; School of Systems Science, Beijing Normal University; D-ITET, ETH Zurich（彩云科技有限公司; 北京师范大学系统科学学院; 苏黎世联邦理工学院动态信息与电气工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在弥合离散和网格化的PM10预测。核心方法是基于卷积条件神经过程的OmniPM-Net，融合化学传输模型与图神经网络预测。主要贡献是提升预测准确性，降低误差，在高浓度尾部和沙尘事件中有显著改进，还能提供网格化场。
AI 中文摘要
预测颗粒物（PM10）既需要站点尺度的准确性，也需要连续的空间场，特别是在严重沙尘暴期间。化学传输模型（CTM）提供网格化预测，但存在局部偏差，而图神经网络（GNN）在短提前期能很好地跟踪监测站点，但不产生网格化输出。本文提出了OmniPM-Net，这是一种基于卷积条件神经过程（ConvCNP）的融合模型，在共享空间表示中协调这两种预测类型。地形感知高斯集卷积将不规则的GNN站点预测提升到规则网格上，多尺度空间源注意力（SSA）模块将其与哥白尼大气监测服务（CAMS）预测融合；共享的全查询读出然后将这种表示解码为108小时内站点或网格单元的一致PM10预测。在2024年全年对中国1618个空气质量监测站进行评估，OmniPM-Net与更强的GNN基线的站点级准确性相匹配（平均绝对误差21.14对22.00μg/m³），并将CAMS平均绝对误差降低30%，同时提供离散GNN无法提供的网格化场。其最明显的改进在于高浓度尾部，其中第90百分位数的MAE相对于GNN下降9%，相对于CAMS下降25%，在沙尘事件期间，它提高了分类检测技能，同时跟踪不断演变的空间轨迹。
英文摘要
Forecasting particulate matter (PM10) requires both station-scale accuracy and continuous spatial fields, especially during severe dust storms. Chemical transport models (CTMs) provide gridded forecasts but retain local biases, whereas graph neural networks (GNNs) track monitoring sites well at short lead times but do not produce gridded outputs. Here we present OmniPM-Net, a Convolutional Conditional Neural Process (ConvCNP)-based fusion model that reconciles these two forecast types within a shared spatial representation. A terrain-aware Gaussian set convolution lifts irregular GNN station forecasts onto a regular grid, where a multi-scale Spatial Source Attention (SSA) module blends them with Copernicus Atmosphere Monitoring Service (CAMS) forecasts; a shared omni-query readout then decodes this representation into consistent PM10 predictions at either stations or grid cells over a 108 h horizon. Evaluated across 1,618 air-quality monitoring stations throughout China over the full year of 2024, OmniPM-Net matches the station-level accuracy of the stronger GNN baseline (mean absolute error 21.14 versus 22.00 ug/m3) and reduces the CAMS mean absolute error by 30%, while simultaneously delivering the gridded fields that the discrete GNN cannot. Its clearest gains are in the high-concentration tail, where the 90th-percentile MAE falls by 9% relative to the GNN and 25% relative to CAMS, and during dust episodes, where it improves categorical detection skill while tracking the evolving spatial trajectory.
