ChinaAirNet

Air quality prediction plays a crucial role in public health and environmental protection. Accurate air quality prediction is a complex multivariate spatiotemporal problem, that involves interactions across temporal patterns, pollutant correlations, spatial station dependencies, and particularly meteorological influences that govern pollutant dispersion and chemical transformations. Existing works underestimate the critical role of atmospheric conditions in air quality prediction and neglect comprehensive meteorological data utilization, thereby impairing the modeling of dynamic interdependencies between air quality and meteorological data. To overcome this, we propose MDSTNet, an encoder-decoder framework that explicitly models air quality observations and atmospheric conditions as distinct modalities, integrating multi-pressure-level meteorological data and weather forecasts to capture atmosphere-pollution dependencies for prediction. Meantime, we construct ChinaAirNet, the first nationwide dataset combining air quality records with multi-pressure-level meteorological observations. Experimental results on ChinaAirNet demonstrate MDSTNet's superiority, substantially reducing 48-hour prediction errors by  17.54\% compared to the state-of-the-art model. The source code and dataset will be available on github.空气质量预测对于公共卫生与环境保护均具有至关重要的意义。精准的空气质量预测是一项复杂的多变量时空问题，其涉及时序模式、污染物相关性、监测站点空间依赖关系之间的交互作用，尤其还受调控污染物扩散与化学转化的气象因素影响。现有研究低估了大气条件在空气质量预测中的关键作用，且忽视了对全面气象数据的充分利用，进而削弱了空气质量与气象数据间动态依赖关系的建模效果。为此，我们提出MDSTNet——一种编码器-解码器框架，该框架将空气质量观测数据与大气条件明确建模为不同模态，并融合多气压层气象数据与天气预报数据，以捕捉大气-污染物间的依赖关系用于预测任务。与此同时，我们构建了全国性数据集ChinaAirNet，这也是首个将空气质量记录与多气压层气象观测数据相结合的数据集。在ChinaAirNet数据集上的实验结果表明，MDSTNet具备显著优势：与当前最优模型相比，其将48小时预测误差大幅降低了17.54%。本研究的源代码与数据集将在GitHub平台公开。

搜集汇总

背景与挑战

背景概述

ChinaAirNet是中国首个全国性空气质量与多压力层气象综合数据集，包含来自1628个监测站的六种污染物浓度和AQI小时数据，以及来自ERA5再分析数据的8个气象变量在7个垂直压力层的气象观测。该数据集专为支持空气质量预测和时空数据分析研究而构建，强调了气象条件对污染物扩散和化学转化的影响。