Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among distinct pollution sources and various other influencing factors. Existing air quality forecasting methods cannot effectively model the diffusion processes of air pollutants between cities and monitoring stations, which may suddenly deteriorate the air quality of a region. In this paper, we propose HighAir, i.e., a hierarchical graph neural network-based air quality forecasting method, which adopts an encoder-decoder architecture and considers complex air quality influencing factors, e.g., weather and land usage. Specifically, we construct a city-level graph and station-level graphs from a hierarchical perspective, which can consider city-level and station-level patterns, respectively. We design two strategies, i.e., upper delivery and lower updating, to implement the inter-level interactions, and introduce message passing mechanism to implement the intra-level interactions. We dynamically adjust edge weights based on wind direction to model the correlations between dynamic factors and air quality. We compare HighAir with the state-of-the-art air quality forecasting methods on the dataset of Yangtze River Delta city group, which covers 10 major cities within 61,500 km2. The experimental results show that HighAir significantly outperforms other methods.

翻译：准确预测空气质量对于保护公众免受肺部和心脏疾病至关重要。由于不同污染源与多种其他影响因素之间存在复杂的相互作用，这是一项具有挑战性的任务。现有的空气质量预测方法无法有效建模城市与监测站之间空气污染物的扩散过程，而该过程可能导致区域空气质量突然恶化。本文提出HighAir，即一种基于分层图神经网络的空气质量预测方法，该方法采用编码器-解码器架构，并考虑了复杂的空气质量影响因素，例如天气和土地利用。具体而言，我们从分层视角构建了一个城市级图和多个站点级图，分别用于考虑城市级和站点级的模式。我们设计了两种策略，即上层传递与下层更新，以实现层级间的交互，并引入消息传递机制以实现层级内的交互。我们基于风向动态调整边权重，以建模动态因素与空气质量之间的关联。我们在覆盖61,500平方公里范围内10个主要城市的长三角城市群数据集上，将HighAir与最先进的空气质量预测方法进行了比较。实验结果表明，HighAir显著优于其他方法。