Air Quality Monitoring and Forecasting has been a popular research topic in recent years. Recently, data-driven approaches for air quality forecasting have garnered significant attention, owing to the availability of well-established data collection facilities in urban areas. Fixed infrastructures, typically deployed by national institutes or tech giants, often fall short in meeting the requirements of diverse personalized scenarios, e.g., forecasting in areas without any existing infrastructure. Consequently, smaller institutes or companies with limited budgets are compelled to seek tailored solutions by introducing more flexible infrastructures for data collection. In this paper, we propose an expandable graph attention network (EGAT) model, which digests data collected from existing and newly-added infrastructures, with different spatial structures. Additionally, our proposal can be embedded into any air quality forecasting models, to apply to the scenarios with evolving spatial structures. The proposal is validated over real air quality data from PurpleAir.

翻译：近年来，空气质量监测与预测已成为热门研究课题。随着城市地区成熟数据采集设施的普及，基于数据驱动的空气质量预测方法获得了广泛关注。由国家机构或科技巨头部署的固定基础设施通常难以满足多样化的个性化场景需求，例如在缺乏现有基础设施的区域进行预测。因此，预算有限的小型机构或企业被迫通过引入更灵活的数据采集基础设施来寻求定制化解决方案。本文提出了一种可扩展图注意力网络（EGAT）模型，该模型能够处理来自现有和新增加基础设施采集的数据，这些数据具有不同的空间结构。此外，我们的方案可嵌入任何空气质量预测模型，适用于空间结构动态演变的场景。该方案已通过PurpleAir的真实空气质量数据得到验证。