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.

本文提出了一种可扩展的图注意力网络（EGAT）模型，它从现有和新添加的基础设施中收集数据，并适用于具有不断变化的空间结构的情景，用于空气质量预测模型中。