Opportunistic Air Quality Monitoring and Forecasting with Expandable
  Graph Neural Networks
Jingwei Zuo Wenbin Li Michele Baldo Hakim Hacid
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.