Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks

TL;DR

This paper introduces an expandable graph attention network (EGAT) for air quality forecasting that adapts to evolving spatial data from various infrastructures, enabling flexible and personalized monitoring solutions.

Contribution

The paper presents a novel EGAT model that integrates data from both existing and new infrastructures, adaptable to changing spatial structures in air quality forecasting.

Findings

Validated on real PurpleAir data

Effective in handling evolving infrastructure data

Improves forecasting flexibility and accuracy

Abstract

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.