Nationwide Air Pollution Forecasting with Heterogeneous Graph Neural Networks

Abstract

Nowadays, air pollution is one of the most relevant environmental problems in most urban settings. Due to the utility in operational terms of anticipating certain pollution levels, several predictors based on Graph Neural Networks (GNN) have been proposed for the last years. Most of these solutions usually encode the relationships among stations in terms of their spatial distance, but they fail when it comes to capturing other spatial and feature-based contextual factors. Besides, they assume a homogeneous setting where all the stations are able to capture the same pollutants. However, large-scale settings frequently comprise different types of stations, each one with different measurement capabilities. For that reason, the present article introduces a novel GNN framework able to capture the similarities among stations related to the land use of their locations and their primary source of pollution. Furthermore, we define a methodology to deal with heterogeneous settings on the top of the GNN architecture. Finally, the proposal has been tested with a nation-wide Spanish air-pollution dataset with very promising results.