AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
This addresses the high cost of deploying air quality monitoring stations for public health in China, but it is incremental as it builds on existing deep learning methods for spatial inference.
The paper tackles the problem of inferring real-time air quality in unmonitored locations in China by introducing AirRadar, a deep neural network that uses data from existing stations, and it demonstrates superiority over baselines on a dataset from 1,085 stations.
Monitoring real-time air quality is essential for safeguarding public health and fostering social progress. However, the widespread deployment of air quality monitoring stations is constrained by their significant costs. To address this limitation, we introduce \emph{AirRadar}, a deep neural network designed to accurately infer real-time air quality in locations lacking monitoring stations by utilizing data from existing ones. By leveraging learnable mask tokens, AirRadar reconstructs air quality features in unmonitored regions. Specifically, it operates in two stages: first capturing spatial correlations and then adjusting for distribution shifts. We validate AirRadar's efficacy using a year-long dataset from 1,085 monitoring stations across China, demonstrating its superiority over multiple baselines, even with varying degrees of unobserved data. The source code can be accessed at https://github.com/CityMind-Lab/AirRadar.