AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks

• URL: http://arxiv.org/abs/2501.13141v1
• Date: Wed, 22 Jan 2025 14:32:20 GMT
• Title: AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
• Authors: Qiongyan Wang, Yutong Xia, Siru ZHong, Weichuang Li, Yuankai Wu, Shifen Cheng, Junbo Zhang, Yu Zheng, Yuxuan Liang,
• Abstract summary: We introduce emphAirRadar, a deep neural network designed to accurately infer real-time air quality in locations lacking monitoring stations. By leveraging learnable mask tokens, AirRadar reconstructs air quality features in unmonitored regions. We validate AirRadar's efficacy using a year-long dataset from 1,085 monitoring stations across China.
• Score: 27.30426866654955
• License:
• Abstract: 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.

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