AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
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 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.
Cite
Text
Wang et al. "AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I27.35069Markdown
[Wang et al. "AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/wang2025aaai-airradar/) doi:10.1609/AAAI.V39I27.35069BibTeX
@inproceedings{wang2025aaai-airradar,
title = {{AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks}},
author = {Wang, Qiongyan and Xia, Yutong and Zhong, Siru and Li, Weichuang and Wu, Yuankai and Cheng, Shifen and Zhang, Junbo and Zheng, Yu and Liang, Yuxuan},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {28467-28475},
doi = {10.1609/AAAI.V39I27.35069},
url = {https://mlanthology.org/aaai/2025/wang2025aaai-airradar/}
}