AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916821907537920 |
|---|---|
| author | Wang, Qiongyan Xia, Yutong ZHong, Siru Li, Weichuang Wu, Yuankai Cheng, Shifen Zhang, Junbo Zheng, Yu Liang, Yuxuan |
| author_facet | Wang, Qiongyan Xia, Yutong ZHong, Siru Li, Weichuang Wu, Yuankai Cheng, Shifen Zhang, Junbo Zheng, Yu Liang, Yuxuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks Wang, Qiongyan Xia, Yutong ZHong, Siru Li, Weichuang Wu, Yuankai Cheng, Shifen Zhang, Junbo Zheng, Yu Liang, Yuxuan Machine Learning Artificial Intelligence 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. |
| title | AirRadar: Inferring Nationwide Air Quality in China with Deep Neural Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.13141 |