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arXiv · 1707.03558

Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach

Tongwen Li·Huanfeng Shen·Qiangqiang Yuan·Xuechen Zhang·Liangpei Zhang
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Abstract

Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can capture the essential features associated with PM2.5 from latent factors. It was trained and tested with data from China in 2015. The results show that Geoi-DBN performs significantly better than the traditional neural network. The cross-validation R increases from 0.63 to 0.94, and RMSE decreases from 29.56 to 13.68$μ$g/m3. On the basis of the derived PM2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM2.5 of greater than 35$μ$g/m3. This study provides a new perspective for air pollution monitoring in large geographic regions.

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Tongwen Li, Huanfeng Shen, Qiangqiang Yuan, Xuechen Zhang, Liangpei Zhang. 2017-07-12. Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach. https://doi.org/10.1002/2017gl075710

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