The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach
Open accessAbstract
Fusing satellite observations and station measurements to estimate ground‐level PM 2.5 is promising for monitoring PM 2.5 pollution. A geo‐intelligent approach, which incorporates geographical correlation into an intelligent deep learning architecture, is developed to estimate PM 2.5. Specifically, it considers geographical distance and spatiotemporally correlated PM 2.5 in a deep belief network (denoted as Geoi‐DBN). Geoi‐DBN can capture the essential features associated with PM 2.5 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 out‐of‐sample cross‐validation R2 increases from 0.42 to 0.88, and RMSE decreases from 29.96 to 13.03 μg/m 3. On the basis of the derived PM 2.5 distribution, it is predicted that over 80% of the Chinese population live in areas with an annual mean PM 2.5 of greater than 35 μg/m 3. This study provides a new perspective for air pollution monitoring in large geographic regions.
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Metadata
- Authors
- Li, T., Shen, H., Yuan, Q., Zhang, X., & Zhang, L.
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-12-05
- Volume
- 44
- Issue
- 23
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W2951572255
- Cited by
- 414
- References
- 43
- FWCI
- 18.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- Funders
- National Natural Science Foundation of China (41422108)
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- Li, T., Shen, H., Yuan, Q., Zhang, X., & Zhang, L. (2017). Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach. Geophysical Research Letters. https://doi.org/10.1002/2017gl075710
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