A Framework to Predict High-Resolution Spatiotemporal PM<sub>2.5</sub> Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China
Air-borne particulate matter, PM<sub>2.5</sub> (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM<sub>2.5...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/17/2825 |