Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 6
Articles | Volume 19, issue 6
https://doi.org/10.5194/amt-19-2225-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2225-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 6
Research article
 | 
01 Apr 2026
Research article |  | 01 Apr 2026

A Physics-Constrained Deep-Learning Framework based on Long-Term Remote-Sensing Data for Retrieving Vertical Distribution of PM2.5 Chemical Components

Hongyi Li, Ting Yang, Yele Sun, and Zifa Wang

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Short summary
We develop a physics-constrained deep-learning framework to retrieve vertical profiles of particulate matter <2.5 µm chemical components from lidar measurements. Comparisons with surface, tower, and aircraft measurements validate the performance of the framework. A six-year vertical profile dataset generated for Beijing reveals that organic matter and nitrate are dominant chemical components. This work offers a new perspective on the lidar inversion of chemical component profiles.
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