Articles | Volume 21, issue 22
Atmos. Chem. Phys., 21, 17003–17016, 2021
https://doi.org/10.5194/acp-21-17003-2021
Atmos. Chem. Phys., 21, 17003–17016, 2021
https://doi.org/10.5194/acp-21-17003-2021

Research article 24 Nov 2021

Research article | 24 Nov 2021

Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms

Yingying Ma et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2021-726', Anonymous Referee #1, 01 Oct 2021
    • AC1: 'Reply on RC1', Boming Liu, 25 Oct 2021
  • RC2: 'Comment on acp-2021-726', Anonymous Referee #2, 03 Oct 2021
    • AC2: 'Reply on RC2', Boming Liu, 25 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Boming Liu on behalf of the Authors (25 Oct 2021)  Author's response    Manuscript
ED: Publish as is (26 Oct 2021) by Jianping Huang
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Short summary
The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter of less than 2.5 μm (PM2.5). However, the traditional linear model cannot consider the influence of multiple meteorological variables sufficiently, which then causes low inversion accuracy. In this study, the machine learning algorithms which can input multiple features are used to solve this constraint.
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