Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2749-2026
https://doi.org/10.5194/essd-18-2749-2026
Data description article
 | 
21 Apr 2026
Data description article |  | 21 Apr 2026

Deriving regional and point source nitrogen oxides emissions in China from TROPOMI using the directional derivative approach with nonlinear chemical lifetime fitting

Ling Chen, Zhaonan Cai, Kang Sun, Yi Liu, Dongxu Yang, Mingming Li, and Lingyun Zhu

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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 essd-2025-480', Anonymous Referee #1, 22 Dec 2025
    • AC1: 'Reply on RC1', Ling Chen, 09 Mar 2026
  • RC2: 'Comment on essd-2025-480', Anonymous Referee #2, 09 Feb 2026
    • AC2: 'Reply on RC2', Ling Chen, 09 Mar 2026

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Ling Chen on behalf of the Authors (10 Mar 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (18 Mar 2026) by Graciela Raga
AR by Ling Chen on behalf of the Authors (20 Mar 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (22 Mar 2026) by Graciela Raga
AR by Ling Chen on behalf of the Authors (22 Mar 2026)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (05 Apr 2026) by Graciela Raga
AR by Ling Chen on behalf of the Authors (08 Apr 2026)  Author's response   Manuscript 
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Short summary
An appropriate representation of the NOx/NO2 ratio and NOx lifetime is essential for satellite-based NOx emissions estimation. We introduce a lightweight approach that applies variable NOx/NO2 ratios and piecewise fitting based on the directional derivative approach (DDA) to estimate regional and point-source NOx emissions. Our method directly captures nonlinear NOx chemistry and serves as an efficient alternative to both bottom-up inventories and computationally demanding top-down models.
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