Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 13, issue 5
Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-2147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-2147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 5
Data description paper
 | 
19 May 2021
Data description paper |  | 19 May 2021

Long-term trends of ambient nitrate (NO3−) concentrations across China based on ensemble machine-learning models

Rui Li, Lulu Cui, Yilong Zhao, Wenhui Zhou, and Hongbo Fu

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Hongbo Fu on behalf of the Authors (27 Feb 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Mar 2021) by David Carlson
RR by Anonymous Referee #2 (22 Mar 2021)
ED: Publish as is (22 Mar 2021) by David Carlson
AR by Hongbo Fu on behalf of the Authors (23 Mar 2021)  Manuscript 
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Short summary
A unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating multi-source variables. The newly developed product featured an excellent cross-validation R2 value (0.78) and relatively lower RMSE (1.19 μg N m−3) and mean absolute error (MAE: 0.81 μg N m−3). The dataset also exhibited relatively robust performance at the spatial and temporal scales. The dataset over China could deepen knowledge of the status of N pollution in China.
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