Articles | Volume 13, issue 5
Earth Syst. Sci. Data, 13, 2147–2163, 2021
https://doi.org/10.5194/essd-13-2147-2021
Earth Syst. Sci. Data, 13, 2147–2163, 2021
https://doi.org/10.5194/essd-13-2147-2021

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 et al.

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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
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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.