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.

Data sets

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 https://doi.org/10.5281/zenodo.3988307

Model code and software

A New Global Gridded Sea Surface Temperature Data Product Based on Multisource Data (Version 1.0) Mengmeng Cao, Kebiao Mao, Yibo Yan, Jiancheng Shi, Han Wang, Tongren Xu, Shu Fang, and Zijin Yuan https://doi.org/10.5281/zenodo.4762067

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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.