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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Cited articles

Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Chen, H., Li, D., Gurmesa, G. A., Yu, G., Li, L., Zhang, W., Fang, H., and Mo, J.: Effects of nitrogen deposition on carbon cycle in terrestrial ecosystems of China: A meta-analysis, Environ. Pollut., 206, 352–360, https://doi.org/10.1016/j.envpol.2015.07.033, 2015. 
Chen, J., Yin, J., Zang, L., Zhang, T., and Zhao, M.: Stacking machine learning model for estimating hourly PM2.5 in China based on Himawari-8 aerosol optical depth data, Sci. Total Environ., 697, 134021, https://doi.org/10.1016/j.scitotenv.2019.134021, 2019. 
Chen, Z., Chen, D., Kwan, M.-P., Chen, B., Gao, B., Zhuang, Y., Li, R., and Xu, B.: The control of anthropogenic emissions contributed to 80 % of the decrease in PM2.5 concentrations in Beijing from 2013 to 2017, Atmos. Chem. Phys., 19, 13519–13533, https://doi.org/10.5194/acp-19-13519-2019, 2019. 
Chen, Z. Y., Zhang, R., Zhang, T. H., Ou, C. Q., and Guo, Y.: A kriging-calibrated machine learning method for estimating daily ground-level NO2 in mainland China, Sci. Total Environ., 690, 556–564, https://doi.org/10.1016/j.scitotenv.2019.06.349, 2019. 
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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.