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.
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.
the Creative Commons Attribution 4.0 License.
Data description article
| 
19 May 2021
Data description article |
| 19 May 2021
| 19 May 2021
Long-term trends of ambient nitrate (NO3−) concentrations across China based on ensemble machine-learning models
Rui Li
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Lulu Cui
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Yilong Zhao
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Wenhui Zhou
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Hongbo Fu
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, China
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, China
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Cited
21 citations as recorded by crossref.
- Explosive PM2.5 growth and health effects during extreme haze in Shanghai, China: Insights from surface and vertical observations W. Sun et al. https://doi.org/10.1016/j.jes.2025.09.010
- Gas-phase reactions accelerate nitrate formation in spring and early summer in a megacity of the Sichuan Basin, China Y. Li et al. https://doi.org/10.1016/j.jes.2026.02.002
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. https://doi.org/10.1029/2023JD038939
- Machine learning revealing key factors influencing HONO chemistry in Beijing during heating and non-heating periods W. Zhang et al. https://doi.org/10.1016/j.atmosres.2023.107130
- Chemical characterization of atmospheric aerosols in spring Shanghai: Formation mechanism of secondary organic aerosols on Asian dust surface R. Li et al. https://doi.org/10.1016/j.atmosres.2025.108308
- Assessment of long-term particulate nitrate air pollution and its health risk in China Y. Hang et al. https://doi.org/10.1016/j.isci.2022.104899
- Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020 W. Sun et al. https://doi.org/10.1007/s11869-024-01663-7
- Hydration-driven metabolic activity enhances the resilience of Xanthoria parietina (L.) Th. Fr. to nitrate stress J. Styburski & K. Skubała https://doi.org/10.1016/j.envexpbot.2025.106276
- Nitrate-driven extreme winter PM2.5 pollution in Shanghai, China G. Chen et al. https://doi.org/10.1038/s44407-025-00028-3
- Measurement report: simultaneous measurement on gas- and particle-phase water-soluble organics in Shanghai: enhanced light absorption of transported Asian dust Z. Li et al. https://doi.org/10.5194/acp-25-12037-2025
- A Decadal Change in Atmospheric Nitrogen Deposition at a Rural Site in Southern China K. Ren et al. https://doi.org/10.3390/atmos15050583
- A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China S. Wang et al. https://doi.org/10.3390/rs14071640
- Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model H. Yao et al. https://doi.org/10.3390/app11177855
- Deep transfer learning for spatiotemporal mapping of PM2.5 nitrate across China: Addressing small data challenges in environmental machine learning X. Zheng et al. https://doi.org/10.1016/j.jhazmat.2025.138206
- Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015 Y. Chen et al. https://doi.org/10.1016/j.chemosphere.2021.133393
- Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method Y. Qiu et al. https://doi.org/10.1016/j.scitotenv.2022.153849
- Formation mechanism and control strategy for particulate nitrate in China H. Wang et al. https://doi.org/10.1016/j.jes.2022.09.019
- Data driven analysis on changing patterns of reactive nitrogen deposition in East and Southeast Asia J. Tan et al. https://doi.org/10.1016/j.atmosenv.2026.121962
- Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. https://doi.org/10.5194/acp-23-3311-2023
- China Played a Greater Role in Reducing the Deposition of Japan and Korea Than Themselves K. Zhou et al. https://doi.org/10.1021/acs.est.5c08247
- Leveraging machine learning and ten-cities observations to disentangle emission and meteorological controls on secondary inorganic aerosols in China H. Xiao et al. https://doi.org/10.1016/j.envpol.2026.128190
21 citations as recorded by crossref.
- Explosive PM2.5 growth and health effects during extreme haze in Shanghai, China: Insights from surface and vertical observations W. Sun et al. https://doi.org/10.1016/j.jes.2025.09.010
- Gas-phase reactions accelerate nitrate formation in spring and early summer in a megacity of the Sichuan Basin, China Y. Li et al. https://doi.org/10.1016/j.jes.2026.02.002
- Impact of Clean Air Policy on Criteria Air Pollutants and Health Risks Across China During 2013–2021 R. Li et al. https://doi.org/10.1029/2023JD038939
- Machine learning revealing key factors influencing HONO chemistry in Beijing during heating and non-heating periods W. Zhang et al. https://doi.org/10.1016/j.atmosres.2023.107130
- Chemical characterization of atmospheric aerosols in spring Shanghai: Formation mechanism of secondary organic aerosols on Asian dust surface R. Li et al. https://doi.org/10.1016/j.atmosres.2025.108308
- Assessment of long-term particulate nitrate air pollution and its health risk in China Y. Hang et al. https://doi.org/10.1016/j.isci.2022.104899
- Spatiotemporal estimates and health risks of atmospheric trace metals across Hong Kong during 2016–2020 W. Sun et al. https://doi.org/10.1007/s11869-024-01663-7
- Hydration-driven metabolic activity enhances the resilience of Xanthoria parietina (L.) Th. Fr. to nitrate stress J. Styburski & K. Skubała https://doi.org/10.1016/j.envexpbot.2025.106276
- Nitrate-driven extreme winter PM2.5 pollution in Shanghai, China G. Chen et al. https://doi.org/10.1038/s44407-025-00028-3
- Measurement report: simultaneous measurement on gas- and particle-phase water-soluble organics in Shanghai: enhanced light absorption of transported Asian dust Z. Li et al. https://doi.org/10.5194/acp-25-12037-2025
- A Decadal Change in Atmospheric Nitrogen Deposition at a Rural Site in Southern China K. Ren et al. https://doi.org/10.3390/atmos15050583
- A High-Performance Convolutional Neural Network for Ground-Level Ozone Estimation in Eastern China S. Wang et al. https://doi.org/10.3390/rs14071640
- Retrieval of Chlorophyll-a Concentrations in the Coastal Waters of the Beibu Gulf in Guangxi Using a Gradient-Boosting Decision Tree Model H. Yao et al. https://doi.org/10.3390/app11177855
- Deep transfer learning for spatiotemporal mapping of PM2.5 nitrate across China: Addressing small data challenges in environmental machine learning X. Zheng et al. https://doi.org/10.1016/j.jhazmat.2025.138206
- Estimation and variation analysis of secondary inorganic aerosols across the Greater Bay Area in 2005 and 2015 Y. Chen et al. https://doi.org/10.1016/j.chemosphere.2021.133393
- Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method Y. Qiu et al. https://doi.org/10.1016/j.scitotenv.2022.153849
- Formation mechanism and control strategy for particulate nitrate in China H. Wang et al. https://doi.org/10.1016/j.jes.2022.09.019
- Data driven analysis on changing patterns of reactive nitrogen deposition in East and Southeast Asia J. Tan et al. https://doi.org/10.1016/j.atmosenv.2026.121962
- Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. https://doi.org/10.5194/acp-23-3311-2023
- China Played a Greater Role in Reducing the Deposition of Japan and Korea Than Themselves K. Zhou et al. https://doi.org/10.1021/acs.est.5c08247
- Leveraging machine learning and ten-cities observations to disentangle emission and meteorological controls on secondary inorganic aerosols in China H. Xiao et al. https://doi.org/10.1016/j.envpol.2026.128190
Saved (final revised paper)
Latest update: 01 Nov 2026
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.
A unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by...
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