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 paper
| 
19 May 2021
Data description paper |
| 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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Shijie Liu, Xinbei Xu, Si Zhang, Rongjie Li, Zheng Li, Can Wu, Rui Li, Guiqin Zhang, and Gehui Wang
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Preprint archived
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We conducted α-pinene photooxidation experiments in an atmospheric chamber at different NOx concentrations. The increased distribution coefficient of the oxidation products between the aerosol and gas phases with NOx was responsible for the increased SOA yields with NOx under low-NOx conditions. We also found the fraction of SOA made up of nitrogen-containing organic compounds (NOCs) increased with NOx.
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Rui Li, Yining Gao, Yubao Chen, Meng Peng, Weidong Zhao, Gehui Wang, and Jiming Hao
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Short summary
Short summary
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Tao Wang, Yangyang Liu, Hanyun Cheng, Zhenzhen Wang, Hongbo Fu, Jianmin Chen, and Liwu Zhang
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Rui Li, Yilong Zhao, Hongbo Fu, Jianmin Chen, Meng Peng, and Chunying Wang
Atmos. Chem. Phys., 21, 8677–8692, https://doi.org/10.5194/acp-21-8677-2021, https://doi.org/10.5194/acp-21-8677-2021, 2021
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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.
A unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by...
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