Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7573-2021
© 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/gmd-14-7573-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
15 Dec 2021
Model evaluation paper |
| 15 Dec 2021
| 15 Dec 2021
High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM)
Qian Ye
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy
of Sciences, Beijing 100049, China
Jie Li
CORRESPONDING AUTHOR
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy
of Sciences, Beijing 100049, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Xueshun Chen
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Huansheng Chen
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Wenyi Yang
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Huiyun Du
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Xiaole Pan
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Xiao Tang
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
Wei Wang
CORRESPONDING AUTHOR
China National Environmental Monitoring Center, Beijing 100012, China
Lili Zhu
China National Environmental Monitoring Center, Beijing 100012, China
Jianjun Li
CORRESPONDING AUTHOR
China National Environmental Monitoring Center, Beijing 100012, China
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
Zifa Wang
CORRESPONDING AUTHOR
State Key Laboratory of Atmospheric Boundary Layer Physics and
Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of
Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy
of Sciences, Beijing 100049, China
Center for Excellence in Regional Atmospheric Environment, Institute
of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
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Cited
11 citations as recorded by crossref.
- Optimizing Air Pollution Prediction With Random Forest Algorithm S. Singh et al. 10.1007/s41810-025-00292-6
- Zoning for synergistic reduction of pollution and carbon emissions in the Beijing-Tianjin-Hebei and its surrounding areas based on environmental justice principles J. Du et al. 10.1016/j.scitotenv.2025.179905
- Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China Z. Wu et al. 10.5194/gmd-17-8885-2024
- Temporally boosting neural network for improving dynamic prediction of PM2.5 concentration with changing and unbalanced distribution H. Shi et al. 10.1016/j.jenvman.2025.125371
- Recent Changes in Groundwater and Surface Water in Large Pan-Arctic River Basins H. Lin et al. 10.3390/rs14030607
- Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs Q. Ye et al. 10.1016/j.atmosenv.2022.119431
- Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex” T. Zhu et al. 10.1007/s00376-023-2379-0
- Observations of polycyclic aromatic hydrocarbons at Changbai Mountain in Northeast Asia: Characteristics, transboundary transport, and health impacts Y. Shan et al. 10.1016/j.jes.2025.06.020
- Influences of stratospheric intrusions to high summer surface ozone over a heavily industrialized region in northern China Y. Zhang et al. 10.1088/1748-9326/ac8b24
- Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case Z. Wang et al. 10.3389/fenvs.2022.955980
- NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components H. Li et al. 10.5194/gmd-17-8495-2024
11 citations as recorded by crossref.
- Optimizing Air Pollution Prediction With Random Forest Algorithm S. Singh et al. 10.1007/s41810-025-00292-6
- Zoning for synergistic reduction of pollution and carbon emissions in the Beijing-Tianjin-Hebei and its surrounding areas based on environmental justice principles J. Du et al. 10.1016/j.scitotenv.2025.179905
- Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China Z. Wu et al. 10.5194/gmd-17-8885-2024
- Temporally boosting neural network for improving dynamic prediction of PM2.5 concentration with changing and unbalanced distribution H. Shi et al. 10.1016/j.jenvman.2025.125371
- Recent Changes in Groundwater and Surface Water in Large Pan-Arctic River Basins H. Lin et al. 10.3390/rs14030607
- Uncertainties in the simulated intercontinental transport of air pollutants in the springtime from emission and meteorological inputs Q. Ye et al. 10.1016/j.atmosenv.2022.119431
- Recent Progress in Atmospheric Chemistry Research in China: Establishing a Theoretical Framework for the “Air Pollution Complex” T. Zhu et al. 10.1007/s00376-023-2379-0
- Observations of polycyclic aromatic hydrocarbons at Changbai Mountain in Northeast Asia: Characteristics, transboundary transport, and health impacts Y. Shan et al. 10.1016/j.jes.2025.06.020
- Influences of stratospheric intrusions to high summer surface ozone over a heavily industrialized region in northern China Y. Zhang et al. 10.1088/1748-9326/ac8b24
- Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case Z. Wang et al. 10.3389/fenvs.2022.955980
- NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components H. Li et al. 10.5194/gmd-17-8495-2024
Latest update: 03 Nov 2025
Short summary
We developed a global tropospheric atmospheric chemistry source–receptor model. This model can quantify the contributions of multiple air pollutants from various source regions in one simulation without introducing the nonlinear error of atmospheric chemistry. The S-R relationships of PM2.5 and O3 from a global high-resolution (0.5° × 0.5°) simulation were given and compared with previous studies. This model will be useful for creating a link between the scientific community and policymakers.
We developed a global tropospheric atmospheric chemistry source–receptor model. This model can...
