Articles | Volume 23, issue 22
https://doi.org/10.5194/acp-23-14271-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-14271-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report
| 
16 Nov 2023
Measurement report |
| 16 Nov 2023
| 16 Nov 2023
Measurement report: Dust and anthropogenic aerosols' vertical distributions over northern China dense aerosols gathered at the top of the mixing layer
Zhuang Wang
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Chune Shi
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Hao Zhang
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Yujia Chen
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Xiyuan Chi
National Meteorological Center, Beijing 100081, China
Congzi Xia
GBA Branch of Aerospace Information Research Institute, Chinese Academy of Sciences, Guangzhou 510530, China
Suyao Wang
Huaibei Meteorological Bureau, Huaibei 235000, Anhui, China
Yizhi Zhu
School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Kaidi Zhang
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Xintong Chen
Anhui Province Key Laboratory of Atmospheric Science and Satellite Remote Sensing, Anhui Institute of Meteorological Sciences, Hefei 230031, China
Shouxian National Climatology Observatory, Shouxian 232200, China
Huaihe River Basin Typical Farmland Ecological Meteorological Field Science Experiment Base of CMA, Shouxian 232200, China
Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China
Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei 230026, China
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Cited
8 citations as recorded by crossref.
- Constructing the 3D spatial distribution of PM2.5 concentrations during the 2022 Beijing Winter Olympics using LiDAR vertical observation networks and machine learning models Z. Wang et al. https://doi.org/10.1016/j.envint.2025.109875
- Opposing trends in the peak and low ozone concentrations in eastern China: anthropogenic and meteorological influences Z. Wang et al. https://doi.org/10.5194/acp-25-347-2025
- Airborne particulate matter reduction through developed dust control systems for wheat thresher T. Mehmood et al. https://doi.org/10.1007/s11869-025-01831-3
- Spatial and temporal distribution of MODIS aerosol optical depth over Ghana and comparison with AERONET data H. Affum et al. https://doi.org/10.1080/27658511.2025.2593743
- Vertical distribution of aerosols and association with atmospheric boundary layer structures during regional aerosol transport over central China X. Sun et al. https://doi.org/10.1016/j.envpol.2024.124967
- The evolution of aerosol mixing state derived from a field campaign in Beijing: implications for particle aging timescales in urban atmospheres J. Liu et al. https://doi.org/10.5194/acp-25-5075-2025
- K-nearest neighbors approximation for the multiwavelength lidar retrieval of bimodal aerosol microphysical properties Z. Chen et al. https://doi.org/10.1364/AO.577119
- Measurement report: Observational insights into the impact of dust transport on atmospheric dicarboxylic acids in ground region and free troposphere M. Shen et al. https://doi.org/10.5194/acp-25-16147-2025
8 citations as recorded by crossref.
- Constructing the 3D spatial distribution of PM2.5 concentrations during the 2022 Beijing Winter Olympics using LiDAR vertical observation networks and machine learning models Z. Wang et al. https://doi.org/10.1016/j.envint.2025.109875
- Opposing trends in the peak and low ozone concentrations in eastern China: anthropogenic and meteorological influences Z. Wang et al. https://doi.org/10.5194/acp-25-347-2025
- Airborne particulate matter reduction through developed dust control systems for wheat thresher T. Mehmood et al. https://doi.org/10.1007/s11869-025-01831-3
- Spatial and temporal distribution of MODIS aerosol optical depth over Ghana and comparison with AERONET data H. Affum et al. https://doi.org/10.1080/27658511.2025.2593743
- Vertical distribution of aerosols and association with atmospheric boundary layer structures during regional aerosol transport over central China X. Sun et al. https://doi.org/10.1016/j.envpol.2024.124967
- The evolution of aerosol mixing state derived from a field campaign in Beijing: implications for particle aging timescales in urban atmospheres J. Liu et al. https://doi.org/10.5194/acp-25-5075-2025
- K-nearest neighbors approximation for the multiwavelength lidar retrieval of bimodal aerosol microphysical properties Z. Chen et al. https://doi.org/10.1364/AO.577119
- Measurement report: Observational insights into the impact of dust transport on atmospheric dicarboxylic acids in ground region and free troposphere M. Shen et al. https://doi.org/10.5194/acp-25-16147-2025
Saved (final revised paper)
Latest update: 27 Jul 2026
Short summary
The annual cycle of dust and anthropogenic aerosols' vertical distributions was revealed by polarization Raman lidar in Beijing. Anthropogenic aerosols typically accumulate at the top of the mixing layer (ML) due to the hygroscopic growth of atmospheric particles, and this is most significant in summer. There is no significant relationship between bottom dust mass concentration and ML height, while the dust in the upper air tends to be distributed near the mixing layer.
The annual cycle of dust and anthropogenic aerosols' vertical distributions was revealed by...
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