Articles | Volume 21, issue 9
https://doi.org/10.5194/acp-21-7023-2021
© 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/acp-21-7023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 May 2021
Research article |
| 07 May 2021
| 07 May 2021
Lidar vertical observation network and data assimilation reveal key processes driving the 3-D dynamic evolution of PM2.5 concentrations over the North China Plain
Institutes of Physical Science and Information Technology, Anhui
University, Hefei 230601, China
Tianshu Zhang
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
Institutes of Physical Science and Information Technology, Anhui
University, Hefei 230601, China
Chaoqun Ma
Minerva Research Group, Max Planck Institute for Chemistry, Mainz,
Germany
Lihui Lv
Institutes of Physical Science and Information Technology, Anhui
University, Hefei 230601, China
Jianguo Liu
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
Wenqing Liu
Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
Institutes of Physical Science and Information Technology, Anhui
University, Hefei 230601, China
Minerva Research Group, Max Planck Institute for Chemistry, Mainz,
Germany
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Cited
32 citations as recorded by crossref.
- Application of data assimilation technology in source apportionment of PM2.5 during winter haze episodes in the Beijing-Tianjin-Hebei region in China T. Sun et al. https://doi.org/10.1016/j.apr.2022.101546
- Hyperspectral remote sensing for air pollutants: Stereoscopic monitoring, source localization & warning, and a dynamic emission inventory concept C. Xing et al. https://doi.org/10.1016/j.envint.2025.109375
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. https://doi.org/10.1016/j.atmosenv.2021.118828
- Needs and challenges of optical atmospheric monitoring on the background of carbon neutrality in China W. Liu & C. Xing https://doi.org/10.1007/s11783-024-1833-2
- Deep learning-driven reconstruction of PM2.5 vertical profiles: A fusion of lidar and tower data Z. Yi et al. https://doi.org/10.1016/j.jclepro.2025.145397
- Seasonal patterns, vertical profiles, and sensitivity analysis of long-term O3 pollution observations in Hefei City, China X. Zhao et al. https://doi.org/10.1016/j.apr.2024.102145
- Novel Insights into the Vertical Distribution Patterns of Multiple PM2.5 Components in a Super Mega-City: Responses to Pollution Control Strategies Y. Song et al. https://doi.org/10.3390/rs17071151
- Practices and Empirical Insights from the National Research Program for Key Issues in Air Pollution in Beijing–Tianjin–Hebei and Surrounding Areas H. Li et al. https://doi.org/10.1016/j.eng.2022.08.021
- Vertical distribution and transport characteristics of ozone pollution based on lidar observation network and data assimilation over the Pearl River Delta, China Y. Pan et al. https://doi.org/10.1016/j.atmosres.2024.107643
- Transport characteristics of urban and rural aerosols based on vertical observations and deep learning predictions T. Zou et al. https://doi.org/10.1016/j.atmosres.2024.107876
- Transparent and Robust Superhydrophobic Coatings for High-Fidelity Signal Transmission in Atmospheric Light Detection and Ranging Windows J. Chen et al. https://doi.org/10.1021/acs.langmuir.5c03890
- Vertical Changes of Pm2.5 Concentration Driven by Meteorology in the Atmospheric Boundary Layer During a Heavy Air Pollution Event in Central China X. Sun et al. https://doi.org/10.2139/ssrn.4135986
- Investigation on the vertical distribution and transportation of PM2.5 in the Beijing-Tianjin-Hebei region based on stereoscopic observation network T. Sun et al. https://doi.org/10.1016/j.atmosenv.2022.119511
- Vertical profiles and regional transport of ozone in typical area of Yangtze-Huaihe River Basin during the autumn base on multiple lidars X. Wang et al. https://doi.org/10.1016/j.apr.2023.101983
- Downdraft influences on the differences of PM2.5 concentration: insights from a mega haze evolution in the winter of northern China Z. Chen et al. https://doi.org/10.1088/1748-9326/ad1229
- Revealing ozone persistence under typhoon subsidence in the Greater Bay Area using lidar observations R. He et al. https://doi.org/10.1016/j.atmosres.2026.108737
- Unveiling 3-D evolution and mechanisms of ozone pollution in Changzhou, China: Insights from lidar observations and modelling Z. Liu et al. https://doi.org/10.1016/j.envpol.2024.124556
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. https://doi.org/10.1016/j.envint.2024.108430
- PM2.5 and PM10 vertical distribution retrieval methods based on coherent Doppler lidar via machine learning: for haze and dust in Qingdao R. Li et al. https://doi.org/10.1016/j.atmosenv.2025.121351
- Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. https://doi.org/10.1016/j.atmosres.2022.106069
- Insights into quantitative evaluation technology of PM2.5 transport at multi–perspective and multi–spatial and temporal scales in the north China plain H. Zhang et al. https://doi.org/10.1016/j.envpol.2023.122693
- Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms Y. Ma et al. https://doi.org/10.5194/acp-21-17003-2021
- 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
- Vertical changes of PM2.5 driven by meteorology in the atmospheric boundary layer during a heavy air pollution event in central China X. Sun et al. https://doi.org/10.1016/j.scitotenv.2022.159830
- Source, transport and impact of biomass burning emissions in spring 2024 in Yunnan, China L. Lv et al. https://doi.org/10.1016/j.jes.2025.10.040
- Shipborne lidar measurements of ozone over the southeastern coastal regions of China in winter L. Lv et al. https://doi.org/10.1016/j.envres.2025.121165
- Lidar-Derived Atmospheric Boundary Layer Height Trends Over North China Plain from 2013 to 2018: Relationship to PM2.5 and Spatiotemporal Changes L. Lv et al. https://doi.org/10.1007/s11270-025-08391-3
- Vertical profile of aerosols in the Himalayas revealed by lidar: New insights into their seasonal/diurnal patterns, sources, and transport Y. Xiang et al. https://doi.org/10.1016/j.envpol.2021.117686
- Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations Y. Pan et al. https://doi.org/10.1016/j.atmosenv.2024.120762
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. https://doi.org/10.1016/j.jes.2022.04.012
- Evaluation of transport processes over North China Plain and Yangtze River Delta using MAX-DOAS observations Y. Song et al. https://doi.org/10.5194/acp-23-1803-2023
- 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
32 citations as recorded by crossref.
- Application of data assimilation technology in source apportionment of PM2.5 during winter haze episodes in the Beijing-Tianjin-Hebei region in China T. Sun et al. https://doi.org/10.1016/j.apr.2022.101546
- Hyperspectral remote sensing for air pollutants: Stereoscopic monitoring, source localization & warning, and a dynamic emission inventory concept C. Xing et al. https://doi.org/10.1016/j.envint.2025.109375
- Three-dimensional variational assimilation of Lidar extinction profiles: Application to PM2.5 prediction in north China L. Gao et al. https://doi.org/10.1016/j.atmosenv.2021.118828
- Needs and challenges of optical atmospheric monitoring on the background of carbon neutrality in China W. Liu & C. Xing https://doi.org/10.1007/s11783-024-1833-2
- Deep learning-driven reconstruction of PM2.5 vertical profiles: A fusion of lidar and tower data Z. Yi et al. https://doi.org/10.1016/j.jclepro.2025.145397
- Seasonal patterns, vertical profiles, and sensitivity analysis of long-term O3 pollution observations in Hefei City, China X. Zhao et al. https://doi.org/10.1016/j.apr.2024.102145
- Novel Insights into the Vertical Distribution Patterns of Multiple PM2.5 Components in a Super Mega-City: Responses to Pollution Control Strategies Y. Song et al. https://doi.org/10.3390/rs17071151
- Practices and Empirical Insights from the National Research Program for Key Issues in Air Pollution in Beijing–Tianjin–Hebei and Surrounding Areas H. Li et al. https://doi.org/10.1016/j.eng.2022.08.021
- Vertical distribution and transport characteristics of ozone pollution based on lidar observation network and data assimilation over the Pearl River Delta, China Y. Pan et al. https://doi.org/10.1016/j.atmosres.2024.107643
- Transport characteristics of urban and rural aerosols based on vertical observations and deep learning predictions T. Zou et al. https://doi.org/10.1016/j.atmosres.2024.107876
- Transparent and Robust Superhydrophobic Coatings for High-Fidelity Signal Transmission in Atmospheric Light Detection and Ranging Windows J. Chen et al. https://doi.org/10.1021/acs.langmuir.5c03890
- Vertical Changes of Pm2.5 Concentration Driven by Meteorology in the Atmospheric Boundary Layer During a Heavy Air Pollution Event in Central China X. Sun et al. https://doi.org/10.2139/ssrn.4135986
- Investigation on the vertical distribution and transportation of PM2.5 in the Beijing-Tianjin-Hebei region based on stereoscopic observation network T. Sun et al. https://doi.org/10.1016/j.atmosenv.2022.119511
- Vertical profiles and regional transport of ozone in typical area of Yangtze-Huaihe River Basin during the autumn base on multiple lidars X. Wang et al. https://doi.org/10.1016/j.apr.2023.101983
- Downdraft influences on the differences of PM2.5 concentration: insights from a mega haze evolution in the winter of northern China Z. Chen et al. https://doi.org/10.1088/1748-9326/ad1229
- Revealing ozone persistence under typhoon subsidence in the Greater Bay Area using lidar observations R. He et al. https://doi.org/10.1016/j.atmosres.2026.108737
- Unveiling 3-D evolution and mechanisms of ozone pollution in Changzhou, China: Insights from lidar observations and modelling Z. Liu et al. https://doi.org/10.1016/j.envpol.2024.124556
- A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023 X. Ma et al. https://doi.org/10.1016/j.envint.2024.108430
- PM2.5 and PM10 vertical distribution retrieval methods based on coherent Doppler lidar via machine learning: for haze and dust in Qingdao R. Li et al. https://doi.org/10.1016/j.atmosenv.2025.121351
- Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China H. Ye et al. https://doi.org/10.1016/j.atmosres.2022.106069
- Insights into quantitative evaluation technology of PM2.5 transport at multi–perspective and multi–spatial and temporal scales in the north China plain H. Zhang et al. https://doi.org/10.1016/j.envpol.2023.122693
- Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms Y. Ma et al. https://doi.org/10.5194/acp-21-17003-2021
- 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
- Vertical changes of PM2.5 driven by meteorology in the atmospheric boundary layer during a heavy air pollution event in central China X. Sun et al. https://doi.org/10.1016/j.scitotenv.2022.159830
- Source, transport and impact of biomass burning emissions in spring 2024 in Yunnan, China L. Lv et al. https://doi.org/10.1016/j.jes.2025.10.040
- Shipborne lidar measurements of ozone over the southeastern coastal regions of China in winter L. Lv et al. https://doi.org/10.1016/j.envres.2025.121165
- Lidar-Derived Atmospheric Boundary Layer Height Trends Over North China Plain from 2013 to 2018: Relationship to PM2.5 and Spatiotemporal Changes L. Lv et al. https://doi.org/10.1007/s11270-025-08391-3
- Vertical profile of aerosols in the Himalayas revealed by lidar: New insights into their seasonal/diurnal patterns, sources, and transport Y. Xiang et al. https://doi.org/10.1016/j.envpol.2021.117686
- Vertical structure and transport characteristic of aerosol and O3 during the emergency control period in Wuhan, China, using vehicle-lidar observations Y. Pan et al. https://doi.org/10.1016/j.atmosenv.2024.120762
- Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook T. Yang et al. https://doi.org/10.1016/j.jes.2022.04.012
- Evaluation of transport processes over North China Plain and Yangtze River Delta using MAX-DOAS observations Y. Song et al. https://doi.org/10.5194/acp-23-1803-2023
- 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
Saved (final revised paper)
Latest update: 28 May 2026
Short summary
For the first time, a vertical observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations were combined with a data assimilation technique to reveal key processes driving the 3-D dynamic evolution of PM2.5 concentrations during extreme heavy aerosol pollution on the North China Plain.
For the first time, a vertical observation network consisting of 13 aerosol lidars and more than...
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