Articles | Volume 21, issue 22
https://doi.org/10.5194/acp-21-17003-2021
© 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-21-17003-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2021
Research article |
| 24 Nov 2021
| 24 Nov 2021
Estimation of the vertical distribution of particle matter (PM2.5) concentration and its transport flux from lidar measurements based on machine learning algorithms
Yingying Ma
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Yang Zhu
School of Computer Science and Technology, Wuhan University of
Science and Technology, Wuhan, China
Boming Liu
CORRESPONDING AUTHOR
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Hui Li
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Shikuan Jin
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Yiqun Zhang
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Ruonan Fan
State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Wei Gong
CORRESPONDING AUTHOR
School of Electronic Information, Wuhan University, Wuhan, China
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Cited
26 citations as recorded by crossref.
- The covariability between temperature inversions and aerosol vertical distribution over China Z. Zhu et al. 10.1016/j.apr.2023.101959
- Profiling of particulate matter transport flux based on dual-wavelength lidar and ensemble learning algorithm R. Li et al. 10.1364/OE.522165
- Quasi-weekly oscillation of regional PM2.5 transport over China driven by the synoptic-scale disturbance of the East Asian winter monsoon circulation Y. Bai et al. 10.5194/acp-25-1273-2025
- 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. 10.1016/j.atmosenv.2025.121351
- Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations H. Shao et al. 10.3390/rs15112742
- Mixture Regression for Clustering Atmospheric-Sounding Data: A Study of the Relationship between Temperature Inversions and PM10 Concentrations P. Mlakar & J. Faganeli Pucer 10.3390/atmos14030481
- 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. 10.1016/j.atmosenv.2022.119511
- Mega Asian dust event over China on 27–31 March 2021 observed with space-borne instruments and ground-based polarization lidar Y. He et al. 10.1016/j.atmosenv.2022.119238
- 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. 10.1088/1748-9326/ad1229
- Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning M. Lu & C. Gao 10.3390/atmos15111356
- Changes in physical and chemical properties of urban atmospheric aerosols and ozone during the COVID-19 lockdown in a semi-arid region Y. Chang et al. 10.1016/j.atmosenv.2022.119270
- Estimation of Aerosol Extinction Coefficient Using Camera Images and Application in Mass Extinction Efficiency Retrieval J. Shin et al. 10.3390/rs14051224
- Extending the wind profile beyond the surface layer by combining physical and machine learning approaches B. Liu et al. 10.5194/acp-24-4047-2024
- Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment B. Liu et al. 10.5194/acp-23-3181-2023
- The relationship between atmospheric boundary layer and temperature inversion layer and their aerosol capture capabilities B. Liu et al. 10.1016/j.atmosres.2022.106121
- Deep learning-driven reconstruction of PM2.5 vertical profiles: A fusion of lidar and tower data Z. Yi et al. 10.1016/j.jclepro.2025.145397
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Machine Learning Predicts Emissions of Brake Wear PM2.5: Model Construction and Interpretation N. Wei et al. 10.1021/acs.estlett.2c00117
- Detection of Atmospheric Wind Speed by Lidar Based on Quadrichannel Mach–Zehnder Interferometer J. Li et al. 10.3390/photonics10070726
- Comprehensive understanding on sources of high levels of fine particulate nitro-aromatic compounds at a coastal rural area in northern China Y. Jiang et al. 10.1016/j.jes.2022.09.033
- Constructing the 3D Spatial Distribution of the HCHO/NO2 Ratio via Satellite Observation and Machine Learning Model Z. Jiang et al. 10.1021/acs.est.4c12362
- Investigation of Vertical Profiles of Particulate Matter and Meteorological Variables up to 2.5 km in Altitude Using a Drone-Based Monitoring System W. Kim et al. 10.3390/atmos16010093
- Lidar- and UAV-Based Vertical Observation of Spring Ozone and Particulate Matter in Nanjing, China Y. Qu et al. 10.3390/rs14133051
- Increasing importance of regional emission controls for further reduction of PM2.5 in Beijing L. Dong et al. 10.1016/j.uclim.2025.102437
- The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region S. Mohyuddin et al. 10.1016/j.atmosenv.2022.119388
- Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm S. Jin et al. 10.5194/amt-15-4323-2022
26 citations as recorded by crossref.
- The covariability between temperature inversions and aerosol vertical distribution over China Z. Zhu et al. 10.1016/j.apr.2023.101959
- Profiling of particulate matter transport flux based on dual-wavelength lidar and ensemble learning algorithm R. Li et al. 10.1364/OE.522165
- Quasi-weekly oscillation of regional PM2.5 transport over China driven by the synoptic-scale disturbance of the East Asian winter monsoon circulation Y. Bai et al. 10.5194/acp-25-1273-2025
- 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. 10.1016/j.atmosenv.2025.121351
- Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations H. Shao et al. 10.3390/rs15112742
- Mixture Regression for Clustering Atmospheric-Sounding Data: A Study of the Relationship between Temperature Inversions and PM10 Concentrations P. Mlakar & J. Faganeli Pucer 10.3390/atmos14030481
- 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. 10.1016/j.atmosenv.2022.119511
- Mega Asian dust event over China on 27–31 March 2021 observed with space-borne instruments and ground-based polarization lidar Y. He et al. 10.1016/j.atmosenv.2022.119238
- 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. 10.1088/1748-9326/ad1229
- Enhancing Fine Aerosol Simulations in the Remote Atmosphere with Machine Learning M. Lu & C. Gao 10.3390/atmos15111356
- Changes in physical and chemical properties of urban atmospheric aerosols and ozone during the COVID-19 lockdown in a semi-arid region Y. Chang et al. 10.1016/j.atmosenv.2022.119270
- Estimation of Aerosol Extinction Coefficient Using Camera Images and Application in Mass Extinction Efficiency Retrieval J. Shin et al. 10.3390/rs14051224
- Extending the wind profile beyond the surface layer by combining physical and machine learning approaches B. Liu et al. 10.5194/acp-24-4047-2024
- Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment B. Liu et al. 10.5194/acp-23-3181-2023
- The relationship between atmospheric boundary layer and temperature inversion layer and their aerosol capture capabilities B. Liu et al. 10.1016/j.atmosres.2022.106121
- Deep learning-driven reconstruction of PM2.5 vertical profiles: A fusion of lidar and tower data Z. Yi et al. 10.1016/j.jclepro.2025.145397
- Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach C. Lei et al. 10.1109/TGRS.2022.3213797
- Machine Learning Predicts Emissions of Brake Wear PM2.5: Model Construction and Interpretation N. Wei et al. 10.1021/acs.estlett.2c00117
- Detection of Atmospheric Wind Speed by Lidar Based on Quadrichannel Mach–Zehnder Interferometer J. Li et al. 10.3390/photonics10070726
- Comprehensive understanding on sources of high levels of fine particulate nitro-aromatic compounds at a coastal rural area in northern China Y. Jiang et al. 10.1016/j.jes.2022.09.033
- Constructing the 3D Spatial Distribution of the HCHO/NO2 Ratio via Satellite Observation and Machine Learning Model Z. Jiang et al. 10.1021/acs.est.4c12362
- Investigation of Vertical Profiles of Particulate Matter and Meteorological Variables up to 2.5 km in Altitude Using a Drone-Based Monitoring System W. Kim et al. 10.3390/atmos16010093
- Lidar- and UAV-Based Vertical Observation of Spring Ozone and Particulate Matter in Nanjing, China Y. Qu et al. 10.3390/rs14133051
- Increasing importance of regional emission controls for further reduction of PM2.5 in Beijing L. Dong et al. 10.1016/j.uclim.2025.102437
- The influence and contribution of fine mode particles to aerosol optical properties during haze events at the foothills of Himalaya-Karakorum region S. Mohyuddin et al. 10.1016/j.atmosenv.2022.119388
- Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm S. Jin et al. 10.5194/amt-15-4323-2022
Latest update: 13 Jul 2025
Short summary
The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems has been used to retrieve the profile of particle matter with a diameter of less than 2.5 μm (PM2.5). However, the traditional linear model cannot consider the influence of multiple meteorological variables sufficiently, which then causes low inversion accuracy. In this study, the machine learning algorithms which can input multiple features are used to solve this constraint.
The vertical distribution of the aerosol extinction coefficient (EC) measured by lidar systems...
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