Articles | Volume 21, issue 22
Atmos. Chem. Phys., 21, 17003–17016, 2021
https://doi.org/10.5194/acp-21-17003-2021
Atmos. Chem. Phys., 21, 17003–17016, 2021
https://doi.org/10.5194/acp-21-17003-2021
Research article
24 Nov 2021
Research article | 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 et al.

Related authors

Assessment of the wind energy resource on the coast of China based on machine learning algorithms
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-634,https://doi.org/10.5194/acp-2022-634, 2022
Preprint under review for ACP
Short summary
Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm
Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong
Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022,https://doi.org/10.5194/amt-15-4323-2022, 2022
Short summary
Intercomparison of wind observations from ESA’s satellite mission Aeolus, ERA5 reanalysis and radiosonde over China
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26,https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Evaluation of retrieval methods for planetary boundary layer height based on radiosonde data
Hui Li, Boming Liu, Xin Ma, Shikuan Jin, Yingying Ma, Yuefeng Zhao, and Wei Gong
Atmos. Meas. Tech., 14, 5977–5986, https://doi.org/10.5194/amt-14-5977-2021,https://doi.org/10.5194/amt-14-5977-2021, 2021
Short summary
Technical note: First comparison of wind observations from ESA's satellite mission Aeolus and ground-based radar wind profiler network of China
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021,https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary

Related subject area

Subject: Aerosols | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
South American 2020 regional smoke plume: intercomparison with previous years, impact on solar radiation, and the role of Pantanal biomass burning season
Nilton Évora do Rosário, Elisa Thomé Sena, and Marcia Akemi Yamasoe
Atmos. Chem. Phys., 22, 15021–15033, https://doi.org/10.5194/acp-22-15021-2022,https://doi.org/10.5194/acp-22-15021-2022, 2022
Short summary
Circular polarization in atmospheric aerosols
Santiago Gassó and Kirk D. Knobelspiesse
Atmos. Chem. Phys., 22, 13581–13605, https://doi.org/10.5194/acp-22-13581-2022,https://doi.org/10.5194/acp-22-13581-2022, 2022
Short summary
Spatiotemporal continuous estimates of daily 1 km PM2.5 from 2000 to present under the Tracking Air Pollution in China (TAP) framework
Qingyang Xiao, Guannan Geng, Shigan Liu, Jiajun Liu, Xia Meng, and Qiang Zhang
Atmos. Chem. Phys., 22, 13229–13242, https://doi.org/10.5194/acp-22-13229-2022,https://doi.org/10.5194/acp-22-13229-2022, 2022
Short summary
Aerosol optical depth regime over Megacities of the world
Kyriakoula Papachristopoulou, Ioannis-Panagiotis Raptis, Antonis Gkikas, Ilias Fountoulakis, Akriti Masoom, and Stelios Kazadzis
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-617,https://doi.org/10.5194/acp-2022-617, 2022
Revised manuscript accepted for ACP
Short summary
Robust evidence for reversal of the trend in aerosol effective climate forcing
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022,https://doi.org/10.5194/acp-22-12221-2022, 2022
Short summary

Cited articles

Altman, N. S.: An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat., 46, 175–185, 1992. 
Belmonte Rivas, M. and Stoffelen, A.: Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT, Ocean Sci., 15, 831–852, https://doi.org/10.5194/os-15-831-2019, 2019. 
Boyouk, N., Léon, J. F., Delbarre, H., Podvin, T., and Deroo, C.: Impact of the mixing boundary layer on the relationship between PM2.5 and aerosol optical thickness, Atmos. Environ., 44, 271–277, 2010. 
Breiman, L.: Random forests, in: Machine Learning, 45, 5–32, 2001. 
Cao, L.: Support vector machines experts for time series forecasting, Neurocomputing, 51, 321–339, 2003. 
Download
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.
Altmetrics
Final-revised paper
Preprint