Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data
<p>The vertical distribution of atmospheric aerosol components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Comb...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-10-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/15/6127/2022/amt-15-6127-2022.pdf |
| _version_ | 1798030836473266176 |
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| author | F. Wang F. Wang T. Yang T. Yang Z. Wang Z. Wang Z. Wang H. Wang H. Wang X. Chen X. Chen Y. Sun Y. Sun J. Li G. Tang W. Chai |
| author_facet | F. Wang F. Wang T. Yang T. Yang Z. Wang Z. Wang Z. Wang H. Wang H. Wang X. Chen X. Chen Y. Sun Y. Sun J. Li G. Tang W. Chai |
| author_sort | F. Wang |
| collection | DOAJ |
| description | <p>The vertical distribution of atmospheric aerosol
components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC), this paper developed a new algorithm to get the vertical mass concentration profiles of fine-mode aerosol components for the first time. Retrieval of aerosol properties was achieved based on the sky radiance at multiple scatter angles, total optical depth (TOD) at 440, 675, 870, and 1020 nm, and lidar signals at 532 and 1064 nm. In addition, the internal mixing model and normalized volume size distribution (VSD) model were established according to the absorption and water solubility of the aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water (AW) content. Results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentrations matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of organic matter (OM <span class="inline-formula">=</span> WIOM <span class="inline-formula">+</span> WSOM, NMB <span class="inline-formula">=</span> 0.98). Moreover, the retrieved AN concentrations were closer to the simulated results (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">=</span> 0.85), especially in polluted conditions. The BC and OM correlations were relatively weaker, with a correlation coefficient
of <span class="inline-formula">∼</span> 0.5. Besides, the uncertainties caused by the input parameters (i.e., relative humidity (RH), volume concentration, and extinction coefficients) were
assessed using the Monte Carlo method. The AN and AW had smaller uncertainties at higher RH. Herein, the proposed algorithm was also applied to remote-sensing measurements in Beijing with two typical cases. In the clean condition with low RH, there were comparable AN and WIOM, but peaking at different altitudes. On the other hand, in the polluted case, AN was
dominant and the maximum mass concentration occurred near the surface. We
expected that the algorithm could provide a new idea for lidar inversion and promote the development of aerosol component profiles.</p> |
| first_indexed | 2024-04-11T19:46:35Z |
| format | Article |
| id | doaj.art-fa347f74e3ae4102ba9d45428b0e533b |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-11T19:46:35Z |
| publishDate | 2022-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-fa347f74e3ae4102ba9d45428b0e533b2022-12-22T04:06:28ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482022-10-01156127614410.5194/amt-15-6127-2022Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing dataF. Wang0F. Wang1T. Yang2T. Yang3Z. Wang4Z. Wang5Z. Wang6H. Wang7H. Wang8X. Chen9X. Chen10Y. Sun11Y. Sun12J. Li13G. Tang14W. Chai15State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCenter for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaCenter for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, ChinaCollege of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, 100049, ChinaChina National Environmental Monitoring Center, Beijing, ChinaChina National Environmental Monitoring Center, Beijing, ChinaChina National Environmental Monitoring Center, Beijing, China<p>The vertical distribution of atmospheric aerosol components is vital to the estimation of radiative forcing and the catalysis of atmospheric photochemical processes. Based on the synergy of ground-based lidar and sun-photometer in Generalized Aerosol Retrieval from Radiometer and Lidar Combined data (GARRLiC), this paper developed a new algorithm to get the vertical mass concentration profiles of fine-mode aerosol components for the first time. Retrieval of aerosol properties was achieved based on the sky radiance at multiple scatter angles, total optical depth (TOD) at 440, 675, 870, and 1020 nm, and lidar signals at 532 and 1064 nm. In addition, the internal mixing model and normalized volume size distribution (VSD) model were established according to the absorption and water solubility of the aerosol components, to separate the profiles of black carbon (BC), water-insoluble organic matter (WIOM), water-soluble organic matter (WSOM), ammonium nitrate-like (AN), and fine aerosol water (AW) content. Results showed a reasonable vertical distribution of aerosol components compared with in situ observations and reanalysis data. The estimated and observed BC concentrations matched well with a correlation coefficient up to 0.91, while there was an evident overestimation of organic matter (OM <span class="inline-formula">=</span> WIOM <span class="inline-formula">+</span> WSOM, NMB <span class="inline-formula">=</span> 0.98). Moreover, the retrieved AN concentrations were closer to the simulated results (<span class="inline-formula"><i>R</i></span> <span class="inline-formula">=</span> 0.85), especially in polluted conditions. The BC and OM correlations were relatively weaker, with a correlation coefficient of <span class="inline-formula">∼</span> 0.5. Besides, the uncertainties caused by the input parameters (i.e., relative humidity (RH), volume concentration, and extinction coefficients) were assessed using the Monte Carlo method. The AN and AW had smaller uncertainties at higher RH. Herein, the proposed algorithm was also applied to remote-sensing measurements in Beijing with two typical cases. In the clean condition with low RH, there were comparable AN and WIOM, but peaking at different altitudes. On the other hand, in the polluted case, AN was dominant and the maximum mass concentration occurred near the surface. We expected that the algorithm could provide a new idea for lidar inversion and promote the development of aerosol component profiles.</p>https://amt.copernicus.org/articles/15/6127/2022/amt-15-6127-2022.pdf |
| spellingShingle | F. Wang F. Wang T. Yang T. Yang Z. Wang Z. Wang Z. Wang H. Wang H. Wang X. Chen X. Chen Y. Sun Y. Sun J. Li G. Tang W. Chai Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data Atmospheric Measurement Techniques |
| title | Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data |
| title_full | Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data |
| title_fullStr | Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data |
| title_full_unstemmed | Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data |
| title_short | Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data |
| title_sort | algorithm for vertical distribution of boundary layer aerosol components in remote sensing data |
| url | https://amt.copernicus.org/articles/15/6127/2022/amt-15-6127-2022.pdf |
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