Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data
The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identifi...
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ftdoajarticles:oai:doaj.org/article:fcae64b1c4684ccda92db3ab372ea71a 2024-09-15T17:35:13+00:00 Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data X. Wei Q. Cui L. Ma F. Zhang W. Li P. Liu 2024-04-01T00:00:00Z https://doi.org/10.5194/acp-24-5025-2024 https://doaj.org/article/fcae64b1c4684ccda92db3ab372ea71a EN eng Copernicus Publications https://acp.copernicus.org/articles/24/5025/2024/acp-24-5025-2024.pdf https://doaj.org/toc/1680-7316 https://doaj.org/toc/1680-7324 doi:10.5194/acp-24-5025-2024 1680-7316 1680-7324 https://doaj.org/article/fcae64b1c4684ccda92db3ab372ea71a Atmospheric Chemistry and Physics, Vol 24, Pp 5025-5045 (2024) Physics QC1-999 Chemistry QD1-999 article 2024 ftdoajarticles https://doi.org/10.5194/acp-24-5025-2024 2024-08-05T17:49:28Z The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Atmospheric Chemistry and Physics 24 8 5025 5045 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
Physics QC1-999 Chemistry QD1-999 X. Wei Q. Cui L. Ma F. Zhang W. Li P. Liu Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
topic_facet |
Physics QC1-999 Chemistry QD1-999 |
description |
The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution. |
format |
Article in Journal/Newspaper |
author |
X. Wei Q. Cui L. Ma F. Zhang W. Li P. Liu |
author_facet |
X. Wei Q. Cui L. Ma F. Zhang W. Li P. Liu |
author_sort |
X. Wei |
title |
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
title_short |
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
title_full |
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
title_fullStr |
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
title_full_unstemmed |
Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data |
title_sort |
global aerosol-type classification using a new hybrid algorithm and aerosol robotic network data |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/acp-24-5025-2024 https://doaj.org/article/fcae64b1c4684ccda92db3ab372ea71a |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmospheric Chemistry and Physics, Vol 24, Pp 5025-5045 (2024) |
op_relation |
https://acp.copernicus.org/articles/24/5025/2024/acp-24-5025-2024.pdf https://doaj.org/toc/1680-7316 https://doaj.org/toc/1680-7324 doi:10.5194/acp-24-5025-2024 1680-7316 1680-7324 https://doaj.org/article/fcae64b1c4684ccda92db3ab372ea71a |
op_doi |
https://doi.org/10.5194/acp-24-5025-2024 |
container_title |
Atmospheric Chemistry and Physics |
container_volume |
24 |
container_issue |
8 |
container_start_page |
5025 |
op_container_end_page |
5045 |
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1810445760527859712 |