AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)
<jats:p>Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents th...
Published in: | Frontiers in Environmental Science |
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Online Access: | https://livrepository.liverpool.ac.uk/3165213/ https://doi.org/10.3389/fenvs.2022.981522 https://livrepository.liverpool.ac.uk/3165213/1/FENVS-2022-AEROsol%20generic%20classification%20using%20a%20novel.pdf |
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ftunivliverpool:oai:livrepository.liverpool.ac.uk:3165213 2024-09-15T17:35:16+00:00 AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) Bilal, Muhammad Ali, Md Arfan Nichol, Janet E Bleiweiss, Max P de Leeuw, Gerrit Mhawish, Alaa Shi, Yuan Mazhar, Usman Mehmood, Tariq Kim, Jhoon Qiu, Zhongfeng Qin, Wenmin Nazeer, Majid 2022 application/pdf https://livrepository.liverpool.ac.uk/3165213/ https://doi.org/10.3389/fenvs.2022.981522 https://livrepository.liverpool.ac.uk/3165213/1/FENVS-2022-AEROsol%20generic%20classification%20using%20a%20novel.pdf en eng Frontiers Media SA https://livrepository.liverpool.ac.uk/3165213/1/FENVS-2022-AEROsol%20generic%20classification%20using%20a%20novel.pdf Collapse authors list. Bilal, Muhammad, Ali, Md Arfan, Nichol, Janet E, Bleiweiss, Max P, de Leeuw, Gerrit, Mhawish, Alaa, Shi, Yuan orcid:0000-0003-4011-8735 , Mazhar, Usman, Mehmood, Tariq, Kim, Jhoon et al (show 3 more authors) , Qiu, Zhongfeng, Qin, Wenmin and Nazeer, Majid (2022) AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA). FRONTIERS IN ENVIRONMENTAL SCIENCE, 10. 981522-. Article NonPeerReviewed 2022 ftunivliverpool https://doi.org/10.3389/fenvs.2022.981522 2024-07-08T14:17:39Z <jats:p>Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20<jats:sup>th</jats:sup> March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, ... Article in Journal/Newspaper Aerosol Robotic Network The University of Liverpool Repository Frontiers in Environmental Science 10 |
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Open Polar |
collection |
The University of Liverpool Repository |
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ftunivliverpool |
language |
English |
description |
<jats:p>Numerous studies (hereafter GA: general approach studies) have been made to classify aerosols into desert dust (DD), biomass-burning (BB), clean continental (CC), and clean maritime (CM) types using only aerosol optical depth (AOD) and Ångström exponent (AE). However, AOD represents the amount of aerosol suspended in the atmospheric column while the AE is a qualitative indicator of the size distribution of the aerosol estimated using AOD measurements at different wavelengths. Therefore, these two parameters do not provide sufficient information to unambiguously classify aerosols into these four types. Evaluation of the performance of GA classification applied to AErosol Robotic NETwork (AERONET) data, at sites for situations with known aerosol types, provides many examples where the GA method does not provide correct results. For example, a thin layer of haze was classified as BB and DD outside the crop burning and dusty seasons respectively, a thick layer of haze was classified as BB, and aerosols from known crop residue burning events were classified as DD, CC, and CM by the GA method. The results also show that the classification varies with the season, for example, the same range of AOD and AE were observed during a dust event in the spring (20<jats:sup>th</jats:sup> March 2012) and a smog event in the autumn (2nd November 2017). The results suggest that only AOD and AE cannot precisely classify the exact nature (i.e., DD, BB, CC, and CM) of aerosol types without incorporating more optical and physical properties. An alternative approach, AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA), is proposed to provide aerosol amount and size information using AOD and AE, respectively, from the Terra-MODIS (MODerate resolution Imaging Spectroradiometer) Collection 6.1 Level 2 combined Dark Target and Deep Blue (DTB) product and AERONET Version 3 Level 2.0 data. Although AEROSA is also based on AOD and AE, it does not claim the nature of aerosol types, ... |
format |
Article in Journal/Newspaper |
author |
Bilal, Muhammad Ali, Md Arfan Nichol, Janet E Bleiweiss, Max P de Leeuw, Gerrit Mhawish, Alaa Shi, Yuan Mazhar, Usman Mehmood, Tariq Kim, Jhoon Qiu, Zhongfeng Qin, Wenmin Nazeer, Majid |
spellingShingle |
Bilal, Muhammad Ali, Md Arfan Nichol, Janet E Bleiweiss, Max P de Leeuw, Gerrit Mhawish, Alaa Shi, Yuan Mazhar, Usman Mehmood, Tariq Kim, Jhoon Qiu, Zhongfeng Qin, Wenmin Nazeer, Majid AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
author_facet |
Bilal, Muhammad Ali, Md Arfan Nichol, Janet E Bleiweiss, Max P de Leeuw, Gerrit Mhawish, Alaa Shi, Yuan Mazhar, Usman Mehmood, Tariq Kim, Jhoon Qiu, Zhongfeng Qin, Wenmin Nazeer, Majid |
author_sort |
Bilal, Muhammad |
title |
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
title_short |
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
title_full |
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
title_fullStr |
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
title_full_unstemmed |
AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA) |
title_sort |
aerosol generic classification using a novel satellite remote sensing approach (aerosa) |
publisher |
Frontiers Media SA |
publishDate |
2022 |
url |
https://livrepository.liverpool.ac.uk/3165213/ https://doi.org/10.3389/fenvs.2022.981522 https://livrepository.liverpool.ac.uk/3165213/1/FENVS-2022-AEROsol%20generic%20classification%20using%20a%20novel.pdf |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
https://livrepository.liverpool.ac.uk/3165213/1/FENVS-2022-AEROsol%20generic%20classification%20using%20a%20novel.pdf Collapse authors list. Bilal, Muhammad, Ali, Md Arfan, Nichol, Janet E, Bleiweiss, Max P, de Leeuw, Gerrit, Mhawish, Alaa, Shi, Yuan orcid:0000-0003-4011-8735 , Mazhar, Usman, Mehmood, Tariq, Kim, Jhoon et al (show 3 more authors) , Qiu, Zhongfeng, Qin, Wenmin and Nazeer, Majid (2022) AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA). FRONTIERS IN ENVIRONMENTAL SCIENCE, 10. 981522-. |
op_doi |
https://doi.org/10.3389/fenvs.2022.981522 |
container_title |
Frontiers in Environmental Science |
container_volume |
10 |
_version_ |
1810447925909651456 |