AEROsol generic classification using a novel Satellite remote sensing Approach (AEROSA)

Muhammad Bilal, Md Arfan Ali, Janet E. Nichol, Max P. Bleiweiss, Gerrit de Leeuw, Alaa Mhawish, Yuan Shi, Usman Mazhar, Tariq Mehmood, Jhoon Kim, Zhongfeng Qiu, Wenmin Qin, Majid Nazeer

Research output: Contribution to journalArticlepeer-review

Abstract

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 (20th 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, instead providing information on aerosol amount and size. The purpose is to introduce AEROSA for those researchers who are interested in the generic classification of aerosols based on AOD and AE, without claiming the exact aerosol types such as DD, BB, CC, and CM. AEROSA not only provides 9 generic aerosol classes for all observations but can also accommodate variations in location and season, which GA aerosol types do not.

Original languageEnglish
Article number981522
JournalFrontiers in Environmental Science
Volume10
DOIs
Publication statusPublished - 2022 Aug 26

Bibliographical note

Funding Information:
This work was supported by the National Key Research and Development Program of China (2016YFC1400901), the scientific research projects of China Three Gorges Corporation (202003111), the Special Project of Jiangsu Distinguished Professor (R2018T22), and the Startup Foundation for Introduction Talent of NUIST (2017r107). AM acknowledges Foreign Young Talent Program (No. QN20211014016L) and Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB410). Additional support came from the New Mexico State University College of Agriculture Consumer and Environmental Sciences’ Agricultural Experiment Station.

Funding Information:
This work was supported by the National Key Research and Development Program of China (2016YFC1400901), the scientific research projects of China Three Gorges Corporation (202003111), the Special Project of Jiangsu Distinguished Professor (R2018T22), and the Startup Foundation for Introduction Talent of NUIST (2017r107). AM acknowledges Foreign Young Talent Program (No. QN20211014016L) and Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB410). Additional support came from the New Mexico State University College of Agriculture Consumer and Environmental Sciences’ Agricultural Experiment Station.

Publisher Copyright:
Copyright © 2022 Bilal, Ali, Nichol, Bleiweiss, de Leeuw, Mhawish, Shi, Mazhar, Mehmood, Kim, Qiu, Qin and Nazeer.

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)

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