Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species

We describe a method of using the Aerosol Robotic Network (AERONET) size distributions and complex refractive indices to retrieve the relative proportion of carbonaceous aerosols and free iron minerals (hematite and goethite). We assume that soot carbon has a spectrally flat refractive index and enh...

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Published in:Atmospheric Chemistry and Physics
Main Authors: Schuster, G. L., Dubovik, O., Arola, A.
Format: Text
Language:English
Published: 2018
Subjects:
Online Access:https://doi.org/10.5194/acp-16-1565-2016
https://www.atmos-chem-phys.net/16/1565/2016/
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spelling ftcopernicus:oai:publications.copernicus.org:acp29753 2023-05-15T13:06:51+02:00 Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species Schuster, G. L. Dubovik, O. Arola, A. 2018-09-10 application/pdf https://doi.org/10.5194/acp-16-1565-2016 https://www.atmos-chem-phys.net/16/1565/2016/ eng eng doi:10.5194/acp-16-1565-2016 https://www.atmos-chem-phys.net/16/1565/2016/ eISSN: 1680-7324 Text 2018 ftcopernicus https://doi.org/10.5194/acp-16-1565-2016 2019-12-24T09:52:46Z We describe a method of using the Aerosol Robotic Network (AERONET) size distributions and complex refractive indices to retrieve the relative proportion of carbonaceous aerosols and free iron minerals (hematite and goethite). We assume that soot carbon has a spectrally flat refractive index and enhanced imaginary indices at the 440 nm wavelength are caused by brown carbon or hematite. Carbonaceous aerosols can be separated from dust in imaginary refractive index space because 95 % of biomass burning aerosols have imaginary indices greater than 0.0042 at the 675–1020 nm wavelengths, and 95 % of dust has imaginary refractive indices of less than 0.0042 at those wavelengths. However, mixtures of these two types of particles can not be unambiguously partitioned on the basis of optical properties alone, so we also separate these particles by size. Regional and seasonal results are consistent with expectations. Monthly climatologies of fine mode soot carbon are less than 1.0 % by volume for West Africa and the Middle East, but the southern African and South American biomass burning sites have peak values of 3.0 and 1.7 %. Monthly averaged fine mode brown carbon volume fractions have a peak value of 5.8 % for West Africa, 2.1 % for the Middle East, 3.7 % for southern Africa, and 5.7 % for South America. Monthly climatologies of free iron volume fractions show little seasonal variability, and range from about 1.1 to 1.7 % for coarse mode aerosols in all four study regions. Finally, our sensitivity study indicates that the soot carbon retrieval is not sensitive to the component refractive indices or densities assumed for carbonaceous and free iron aerosols, and the retrieval differs by only 15.4 % when these parameters are altered from our chosen baseline values. The total uncertainty of retrieving soot carbon mass is ∼ 50 % (when uncertainty in the AERONET product and mixing state is included in the analysis). Text Aerosol Robotic Network Copernicus Publications: E-Journals Atmospheric Chemistry and Physics 16 3 1565 1585
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collection Copernicus Publications: E-Journals
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language English
description We describe a method of using the Aerosol Robotic Network (AERONET) size distributions and complex refractive indices to retrieve the relative proportion of carbonaceous aerosols and free iron minerals (hematite and goethite). We assume that soot carbon has a spectrally flat refractive index and enhanced imaginary indices at the 440 nm wavelength are caused by brown carbon or hematite. Carbonaceous aerosols can be separated from dust in imaginary refractive index space because 95 % of biomass burning aerosols have imaginary indices greater than 0.0042 at the 675–1020 nm wavelengths, and 95 % of dust has imaginary refractive indices of less than 0.0042 at those wavelengths. However, mixtures of these two types of particles can not be unambiguously partitioned on the basis of optical properties alone, so we also separate these particles by size. Regional and seasonal results are consistent with expectations. Monthly climatologies of fine mode soot carbon are less than 1.0 % by volume for West Africa and the Middle East, but the southern African and South American biomass burning sites have peak values of 3.0 and 1.7 %. Monthly averaged fine mode brown carbon volume fractions have a peak value of 5.8 % for West Africa, 2.1 % for the Middle East, 3.7 % for southern Africa, and 5.7 % for South America. Monthly climatologies of free iron volume fractions show little seasonal variability, and range from about 1.1 to 1.7 % for coarse mode aerosols in all four study regions. Finally, our sensitivity study indicates that the soot carbon retrieval is not sensitive to the component refractive indices or densities assumed for carbonaceous and free iron aerosols, and the retrieval differs by only 15.4 % when these parameters are altered from our chosen baseline values. The total uncertainty of retrieving soot carbon mass is ∼ 50 % (when uncertainty in the AERONET product and mixing state is included in the analysis).
format Text
author Schuster, G. L.
Dubovik, O.
Arola, A.
spellingShingle Schuster, G. L.
Dubovik, O.
Arola, A.
Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
author_facet Schuster, G. L.
Dubovik, O.
Arola, A.
author_sort Schuster, G. L.
title Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
title_short Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
title_full Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
title_fullStr Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
title_full_unstemmed Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species
title_sort remote sensing of soot carbon – part 1: distinguishing different absorbing aerosol species
publishDate 2018
url https://doi.org/10.5194/acp-16-1565-2016
https://www.atmos-chem-phys.net/16/1565/2016/
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source eISSN: 1680-7324
op_relation doi:10.5194/acp-16-1565-2016
https://www.atmos-chem-phys.net/16/1565/2016/
op_doi https://doi.org/10.5194/acp-16-1565-2016
container_title Atmospheric Chemistry and Physics
container_volume 16
container_issue 3
container_start_page 1565
op_container_end_page 1585
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