Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data.
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| Title: | Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data. |
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| Authors: | Amarillo, Ana Carolina1,2 (AUTHOR) anacarolina.amarillo@univaq.it, Curci, Gabriele1,2 (AUTHOR) gabriele.curci@univaq.it, De Santis, Davide3 (AUTHOR) davide.de.santis@uniroma2.it, Bassani, Cristiana4 (AUTHOR) cristiana.bassani@cnr.it, Barnaba, Francesca5 (AUTHOR) f.barnaba@isac.cnr.it, Rémy, Samuel6 (AUTHOR) sr@hygeos.com, Di Liberto, Luca5 (AUTHOR) l.diliberto@isac.cnr.it, Oxford, Christopher R.7 (AUTHOR) coxford@wustl.edu, Windwer, Eli8 (AUTHOR) eli.windwer@weizmann.ac.il, Del Frate, Fabio3 (AUTHOR) fabio.del.frate@uniroma2.it |
| Source: | Atmospheric Environment. Oct2024, Vol. 334, pN.PAG-N.PAG. 1p. |
| Subject Terms: | *Particulate matter, *Air pollution, *Aerosol analysis, Machine learning, Analytical chemistry |
| Abstract: | Monitoring particulate matter (PM) air pollution in terms of both concentration and composition, is very important due to its effects on human health and climate. In the PRIMARY project we aim at retrieving the aerosol composition from space using the hyperspectral observations from the Italian Space Agency's PRISMA mission. To this end, we are developing a machine learning algorithm trained with synthetic top-of-atmosphere reflectances and underlying aerosol fields. As part of this process, we plan to use the global forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) as the core to generate this synthetic dataset. However, to proceed in this direction, a preliminary assessment of the reliability of this model-based dataset when compared to observations is necessary, also to bias correct the output if needed. With this aim, we assess the representation of the aerosol chemical composition and the related optical properties at selected globally distributed sites in CAMS, comparing the simulations with near-surface aerosol chemical analyses from the SPARTAN network and column sun-photometer observations from the AERONET network. We found that CAMS forecasts skills changed over time due to updates in the modelling system, with the latter two version cycles (46 and 47) being similar. Generally, they reproduce the aerosol composition within a factor of 2. We found a substantial overestimation of organic matter (OM) by a factor of 3. Applying a correcting factor to OM (constant at the global level) warrants a much more realistic representation of PM 2.5 total mass and relative fraction of single species in CAMS. From the so derived CAMS aerosol-speciated profiles, we calculate aerosol optical properties, needed for subsequent use in a radiative transfer model. Comparison against AERONET indeed shows that OM bias correction resulted in improvements in Extinction Ångstrom Exponent (α 440 n m 870 n m ). Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (g) simulations resulted slightly degraded, confirming the possibility of using CAMS as the base for a synthetic retrieval training dataset. [Display omitted] • CAMS model overestimates OM and PM 2.5 concentration compared with global SPARTAN network ground-based data. • Applying a correction factor of 0.35 to OM improves the estimation of PM 2.5 concentration and composition. • The CAMS vs AERONET optical parameters were slightly affected allowing the use of corrected data for further application. [ABSTRACT FROM AUTHOR] |
| Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 178858054 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Amarillo%2C+Ana+Carolina%22">Amarillo, Ana Carolina</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> anacarolina.amarillo@univaq.it</i><br /><searchLink fieldCode="AR" term="%22Curci%2C+Gabriele%22">Curci, Gabriele</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> gabriele.curci@univaq.it</i><br /><searchLink fieldCode="AR" term="%22De+Santis%2C+Davide%22">De Santis, Davide</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> davide.de.santis@uniroma2.it</i><br /><searchLink fieldCode="AR" term="%22Bassani%2C+Cristiana%22">Bassani, Cristiana</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> cristiana.bassani@cnr.it</i><br /><searchLink fieldCode="AR" term="%22Barnaba%2C+Francesca%22">Barnaba, Francesca</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> f.barnaba@isac.cnr.it</i><br /><searchLink fieldCode="AR" term="%22Rémy%2C+Samuel%22">Rémy, Samuel</searchLink><relatesTo>6</relatesTo> (AUTHOR)<i> sr@hygeos.com</i><br /><searchLink fieldCode="AR" term="%22Di+Liberto%2C+Luca%22">Di Liberto, Luca</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> l.diliberto@isac.cnr.it</i><br /><searchLink fieldCode="AR" term="%22Oxford%2C+Christopher+R%2E%22">Oxford, Christopher R.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<i> coxford@wustl.edu</i><br /><searchLink fieldCode="AR" term="%22Windwer%2C+Eli%22">Windwer, Eli</searchLink><relatesTo>8</relatesTo> (AUTHOR)<i> eli.windwer@weizmann.ac.il</i><br /><searchLink fieldCode="AR" term="%22Del+Frate%2C+Fabio%22">Del Frate, Fabio</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> fabio.del.frate@uniroma2.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Atmospheric+Environment%22">Atmospheric Environment</searchLink>. Oct2024, Vol. 334, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br />*<searchLink fieldCode="DE" term="%22Air+pollution%22">Air pollution</searchLink><br />*<searchLink fieldCode="DE" term="%22Aerosol+analysis%22">Aerosol analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Analytical+chemistry%22">Analytical chemistry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Monitoring particulate matter (PM) air pollution in terms of both concentration and composition, is very important due to its effects on human health and climate. In the PRIMARY project we aim at retrieving the aerosol composition from space using the hyperspectral observations from the Italian Space Agency's PRISMA mission. To this end, we are developing a machine learning algorithm trained with synthetic top-of-atmosphere reflectances and underlying aerosol fields. As part of this process, we plan to use the global forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) as the core to generate this synthetic dataset. However, to proceed in this direction, a preliminary assessment of the reliability of this model-based dataset when compared to observations is necessary, also to bias correct the output if needed. With this aim, we assess the representation of the aerosol chemical composition and the related optical properties at selected globally distributed sites in CAMS, comparing the simulations with near-surface aerosol chemical analyses from the SPARTAN network and column sun-photometer observations from the AERONET network. We found that CAMS forecasts skills changed over time due to updates in the modelling system, with the latter two version cycles (46 and 47) being similar. Generally, they reproduce the aerosol composition within a factor of 2. We found a substantial overestimation of organic matter (OM) by a factor of 3. Applying a correcting factor to OM (constant at the global level) warrants a much more realistic representation of PM 2.5 total mass and relative fraction of single species in CAMS. From the so derived CAMS aerosol-speciated profiles, we calculate aerosol optical properties, needed for subsequent use in a radiative transfer model. Comparison against AERONET indeed shows that OM bias correction resulted in improvements in Extinction Ångstrom Exponent (α 440 n m 870 n m ). Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA) and Asymmetry Parameter (g) simulations resulted slightly degraded, confirming the possibility of using CAMS as the base for a synthetic retrieval training dataset. [Display omitted] • CAMS model overestimates OM and PM 2.5 concentration compared with global SPARTAN network ground-based data. • Applying a correction factor of 0.35 to OM improves the estimation of PM 2.5 concentration and composition. • The CAMS vs AERONET optical parameters were slightly affected allowing the use of corrected data for further application. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.atmosenv.2024.120683 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Particulate matter Type: general – SubjectFull: Air pollution Type: general – SubjectFull: Aerosol analysis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Analytical chemistry Type: general Titles: – TitleFull: Validation of aerosol chemical composition and optical properties provided by Copernicus Atmosphere Monitoring Service (CAMS) using ground-based global data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Amarillo, Ana Carolina – PersonEntity: Name: NameFull: Curci, Gabriele – PersonEntity: Name: NameFull: De Santis, Davide – PersonEntity: Name: NameFull: Bassani, Cristiana – PersonEntity: Name: NameFull: Barnaba, Francesca – PersonEntity: Name: NameFull: Rémy, Samuel – PersonEntity: Name: NameFull: Di Liberto, Luca – PersonEntity: Name: NameFull: Oxford, Christopher R. – PersonEntity: Name: NameFull: Windwer, Eli – PersonEntity: Name: NameFull: Del Frate, Fabio IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 13522310 Numbering: – Type: volume Value: 334 Titles: – TitleFull: Atmospheric Environment Type: main |
| ResultId | 1 |
