Understanding sources and drivers of size-resolved aerosol in the High Arctic islands of Svalbard using a receptor model coupled with machine learning

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dc.contributor.authorSong, Congbo
dc.contributor.authorBecagli, Silvia
dc.contributor.authorBeddows, David C. S.
dc.contributor.authorBrean, James
dc.contributor.authorBrowse, Jo
dc.contributor.authorDai, Qili
dc.contributor.authorDall'Osto, Manuel
dc.contributor.authorFerracci, Valerio
dc.contributor.authorHarrison, Roy M.
dc.contributor.authorHarris, Neil R. P.
dc.contributor.authorLi, Weijun
dc.contributor.authorJones, Anna E.
dc.contributor.authorKirchgäßner, Amelie
dc.contributor.authorKramawijaya, Agung Ghani
dc.contributor.authorKurganskiy, Alexander
dc.contributor.authorLupi, Angelo
dc.contributor.authorMazzola, Mauro
dc.contributor.authorSeveri, Mirko
dc.contributor.authorTraversi, Rita
dc.contributor.authorShi, Zongbo
dc.date.accessioned2022-08-11T11:05:54Z
dc.date.available2022-08-11T11:05:54Z
dc.date.issued2022-07-25
dc.description.abstractAtmospheric aerosols are important drivers of Arctic climate change through aerosol–cloud–climate interactions. However, large uncertainties remain on the sources and processes controlling particle numbers in both fine and coarse modes. Here, we applied a receptor model and an explainable machine learning technique to understand the sources and drivers of particle numbers from 10 nm to 20 μm in Svalbard. Nucleation, biogenic, secondary, anthropogenic, mineral dust, sea salt and blowing snow aerosols and their major environmental drivers were identified. Our results show that the monthly variations in particles are highly size/source dependent and regulated by meteorology. Secondary and nucleation aerosols are the largest contributors to potential cloud condensation nuclei (CCN, particle number with a diameter larger than 40 nm as a proxy) in the Arctic. Nonlinear responses to temperature were found for biogenic, local dust particles and potential CCN, highlighting the importance of melting sea ice and snow. These results indicate that the aerosol factors will respond to rapid Arctic warming differently and in a nonlinear fashion.en_UK
dc.description.sponsorshipNatural Environment Research Council (NERC): NE/S00579X/1en_UK
dc.identifier.citationSong C, Becagli S, Beddows DCS, et al., (2022) Understanding sources and drivers of size-resolved aerosol in the High Arctic islands of Svalbard using a receptor model coupled with machine learning, Environmental Science and Technology, Volume 56, Issue 16, 16 August 2022, pp. 11189–11198en_UK
dc.identifier.eissn1520-5851
dc.identifier.issn0013-936X
dc.identifier.urihttps://doi.org/10.1021/acs.est.1c07796
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18310
dc.language.isoenen_UK
dc.publisherAmerican Chemical Societyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArcticen_UK
dc.subjectsource apportionmenten_UK
dc.subjectpositive matrix factorizationen_UK
dc.subjectmachine learningen_UK
dc.subjectparticle number concentrationen_UK
dc.subjectmeteorologyen_UK
dc.titleUnderstanding sources and drivers of size-resolved aerosol in the High Arctic islands of Svalbard using a receptor model coupled with machine learningen_UK
dc.typeArticleen_UK

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