Atmospheric 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.

Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning / Song C.; Becagli S.; Beddows D.C.S.; Brean J.; Browse J.; Dai Q.; Dall'osto M.; Ferracci V.; Harrison R.M.; Harris N.; Li W.; Jones A.E.; Kirchgassner A.; Kramawijaya A.G.; Kurganskiy A.; Lupi A.; Mazzola M.; Severi M.; Traversi R.; Shi Z.. - In: ENVIRONMENTAL SCIENCE & TECHNOLOGY. - ISSN 0013-936X. - ELETTRONICO. - 56:(2022), pp. 11189-11198. [10.1021/acs.est.1c07796]

Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning

Becagli S.;Severi M.;Traversi R.;
2022

Abstract

Atmospheric 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.
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11198
Song C.; Becagli S.; Beddows D.C.S.; Brean J.; Browse J.; Dai Q.; Dall'osto M.; Ferracci V.; Harrison R.M.; Harris N.; Li W.; Jones A.E.; Kirchgassner A.; Kramawijaya A.G.; Kurganskiy A.; Lupi A.; Mazzola M.; Severi M.; Traversi R.; Shi Z.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/1279819
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