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

Song, Congbo; Becagli, Silvia; Beddows, David C.S.; Brean, James; Browse, Jo; Dai, Qili; Dall’Osto, Manuel; Ferracci, Valerio; Harrison, Roy M.; Harris, Neil; Li, Weijun; Jones, Anna E. ORCID: https://orcid.org/0000-0002-2040-4841; Kirchgäßner, Amélie ORCID: https://orcid.org/0000-0001-7483-3652; Kramawijaya, Agung Ghani; Kurganskiy, Alexander; Lupi, Angelo; Mazzola, Mauro; Severi, Mirko; Traversi, Rita; Shi, Zongbo. 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 & Technology, 56 (16). 11189-11198. 10.1021/acs.est.1c07796

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Official URL: https://pubs.acs.org/doi/full/10.1021/acs.est.1c07...

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1021/acs.est.1c07796
ISSN:	0013-936X
Additional Keywords:	Arctic, source apportionment, positive matrix factorization, machine learning, particle number concentration, meteorology
Date made live:	01 Aug 2022 10:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533007

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1021/acs.est.1c07796

ISSN:

0013-936X

Additional Keywords:

Arctic, source apportionment, positive matrix factorization, machine learning, particle number concentration, meteorology

Date made live:

01 Aug 2022 10:23 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533007