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

10 pages, 3 figures, 1 table, supporting information https://doi.org/10.1021/acs.est.1c07796 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 i...

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Published in:Environmental Science & Technology
Main Authors: Song, Congbo, Becagli, Silvia, Beddows, D.C.S., Brean, James, Browse, Jo, Dai, Qili, Dall'Osto, Manuel, Ferracci, Valerio, Harrison, Roy M., Harris, Neil R. P., Li, Weijun, Jones, Anna E., Kirchgäßner, Amélie, Kramawijaya, Agung Ghani, Kurganskiy, Alexander, Lupi, Angelo, Mazzola, Mauro, Severi, Mirko, Traversi, Rita, Shi, Zongbo
Other Authors: Natural Environment Research Council (UK), Agencia Estatal de Investigación (España)
Format: Article in Journal/Newspaper
Language:English
Published: American Chemical Society 2022
Subjects:
Online Access:http://hdl.handle.net/10261/278916
https://doi.org/10.1021/acs.est.1c07796
https://doi.org/10.13039/501100011033
https://doi.org/10.13039/501100000270
id ftcsic:oai:digital.csic.es:10261/278916
record_format openpolar
spelling ftcsic:oai:digital.csic.es:10261/278916 2024-02-11T10:00:23+01:00 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, D.C.S. Brean, James Browse, Jo Dai, Qili Dall'Osto, Manuel Ferracci, Valerio Harrison, Roy M. Harris, Neil R. P. Li, Weijun Jones, Anna E. Kirchgäßner, Amélie Kramawijaya, Agung Ghani Kurganskiy, Alexander Lupi, Angelo Mazzola, Mauro Severi, Mirko Traversi, Rita Shi, Zongbo Natural Environment Research Council (UK) Agencia Estatal de Investigación (España) 2022-07 http://hdl.handle.net/10261/278916 https://doi.org/10.1021/acs.est.1c07796 https://doi.org/10.13039/501100011033 https://doi.org/10.13039/501100000270 en eng American Chemical Society Publisher's version https://doi.org/10.1021/acs.est.1c07796 Sí Environmental Science and Technology 56(16): 11189-11198 (2022) 0013-936X CEX2019-000928-S http://hdl.handle.net/10261/278916 doi:10.1021/acs.est.1c07796 1520-5851 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100000270 open Arctic Source apportionment Positive matrix factorization Machine learning Particle number concentration Meteorology artículo 2022 ftcsic https://doi.org/10.1021/acs.est.1c0779610.13039/50110001103310.13039/501100000270 2024-01-16T11:28:46Z 10 pages, 3 figures, 1 table, supporting information https://doi.org/10.1021/acs.est.1c07796 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 This research was supported by the Natural Environment Research Council (grant no. NE/S00579X/1) and endorsed by the Surface Ocean-Lower Atmosphere Study (SOLAS) With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) Peer reviewed Article in Journal/Newspaper Arctic Climate change Sea ice Svalbard Digital.CSIC (Spanish National Research Council) Arctic Svalbard Environmental Science & Technology 56 16 11189 11198
institution Open Polar
collection Digital.CSIC (Spanish National Research Council)
op_collection_id ftcsic
language English
topic Arctic
Source apportionment
Positive matrix factorization
Machine learning
Particle number concentration
Meteorology
spellingShingle Arctic
Source apportionment
Positive matrix factorization
Machine learning
Particle number concentration
Meteorology
Song, Congbo
Becagli, Silvia
Beddows, D.C.S.
Brean, James
Browse, Jo
Dai, Qili
Dall'Osto, Manuel
Ferracci, Valerio
Harrison, Roy M.
Harris, Neil R. P.
Li, Weijun
Jones, Anna E.
Kirchgäßner, Amélie
Kramawijaya, Agung Ghani
Kurganskiy, Alexander
Lupi, Angelo
Mazzola, Mauro
Severi, Mirko
Traversi, Rita
Shi, Zongbo
Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
topic_facet Arctic
Source apportionment
Positive matrix factorization
Machine learning
Particle number concentration
Meteorology
description 10 pages, 3 figures, 1 table, supporting information https://doi.org/10.1021/acs.est.1c07796 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 This research was supported by the Natural Environment Research Council (grant no. NE/S00579X/1) and endorsed by the Surface Ocean-Lower Atmosphere Study (SOLAS) With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S) Peer reviewed
author2 Natural Environment Research Council (UK)
Agencia Estatal de Investigación (España)
format Article in Journal/Newspaper
author Song, Congbo
Becagli, Silvia
Beddows, D.C.S.
Brean, James
Browse, Jo
Dai, Qili
Dall'Osto, Manuel
Ferracci, Valerio
Harrison, Roy M.
Harris, Neil R. P.
Li, Weijun
Jones, Anna E.
Kirchgäßner, Amélie
Kramawijaya, Agung Ghani
Kurganskiy, Alexander
Lupi, Angelo
Mazzola, Mauro
Severi, Mirko
Traversi, Rita
Shi, Zongbo
author_facet Song, Congbo
Becagli, Silvia
Beddows, D.C.S.
Brean, James
Browse, Jo
Dai, Qili
Dall'Osto, Manuel
Ferracci, Valerio
Harrison, Roy M.
Harris, Neil R. P.
Li, Weijun
Jones, Anna E.
Kirchgäßner, Amélie
Kramawijaya, Agung Ghani
Kurganskiy, Alexander
Lupi, Angelo
Mazzola, Mauro
Severi, Mirko
Traversi, Rita
Shi, Zongbo
author_sort Song, Congbo
title Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
title_short Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
title_full Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
title_fullStr Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
title_full_unstemmed Understanding Sources and Drivers of Size-Resolved Aerosol in the High Arctic Islands of Svalbard Using a Receptor Model Coupled with Machine Learning
title_sort understanding sources and drivers of size-resolved aerosol in the high arctic islands of svalbard using a receptor model coupled with machine learning
publisher American Chemical Society
publishDate 2022
url http://hdl.handle.net/10261/278916
https://doi.org/10.1021/acs.est.1c07796
https://doi.org/10.13039/501100011033
https://doi.org/10.13039/501100000270
geographic Arctic
Svalbard
geographic_facet Arctic
Svalbard
genre Arctic
Climate change
Sea ice
Svalbard
genre_facet Arctic
Climate change
Sea ice
Svalbard
op_relation Publisher's version
https://doi.org/10.1021/acs.est.1c07796

Environmental Science and Technology 56(16): 11189-11198 (2022)
0013-936X
CEX2019-000928-S
http://hdl.handle.net/10261/278916
doi:10.1021/acs.est.1c07796
1520-5851
http://dx.doi.org/10.13039/501100011033
http://dx.doi.org/10.13039/501100000270
op_rights open
op_doi https://doi.org/10.1021/acs.est.1c0779610.13039/50110001103310.13039/501100000270
container_title Environmental Science & Technology
container_volume 56
container_issue 16
container_start_page 11189
op_container_end_page 11198
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