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

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 le...

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Published in:Environmental Science & Technology
Main Authors: 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., Kirchgäßner, Amélie, Kramawijaya, Agung Ghani, Kurganskiy, Alexander, Lupi, Angelo, Mazzola, Mauro, Severi, Mirko, Traversi, Rita, Shi, Zongbo
Format: Article in Journal/Newspaper
Language:English
Published: 2022
Subjects:
Online Access:https://research.manchester.ac.uk/en/publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae
https://doi.org/10.1021/acs.est.1c07796
http://www.scopus.com/inward/record.url?scp=85135923849&partnerID=8YFLogxK
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spelling ftumanchesterpub:oai:pure.atira.dk:publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae 2024-06-23T07:48:43+00:00 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, David C.S. Brean, James Browse, Jo Dai, Qili Dall'Osto, Manuel Ferracci, Valerio Harrison, Roy M. Harris, Neil Li, Weijun Jones, Anna E. Kirchgäßner, Amélie Kramawijaya, Agung Ghani Kurganskiy, Alexander Lupi, Angelo Mazzola, Mauro Severi, Mirko Traversi, Rita Shi, Zongbo 2022-08-16 https://research.manchester.ac.uk/en/publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae https://doi.org/10.1021/acs.est.1c07796 http://www.scopus.com/inward/record.url?scp=85135923849&partnerID=8YFLogxK eng eng https://research.manchester.ac.uk/en/publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae info:eu-repo/semantics/openAccess 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 , Kirchgäßner , A , Kramawijaya , A G , Kurganskiy , A , Lupi , A , Mazzola , M , Severi , M , Traversi , R & Shi , Z 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 , vol. 56 , no. 16 , pp. 11189-11198 . https://doi.org/10.1021/acs.est.1c07796 Arctic machine learning meteorology particle number concentration positive matrix factorization source apportionment article 2022 ftumanchesterpub https://doi.org/10.1021/acs.est.1c07796 2024-06-04T01:03:47Z 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. Article in Journal/Newspaper Arctic Arctic Climate change Sea ice Svalbard The University of Manchester: Research Explorer Arctic Svalbard Environmental Science & Technology 56 16 11189 11198
institution Open Polar
collection The University of Manchester: Research Explorer
op_collection_id ftumanchesterpub
language English
topic Arctic
machine learning
meteorology
particle number concentration
positive matrix factorization
source apportionment
spellingShingle Arctic
machine learning
meteorology
particle number concentration
positive matrix factorization
source apportionment
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.
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
machine learning
meteorology
particle number concentration
positive matrix factorization
source apportionment
description 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.
format Article in Journal/Newspaper
author 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.
Kirchgäßner, Amélie
Kramawijaya, Agung Ghani
Kurganskiy, Alexander
Lupi, Angelo
Mazzola, Mauro
Severi, Mirko
Traversi, Rita
Shi, Zongbo
author_facet 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.
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
publishDate 2022
url https://research.manchester.ac.uk/en/publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae
https://doi.org/10.1021/acs.est.1c07796
http://www.scopus.com/inward/record.url?scp=85135923849&partnerID=8YFLogxK
geographic Arctic
Svalbard
geographic_facet Arctic
Svalbard
genre Arctic
Arctic
Climate change
Sea ice
Svalbard
genre_facet Arctic
Arctic
Climate change
Sea ice
Svalbard
op_source 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 , Kirchgäßner , A , Kramawijaya , A G , Kurganskiy , A , Lupi , A , Mazzola , M , Severi , M , Traversi , R & Shi , Z 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 , vol. 56 , no. 16 , pp. 11189-11198 . https://doi.org/10.1021/acs.est.1c07796
op_relation https://research.manchester.ac.uk/en/publications/18ff722c-c4c4-44a5-8106-25bcf1f785ae
op_rights info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.1021/acs.est.1c07796
container_title Environmental Science & Technology
container_volume 56
container_issue 16
container_start_page 11189
op_container_end_page 11198
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