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...
Published in: | Environmental Science & Technology |
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2022
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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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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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1802639044802248704 |