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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American Chemical Society
2022
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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 |
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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 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 |
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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1790596088810438656 |