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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Online Access: | https://doi.org/10.1021/acs.est.1c07796 https://dspace.lib.cranfield.ac.uk/handle/1826/18310 |
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ftcranfield:oai:dspace.lib.cranfield.ac.uk:1826/18310 2024-05-19T07:33:17+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 R. P. Li, Weijun Jones, Anna E. Kirchgäßner, Amelie Kramawijaya, Agung Ghani Kurganskiy, Alexander Lupi, Angelo Mazzola, Mauro Severi, Mirko Traversi, Rita Shi, Zongbo 2022-07-25 https://doi.org/10.1021/acs.est.1c07796 https://dspace.lib.cranfield.ac.uk/handle/1826/18310 en eng American Chemical Society Song C, Becagli S, Beddows DCS, et al., (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, Volume 56, Issue 16, 16 August 2022, pp. 11189–11198 0013-936X https://doi.org/10.1021/acs.est.1c07796 https://dspace.lib.cranfield.ac.uk/handle/1826/18310 1520-5851 Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ Arctic source apportionment positive matrix factorization machine learning particle number concentration meteorology Article 2022 ftcranfield https://doi.org/10.1021/acs.est.1c07796 2024-04-23T23:31:00Z 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. Natural Environment Research Council (NERC): NE/S00579X/1 Article in Journal/Newspaper Arctic Arctic Climate change Sea ice Svalbard Cranfield University: Collection of E-Research - CERES Environmental Science & Technology 56 16 11189 11198 |
institution |
Open Polar |
collection |
Cranfield University: Collection of E-Research - CERES |
op_collection_id |
ftcranfield |
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, David 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, Amelie 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 |
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. Natural Environment Research Council (NERC): NE/S00579X/1 |
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 R. P. Li, Weijun Jones, Anna E. Kirchgäßner, Amelie 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 R. P. Li, Weijun Jones, Anna E. Kirchgäßner, Amelie 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 |
https://doi.org/10.1021/acs.est.1c07796 https://dspace.lib.cranfield.ac.uk/handle/1826/18310 |
genre |
Arctic Arctic Climate change Sea ice Svalbard |
genre_facet |
Arctic Arctic Climate change Sea ice Svalbard |
op_relation |
Song C, Becagli S, Beddows DCS, et al., (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, Volume 56, Issue 16, 16 August 2022, pp. 11189–11198 0013-936X https://doi.org/10.1021/acs.est.1c07796 https://dspace.lib.cranfield.ac.uk/handle/1826/18310 1520-5851 |
op_rights |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
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 |
_version_ |
1799471360224788480 |