Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning

Congbo Song; Silvia Becagli; David C S Beddows; James Brean; Jo Browse; Qili Dai; Manuel Dall'Osto; Valerio Ferracci; Roy M Harrison; Neil Harris; Weijun Li; Anna E Jones; Amélie Kirchgäßner; Agung Ghani Kramawijaya; Alexander Kurganskiy; Angelo Lupi; Mauro Mazzola; Mirko Severi; Rita Traversi; Zongbo Shi

doi:10.1021/acs.est.1c07796

Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning

Congbo Song^*, Silvia Becagli, David C S Beddows, James Brean, Jo Browse, Qili Dai, Manuel Dall'Osto, Valerio Ferracci, Roy M Harrison, Neil Harris, Weijun Li, Anna E Jones, Amélie Kirchgäßner, Agung Ghani Kramawijaya, Alexander Kurganskiy, Angelo Lupi, Mauro Mazzola, Mirko Severi, Rita Traversi, Zongbo Shi^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

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.

Original language	English
Pages (from-to)	11189-11198
Number of pages	10
Journal	Environmental Science and Technology
Volume	56
Issue number	16
Early online date	25 Jul 2022
DOIs	https://doi.org/10.1021/acs.est.1c07796
Publication status	Published - 16 Aug 2022

Bibliographical note

Funding Information:
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). The authors acknowledge the staff of the Arctic Station Dirigibile Italia of the National Research Council of Italy for their support in measurements at the GVB station. The authors acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT model used to analyze the back trajectories.

Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.

Keywords

Arctic
machine learning
meteorology
particle number concentration
positive matrix factorization
source apportionment

ASJC Scopus subject areas

Chemistry(all)
Environmental Chemistry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acs.est.1c07796Licence: Creative Commons: Attribution (CC BY)

SongC2022UnderstandingFinal published version, 2.86 MBLicence: Creative Commons: Attribution (CC BY)

SEANA - Shipping Emissions in the Arctic and North Atlantic Atmosphere
Harrison, R., Shi, Z. & Beddows, D.
Natural Environment Research Council
3/01/19 → 30/06/26
Project: Research

Cite this

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, 56(16), 11189-11198. Advance online publication. https://doi.org/10.1021/acs.est.1c07796

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title = "Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning",

abstract = "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.",

keywords = "Arctic, machine learning, meteorology, particle number concentration, positive matrix factorization, source apportionment",

author = "Congbo Song and Silvia Becagli and Beddows, {David C S} and James Brean and Jo Browse and Qili Dai and Manuel Dall'Osto and Valerio Ferracci and Harrison, {Roy M} and Neil Harris and Weijun Li and Jones, {Anna E} and Am{\'e}lie Kirchg{\"a}{\ss}ner and Kramawijaya, {Agung Ghani} and Alexander Kurganskiy and Angelo Lupi and Mauro Mazzola and Mirko Severi and Rita Traversi and Zongbo Shi",

note = "Funding Information: 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). The authors acknowledge the staff of the Arctic Station Dirigibile Italia of the National Research Council of Italy for their support in measurements at the GVB station. The authors acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT model used to analyze the back trajectories. Publisher Copyright: {\textcopyright} 2022 The Authors. Published by American Chemical Society.",

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month = aug,

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doi = "10.1021/acs.est.1c07796",

language = "English",

volume = "56",

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Song, C, Becagli, S, Beddows, DCS , Brean, J, Browse, J, Dai, Q, Dall'Osto, M, Ferracci, V, Harrison, RM, Harris, N, Li, W, Jones, AE, Kirchgäßner, A, Kramawijaya, AG, 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

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T1 - Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning

AU - Song, Congbo

AU - Becagli, Silvia

AU - Beddows, David C S

AU - Brean, James

AU - Browse, Jo

AU - Dai, Qili

AU - Dall'Osto, Manuel

AU - Ferracci, Valerio

AU - Harrison, Roy M

AU - Harris, Neil

AU - Li, Weijun

AU - Jones, Anna E

AU - Kirchgäßner, Amélie

AU - Kramawijaya, Agung Ghani

AU - Kurganskiy, Alexander

AU - Lupi, Angelo

AU - Mazzola, Mauro

AU - Severi, Mirko

AU - Traversi, Rita

AU - Shi, Zongbo

N1 - Funding Information: 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). The authors acknowledge the staff of the Arctic Station Dirigibile Italia of the National Research Council of Italy for their support in measurements at the GVB station. The authors acknowledge the NOAA Air Resources Laboratory (ARL) for providing the HYSPLIT model used to analyze the back trajectories. Publisher Copyright: © 2022 The Authors. Published by American Chemical Society.

PY - 2022/8/16

Y1 - 2022/8/16

N2 - 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.

AB - 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.

KW - Arctic

KW - machine learning

KW - meteorology

KW - particle number concentration

KW - positive matrix factorization

KW - source apportionment

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U2 - 10.1021/acs.est.1c07796

DO - 10.1021/acs.est.1c07796

M3 - Article

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SN - 0013-936X

VL - 56

SP - 11189

EP - 11198

JO - Environmental Science and Technology

JF - Environmental Science and Technology

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ER -

Understanding sources and drivers of size-resolved aerosol in the high Arctic islands of Svalbard using a receptor model coupled with machine learning

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Projects

SEANA - Shipping Emissions in the Arctic and North Atlantic Atmosphere

Cite this