TY - JOUR
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 American Chemical Society. All rights reserved.
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
UR - http://www.scopus.com/inward/record.url?scp=85135923849&partnerID=8YFLogxK
U2 - 10.1021/acs.est.1c07796
DO - 10.1021/acs.est.1c07796
M3 - Article
C2 - 35878000
AN - SCOPUS:85135923849
SN - 0013-936X
VL - 56
SP - 11189
EP - 11198
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 16
ER -
