Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

Martha A. Zaidan*, Ville Haapasilta, Rishi Relan, Pauli Paasonen, Veli-Matti Kerminen, Heikki Junninen, Markku Kulmala, Adam S. Foster

*Corresponding author for this work

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Abstract

Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiala, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.
Original languageEnglish
JournalAtmospheric Chemistry and Physics
Volume18
Issue number17
Pages (from-to)12699-12714
Number of pages16
ISSN1680-7316
DOIs
Publication statusPublished - 2018

Cite this

Zaidan, M. A., Haapasilta, V., Relan, R., Paasonen, P., Kerminen, V-M., Junninen, H., ... Foster, A. S. (2018). Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach. Atmospheric Chemistry and Physics, 18(17), 12699-12714. https://doi.org/10.5194/acp-18-12699-2018
Zaidan, Martha A. ; Haapasilta, Ville ; Relan, Rishi ; Paasonen, Pauli ; Kerminen, Veli-Matti ; Junninen, Heikki ; Kulmala, Markku ; Foster, Adam S. / Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach. In: Atmospheric Chemistry and Physics. 2018 ; Vol. 18, No. 17. pp. 12699-12714.
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title = "Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach",
abstract = "Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiala, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.",
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Zaidan, MA, Haapasilta, V, Relan, R, Paasonen, P, Kerminen, V-M, Junninen, H, Kulmala, M & Foster, AS 2018, 'Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach', Atmospheric Chemistry and Physics, vol. 18, no. 17, pp. 12699-12714. https://doi.org/10.5194/acp-18-12699-2018

Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach. / Zaidan, Martha A.; Haapasilta, Ville; Relan, Rishi; Paasonen, Pauli; Kerminen, Veli-Matti; Junninen, Heikki; Kulmala, Markku; Foster, Adam S.

In: Atmospheric Chemistry and Physics, Vol. 18, No. 17, 2018, p. 12699-12714.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach

AU - Zaidan, Martha A.

AU - Haapasilta, Ville

AU - Relan, Rishi

AU - Paasonen, Pauli

AU - Kerminen, Veli-Matti

AU - Junninen, Heikki

AU - Kulmala, Markku

AU - Foster, Adam S.

PY - 2018

Y1 - 2018

N2 - Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiala, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.

AB - Atmospheric new-particle formation (NPF) is a very non-linear process that includes atmospheric chemistry of precursors and clustering physics as well as subsequent growth before NPF can be observed. Thanks to ongoing efforts, now there exists a tremendous amount of atmospheric data, obtained through continuous measurements directly from the atmosphere. This fact makes the analysis by human brains difficult but, on the other hand, enables the usage of modern data science techniques. Here, we calculate and explore the mutual information (MI) between observed NPF events (measured at Hyytiala, Finland) and a wide variety of simultaneously monitored ambient variables: trace gas and aerosol particle concentrations, meteorology, radiation and a few derived quantities. The purpose of the investigations is to identify key factors contributing to the NPF. The applied mutual information method finds that the formation events are strongly linked to sulfuric acid concentration and water content, ultraviolet radiation, condensation sink (CS) and temperature. Previously, these quantities have been well-established to be important players in the phenomenon via dedicated field, laboratory and theoretical research. The novelty of this work is to demonstrate that the same results are now obtained by a data analysis method which operates without supervision and without the need of understanding the physics deeply. This suggests that the method is suitable to be implemented widely in the atmospheric field to discover other interesting phenomena and their relevant variables.

U2 - 10.5194/acp-18-12699-2018

DO - 10.5194/acp-18-12699-2018

M3 - Journal article

VL - 18

SP - 12699

EP - 12714

JO - Atmospheric Chemistry and Physics

JF - Atmospheric Chemistry and Physics

SN - 1680-7316

IS - 17

ER -