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Discussion papers
https://doi.org/10.5194/acp-2018-631
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-631
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 09 Aug 2018

Research article | 09 Aug 2018

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This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Refined classification and characterization of atmospheric new particle formation events using air ions

Lubna Dada1, Robert Chellapermal1, Stephany Buenrostro Mazon1, Pauli Paasonen1, Janne Lampilahti1, Hanna E. Manninen1,2, Heikki Junninen1,3, Tuukka Petäjä1,4, Veli-Matti Kerminen1, and Markku Kulmala1,4,5 Lubna Dada et al.
  • 1Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
  • 2Experimental Physics Department, CERN, 1211 Geneva, Switzerland
  • 3Institute of Physics, University of Tartu, Ülikooli 18, 50090 Tartu, Estonia
  • 4Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, China
  • 5Joint International Research Laboratory of Atmospheric and Earth System Sciences, Nanjing University, Nanjing, China

Abstract. Atmospheric new particle formation (NPF) is a world-wide observed phenomenon that affects the human health and the global climate. With the growing network of global atmospheric measurement stations, efforts towards investigating NPF have increased. In this study, we present an automated method to classify days into four categories including NPF events, non-events and two classes in between, which then ensures the reproducibility and minimizes the man-hours spent on manual classification. We applied our automated method to 10 years of data collected at the SMEAR II measurement station in Hyytiälä, southern Finland. In contrast to the traditionally-applied classification methods which categorize days into events, non-events and ambiguous days as undefined days, our method is able to classify the undefined days as it accesses the initial steps of NPF at sub-3nm sizes. Our results show that on ~24% of the days in Hyytiälä, a regional NPF event occurred and was characterized by a nice weather and favorable conditions such as a clear sky and low condensation sink. Another class found in Hyytiälä is the transported event class, which seems to be NPF carried horizontally or vertically to our measurement location and it occurred on 17% of the total studied days. Additionally, we found that an ion burst, where the ions apparently fail to grow to larger sizes, occurred on 18% of the days in Hyytiälä. The transported events and ion bursts were characterized by less favorable ambient conditions than regional NPF events, and thus experienced interrupted particle formation or growth. Non-events occurred on 41% of the days and were characterized by a complete cloud cover and high relative humidity. Moreover, for the regional NPF events occurring at the measurement site, the method identifies the start time, peak time and end time, which helps us focus on variables within an exact time window to better understand NPF in a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.

Lubna Dada et al.
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Lubna Dada et al.
Lubna Dada et al.
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Our manuscript provides an automatic method to classify new particle formation events into 4 classes based on the accompanying air ion concentrations. The method is applied to 10 years of data measured within the SMEAR II station and was capable to eliminate the undefined class as well as to define the start, peak and end times of a regional event by monitoring the initial steps of cluster formation. Our method can be modified and applied to different locations where particle formation occurs.
Our manuscript provides an automatic method to classify new particle formation events into 4...
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