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

Atmospheric new-particle formation (NPF) is a worldwide-observed phenomenon that affects the human health and the global climate. With a 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 reproducibility and minimizes the 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 using a Neutral cluster and Air Ion Spectrometer (NAIS). In contrast to the traditionally applied classification methods, which categorize days into events and 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-3&thinsp;nm sizes. Our results show that, on ∼24&thinsp;% of the days in Hyytiälä, a regional NPF event occurred and was characterized by nice weather and favourable 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&thinsp;% of the total studied days. Additionally, we found that an ion burst, wherein the ions apparently fail to grow to larger sizes, occurred on 18&thinsp;% of the days in Hyytiälä. The transported events and ion bursts were characterized by less favourable ambient conditions than regional NPF events and thus experienced interrupted particle formation or growth. Non-events occurred on 41&thinsp;% of the days and were characterized by complete cloud cover and high relative humidity. Moreover, for 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 at a process level. Our automated method can be modified to work in other measurement locations where NPF is observed.</p