Abstract
Abstract. Atmospheric new particle formation (NPF) is an important source of climate-relevant aerosol particles which has been observed at many locations globally. To study this phenomenon, the first step is to identify whether an NPF event occurs or not on a given day. In practice, NPF event identification is performed visually by classifying the NPF event or non-event days from the particle number size distribution surface plots. Unfortunately, this day-by-day visual classification is time-consuming and labor-intensive, and the identification process renders subjective results. To detect NPF events automatically, we regard the visual signature (banana shape) which has been observed all over the world in NPF surface plots as a special kind of object, and a deep learning model called Mask R-CNN is applied to localize the spatial layouts of NPF events in their surface plots. Utilizing only 358 human-annotated masks on data from the Station for Measuring Ecosystem–Atmosphere Relations (SMEAR) II station (Hyytiälä, Finland), the Mask R-CNN model was successfully generalized for three SMEAR stations in Finland and the San Pietro Capofiume (SPC) station in Italy. In addition to the detection of NPF events (especially the strongest events), the presented method can determine the growth rates, start times, and end times for NPF events automatically. The automatically determined growth rates agree with the manually determined growth rates. The statistical results validate the potential of applying the proposed method to different sites, which will improve the automatic level for NPF event detection and analysis. Furthermore, the proposed automatic NPF event analysis method can minimize subjectivity compared with human-made analysis, especially when long-term data series are analyzed and statistical comparisons between different sites are needed for event characteristics such as the start and end times, thereby saving time and effort for scientists studying NPF events.
Highlights
Atmospheric aerosols have profound impacts on air quality, human health, ecosystems, weather, and climate (Asmi et al, 2011a; Hirsikko et al, 2011; Joutsensaari et al, 2018; Chu et al, 2019; Lee et al, 2019)
The advantage of vision-based methods is that experts can explicitly tell which region in a surface plot is thought of as the evidence of an New particle formation (NPF) event, and the drawbacks of vision-based methods are that they are labor-intensive and time-consuming and the classification process is subject to human bias
Rule-based methods can classify NPF types automatically, but the drawback of these methods is that the particle number concentrations can vary a lot between different environments, meaning that the prior knowledge used in one site may fail in other sites or complex situations
Summary
Atmospheric aerosols have profound impacts on air quality, human health, ecosystems, weather, and climate (Asmi et al, 2011a; Hirsikko et al, 2011; Joutsensaari et al, 2018; Chu et al, 2019; Lee et al, 2019). Considering the increasing number of global observation stations (Kulmala, 2018), an automatic NPF detection method that applies to NPF datasets collected in different sites is necessary. Our aims in this study are (1) to automatically localize the globally observed visual signature (banana shape) for regional NPF events, which can identify NPF types (events occur or not, especially for the strongest events), and determine the growth rates, start times, and end times and (2) to investigate the statistical characteristics of growth rates, start times, and end times for the strongest NPF events for the three SMEAR stations in Finland and the SPC station in Italy.
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