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

机译:使用空气离子精制分类和表征大气新粒子形成事件

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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 Hyyti?l?, 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-3nm sizes. Our results show that, on ~24% of the days in Hyyti?l?, 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 Hyyti?l? 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, wherein the ions apparently fail to grow to larger sizes, occurred on 18% of the days in Hyyti?l?. 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% 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.
机译:大气新粒子形成(NPF)是一个影响人类健康和全球气候的全球观察现象。随着全球大气测量站的不断增长的网络,研究NPF的努力增加了。在这项研究中,我们提出了一种自动化方法,将天数分为四个类别,包括NPF事件,非事件和两种类别,然后确保重复性并最大限度地减少在手动分类上花费的时间。我们将自动化方法应用于10?多年的数据在Hyyti的Smear II测量站收集,使用中性簇和空气离子光谱仪(Nais)。与传统应用的分类方法相比,将日期分类为事件和非事件以及含糊不清的日子,我们的方法能够将未定期的日期分类,因为它在Sub-3nm大小上访问NPF的初始步骤。我们的结果表明,在Hyyti的〜24%的日子里?L?,发生了区域NPF事件,其特点是晴朗的天气和良好的条件,如晴朗的天空和低凝结水槽。在Hyyti找到了另一个课程?L?是运输的事件类,似乎是NPF水平或垂直携带到我们的测量位置,并且它发生在学习日的总次数的17%上。另外,我们发现离子突发,其中离子显然不能生长到较大尺寸,在Hyyti的18%的日子中发生了18%?运输的事件和离子突发的特征在于,与区域NPF事件相比的良好环境条件,因此经历了中断的颗粒形成或生长。非事件发生在41%的日子里,并以完全云覆盖和高相对湿度为特征。此外,对于在测量站点发生的区域NPF事件,该方法识别开始时间,峰值时间和结束时间,这有助于我们专注于确切的时间窗口中的变量,以更好地了解过程级别的NPF。我们的自动化方法可以被修改为在观察NPF的其他测量位置工作。
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