Global analysis of continental boundary layer new particle formation based on long-term 
measurements

Nieminen, Tuomo; Kerminen, Veli‐Matti; Petäj̈ä, Tuukka; Aalto, Pasi; Arshinov, M.; Asmi, Eija; Baltensperger, Urs; Beddows, David C. S.; Beukes, Johan P.; Collins, D. R.; Ding, Aijun; Harrison, Roy M.; Henzing, Bas; Hooda, Rakesh K.; Hu, Min; Hõrrak, Urmas; Kivekäs, Niku; Komsaare, Kaupo; Krejci, Radovan; Kristensson, Adam; Laakso, Lauri; Laaksonen, A.; Leaitch, W. Richard; Lihavainen, Heikki; Mihalopoulos, Nikolaos; Németh, Zoltán; Nie, Wei; O’Dowd, Colin; Salma, Imre; Sellegri, Karine; Svenningsson, Birgitta; Swietlicki, Erik; Tunved, Peter; Ulevičius, Vidmantas; Vakkari, Ville; Vana, Marko; Wiedensohler, Alfred; Wu, Zhijun; Virtanen, Annele; Kulmala, Markku

doi:10.5194/acp-2018-304

Cited by 26 publications

(58 citation statements)

References 76 publications

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“…In a vast majority of the sites, NPF is more frequent in summer compared with winter ( Although the NPF frequency has varying seasonal characteristics at different sites, the GRs of newlyformed particle display almost exclusive a summer maximum (e.g. Nieminen et al 2018). This feature originates from higher biogenic emissions and typically stronger atmospheric photochemistry during the summertime, both of them enhancing the production of LVOC vapors responsible for the particle growth, while there is practically nothing during summer (except perhaps extreme temperatures) that would be expected to suppress the particle growth.…”

Section: Temporal Characteristicsmentioning

confidence: 99%

“…During the past decade or so, a number of scientific reviews, or compilation studies, on atmospheric NPF has been written , O'Dowd and Hoffmann 2005, Curtius 2006, Holmes 2007, Enghoff and Svensmark 2008, Kazil et al 2008, Hegg and Baker 2009, Bzdek and Johnston 2010, Hirsikko et al 2011, Vehkamäki and Riipinen 2012, Zhang et al 2012, Li et al 2015b, Wang et al 2017a, Nieminen et al 2018. These papers have focused on varying aspects of atmospheric NPF, typically covering one or more of the following topics: (1) the observed character of NPF in different atmospheric environments, including the particle formation and growth rates (GRs) during NPF and frequency at which NPF occurs, (2) the chemistry of atmospheric NPF, (3) the thermodynamics and kinetics of NPF, (4) atmospheric NPF mechanisms, including the role of ions in this process, (5) analysis of the factors favoring, or disfavoring, atmospheric NPF, and (6) instrumental issues related to investigating NPF.…”

Section: Introductionmentioning

confidence: 99%

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Atmospheric new particle formation and growth: review of field observations

Kerminen

Chen

Vakkari

et al. 2018

Environ. Res. Lett.

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442

405

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This review focuses on the observed characteristics of atmospheric new particle formation (NPF) in different environments of the global troposphere. After a short introduction, we will present a theoretical background that discusses the methods used to analyze measurement data on atmospheric NPF and the associated terminology. We will update on our current understanding of regional NPF, i.e. NPF taking simultaneously place over large spatial scales, and complement that with a full review on reported NPF and growth rates during regional NPF events. We will shortly review atmospheric NPF taking place at sub-regional scales. Since the growth of newly-formed particles into larger sizes is of great current interest, we will briefly discuss our observation-based understanding on which gaseous compounds contribute to the growth of newly-formed particles, and what implications this will have on atmospheric cloud condensation nuclei formation. We will finish the review with a summary of our main findings and future outlook that outlines the remaining research questions and needs for additional measurements.

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Section: Temporal Characteristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Atmospheric new particle formation and growth: review of field observations

Kerminen

Chen

Vakkari

et al. 2018

Environ. Res. Lett.

Self Cite

442

405

View full text Add to dashboard Cite

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“…in general. Since there are usually more non-nucleation days than nucleation days in a time interval of a month or more (Nieminen et al, 2017), the assumptions for NSF GEN are met more easily than for NSF NUC . NSF NUC characterizes an ordinary nucleation day within e.g.…”

Section: Data Treatmentmentioning

confidence: 99%

“…In addition, up to approximately 50 % of all cloud condensation nuclei (CCN) can originate from NPF and growth (Spracklen et al, 2008;Merikanto et al, 2009), which relates the process to the climate system and indicates its overall importance Carslaw et al, 2013;Shen et al, 2017). New particle formation has also been proven to be common in large cities (Nieminen et al, 2017). Urban NPF can interact with and can be influenced by regional nucleation events, at least under some geographic conditions, and can become part of a phenomenon with a much larger horizontal extension than the city (Salma et al, 2016b).…”

Section: Introductionmentioning

confidence: 99%

Quantification of an atmospheric nucleation and growth process as a single source of aerosol particles in a city

Salma¹,

Varga²,

Németh³

2017

Atmos. Chem. Phys.

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Abstract. Effects of a new aerosol particle formation (NPF) and particle diameter growth process as a single source of atmospheric particle number concentrations were evaluated and quantified on the basis of experimental data sets obtained from particle number size distribution measurements in the city centre and near-city background of Budapest for 5 years. Nucleation strength factors for a nucleation day (NSF NUC ) and for a general day (NSF GEN ) were derived separately for seasons and full years. The former characteristic represents the concentration increment of ultrafine (UF) particles specifically on nucleation days with respect to accumulationmode (regional background) concentrations (particles with equivalent diameters of 100-1000 nm; N 100−1000 ) due solely to the nucleation process. The latter factor expresses the contribution of nucleation to particle numbers on general days; thus, it represents a longer time interval such as season or year. The nucleation source had the largest effect on the concentrations around noon and early afternoon, as expected. During this time interval, it became the major source of particles in the near-city background. Nucleation increased the daily mean concentrations on nucleation days by mean factors of 2.3 and 1.58 in the near-city background and city centre, respectively. Its effect was largest in winter, which was explained by the substantially lower N 100−1000 levels on nucleation days than those on non-nucleation days. On an annual timescale, 37 % of the UF particles were generated by nucleation in the near-city background, while NPF produced 13 % of UF particles in the city centre. The differences among the annual mean values, and among the corresponding seasonal mean values, were likely caused by the variability in controlling factors from year to year. The values obtained represent the lower limits of the contributions. The shares determined imply that NPF is a non-negligible or substantial source of particles in near-city background environments and even in city centres, where the vehicular road emissions usually prevail. Atmospheric residence time of nucleationmode particles was assessed by a decay curve analysis, and a mean of 02:30 was obtained. The present study suggests that the health-related consequences of the atmospheric NPF and growth process in cities should also be considered in addition to its urban climate implications.

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“…The Po Valley area is the largest industrial, trading and agricultural area in Italy and has a high population density, and hence it is one of the most polluted areas in Europe. On the average at SPC, NPF events occur on 36 % of the days whilst 33 % are clearly nonevent (NE) days and the probability for NPF events is highest in spring and summer seasons (Hamed et al, 2007;Nieminen et al, 2018).…”

Section: Measurement Site Instrumentation and Datasetmentioning

confidence: 99%

Identification of new particle formation events with deep learning

et al. 2018

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Abstract. New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles. Occurrence of NPF events is typically analyzed by researchers manually from particle size distribution data day by day, which is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time that a deep learning method, i.e., transfer learning of a convolutional neural network (CNN), has successfully been used to automatically classify NPF events into different classes directly from particle size distribution images, similarly to how the researchers carry out the manual classification. The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images was used in the training stage of the CNN and the rest of the images for testing the success of the training. The method was utilized for a 15-yearlong dataset measured at San Pietro Capofiume (SPC) in Italy. We studied the performance of the training with different training and testing of image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., classes 1 and 2) and nonevent days can be identified with an accuracy of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, and thus overlapping or confusion between different classes occurs. Hence, we cross-validated the learning results of CNN with the expert-made classification. The results show that the overlapping occurs, typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into classes 1 and 2 by CNN, indicating that the manual and CNN classifications are very consistent for most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of "quantifiable" (i.e., clear events, classes 1 and 2) and "nonquantifiable" (i.e., weak events, Class 3). This would better describe the difference of those classes: both formation and growth rates can be determined for quantifiable days but not both for nonquantifiable days. Furthermore, we investigated more deeply the days that are classified as clear events by experts and recognized as nonevents by the CNN and vice versa. Clear misclassifications seem to ...

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Global analysis of continental boundary layer new particle formation based on long-term measurements

Cited by 26 publications

References 76 publications

Atmospheric new particle formation and growth: review of field observations

Atmospheric new particle formation and growth: review of field observations

Quantification of an atmospheric nucleation and growth process as a single source of aerosol particles in a city

Identification of new particle formation events with deep learning

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