气候变化领域集成服务门户(CCPortal): New particle formation event detection with Mask R-CNN

CCPortal

DOI	10.5194/acp-22-1293-2022
	New particle formation event detection with Mask R-CNN
	Su, Peifeng; Joutsensaari, Jorma; Dada, Lubna; Zaidan, Martha Arbayani; Nieminen, Tuomo; Li, Xinyang; Wu, Yusheng; Decesari, Stefano; Tarkoma, Sasu; Petaja, Tuukka; Kulmala, Markku; Pellikka, Petri
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	1293
结束页码	1309
卷号	22 期号:2 页码:17
英文摘要	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 (Hyytiala, 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.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000751160400001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273127
作者单位	University of Helsinki; University of Helsinki; University of Eastern Finland; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Nanjing University; University of Helsinki; Consiglio Nazionale delle Ricerche (CNR); Istituto di Scienze dell'Atmosfera e del Clima (ISAC-CNR); University of Helsinki
推荐引用方式 GB/T 7714	Su, Peifeng,Joutsensaari, Jorma,Dada, Lubna,et al. New particle formation event detection with Mask R-CNN[J],2022,22(2):17.
APA	Su, Peifeng.,Joutsensaari, Jorma.,Dada, Lubna.,Zaidan, Martha Arbayani.,Nieminen, Tuomo.,...&Pellikka, Petri.(2022).New particle formation event detection with Mask R-CNN.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(2),17.
MLA	Su, Peifeng,et al."New particle formation event detection with Mask R-CNN".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.2(2022):17.