DOI: 10.1016/j.atmosenv.2017.12.013
Scopus记录号: 2-s2.0-85038349900
论文题名: Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea
作者: Lee J ; , Kim K ; -Y
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 175 起始页码: 199
结束页码: 209
语种: 英语
英文关键词: Back trajectory
; CSEOF
; HYSPLIT model
; K-means algorithm
; PM10
Scopus关键词: Regression analysis
; Transportation routes
; Back trajectories
; CSEOF
; HYSPLIT model
; k-Means algorithm
; PM10
; Clustering algorithms
; algorithm
; cluster analysis
; concentration (composition)
; data set
; numerical model
; particulate matter
; regression analysis
; trajectory
; air
; Article
; cluster analysis
; desert
; dust
; industrial area
; meteorological phenomena
; meteorology
; particulate matter
; priority journal
; regression analysis
; simulation
; South Korea
; spring
; traffic and transport
; China
; Gobi Desert
; Seoul [South Korea]
; South Korea
; Taklimakan Desert
; Xinjiang Uygur
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: CSEOF analysis is applied for the springtime (March, April, May) daily PM10 concentrations measured at 23 Ministry of Environment stations in Seoul, Korea for the period of 2003–2012. Six meteorological variables at 12 pressure levels are also acquired from the ERA Interim reanalysis datasets. CSEOF analysis is conducted for each meteorological variable over East Asia. Regression analysis is conducted in CSEOF space between the PM10 concentrations and individual meteorological variables to identify associated atmospheric conditions for each CSEOF mode. By adding the regressed loading vectors with the mean meteorological fields, the daily atmospheric conditions are obtained for the first five CSEOF modes. Then, HYSPLIT model is run with the atmospheric conditions for each CSEOF mode in order to back trace the air parcels and dust reaching Seoul. The K-means clustering algorithm is applied to identify major source regions for each CSEOF mode of the PM10 concentrations in Seoul. Three main source regions identified based on the mean fields are: (1) northern Taklamakan Desert (NTD), (2) Gobi Desert and (GD), and (3) East China industrial area (ECI). The main source regions for the mean meteorological fields are consistent with those of previous study; 41% of the source locations are located in GD followed by ECI (37%) and NTD (21%). Back trajectory calculations based on CSEOF analysis of meteorological variables identify distinct source characteristics associated with each CSEOF mode and greatly facilitate the interpretation of the PM10 variability in Seoul in terms of transportation route and meteorological conditions including the source area. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83073
Appears in Collections: 气候变化事实与影响
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作者单位: School of Earth and Environmental Sciences, College of Natural Sciences, Seoul National University, Seoul, South Korea
Recommended Citation:
Lee J,, Kim K,-Y. Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea[J]. Atmospheric Environment,2018-01-01,175