DOI: 10.1016/j.atmosenv.2015.02.004
Scopus记录号: 2-s2.0-84922475892
论文题名: Improvement of PM10 prediction in East Asia using inverse modeling
作者: Koo Y ; -S ; , Choi D ; -R ; , Kwon H ; -Y ; , Jang Y ; -K ; , Han J ; -S
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 106 起始页码: 318
结束页码: 328
语种: 英语
英文关键词: CTM
; East asia
; Emissions inventory
; Inverse modeling
; PM10
Scopus关键词: Air quality
; Atmospheric movements
; Bayesian networks
; Dust
; Forecasting
; Inverse problems
; Particulate emissions
; Quality management
; Uranium
; CTM
; East Asia
; Emissions inventory
; Inverse modeling
; PM10
; Particles (particulate matter)
; air quality
; anthropogenic source
; atmospheric modeling
; concentration (composition)
; emission inventory
; particulate matter
; prediction
; urban area
; aerosol
; air pollution
; air quality
; Article
; China
; desert
; inverse modeling
; Korea
; Mongolia
; nonbiological model
; particulate matter
; prediction
; priority journal
; seasonal variation
; simulation
; soil pollution
; vegetation
; China
; Korea
; Mongolia
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: Aerosols from anthropogenic emissions in industrialized region in China as well as dust emissions from southern Mongolia and northern China that transport along prevailing northwestern wind have a large influence on the air quality in Korea. The emission inventory in the East Asia region is an important factor in chemical transport modeling (CTM) for PM10 (particulate matters less than 10[U+339B] in aerodynamic diameter) forecasts and air quality management in Korea. Most previous studies showed that predictions of PM10 mass concentration by the CTM were underestimated when comparing with observational data. In order to fill the gap in discrepancies between observations and CTM predictions, the inverse Bayesian approach with Comprehensive Air-quality Model with extension (CAMx) forward model was applied to obtain optimized a posteriori PM10 emissions in East Asia. The predicted PM10 concentrations with a priori emission were first compared with observations at monitoring sites in China and Korea for January and August 2008. The comparison showed that PM10 concentrations with a priori PM10 emissions for anthropogenic and dust sources were generally under-predicted. The result from the inverse modeling indicated that anthropogenic PM10 emissions in the industrialized and urbanized areas in China were underestimated while dust emissions from desert and barren soil in southern Mongolia and northern China were overestimated. A priori PM10 emissions from northeastern China regions including Shenyang, Changchun, and Harbin were underestimated by about 300% (i.e., the ratio of a posteriori to a priori PM10 emission was a factor of about 3). The predictions of PM10 concentrations with a posteriori emission showed better agreement with the observations, implying that the inverse modeling minimized the discrepancies in the model predictions by improving PM10 emissions in East Asia. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81930
Appears in Collections: 气候变化事实与影响
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作者单位: Dept. of Environmental and Energy Eng., Anyang University, Anyang, Gyeonggi-do, South Korea; Dept. of Computer Eng., Anyang University, Anyang, Gyeonggi-do, South Korea; Dept. of Environmental and Energy Eng., University of Suwon, Gyeonggi-do, South Korea; Dept. of Climate and Air Quality, National Institute of Environmental Research, Incheon, South Korea
Recommended Citation:
Koo Y,-S,, Choi D,et al. Improvement of PM10 prediction in East Asia using inverse modeling[J]. Atmospheric Environment,2015-01-01,106