DOI: 10.1016/j.atmosenv.2018.02.016
Scopus记录号: 2-s2.0-85042706756
论文题名: An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China
作者: Huang Z ; , Hu Y ; , Zheng J ; , Zhai X ; , Huang R
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 180 起始页码: 59
结束页码: 68
语种: 英语
英文关键词: Data fusion
; Lateral boundary conditions
; Pearl River Delta region
; Regional air quality modeling
Scopus关键词: Air quality
; Boundary conditions
; Gems
; Interpolation
; Rivers
; Chemical transport models
; Data fusion methods
; Lateral boundary conditions
; Pearl River delta
; Pearl River Delta region
; PM2.5 concentration
; Regional air quality modeling
; Regional transport
; Data fusion
; air quality
; atmospheric modeling
; boundary condition
; concentration (composition)
; data acquisition
; ground-based measurement
; kriging
; numerical method
; particulate matter
; temporal variation
; winter
; air quality
; Article
; China
; information processing
; kriging
; particulate matter
; priority journal
; simulation
; winter
; China
; Guangdong
; Zhujiang Delta
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: Lateral boundary conditions (LBCs) are essential for chemical transport models to simulate regional transport; however they often contain large uncertainties. This study proposes an optimized data fusion approach to reduce the bias of LBCs by fusing gridded model outputs, from which the daughter domain's LBCs are derived, with ground-level measurements. The optimized data fusion approach follows the framework of a previous interpolation-based fusion method but improves it by using a bias kriging method to correct the spatial bias in gridded model outputs. Cross-validation shows that the optimized approach better estimates fused fields in areas with a large number of observations compared to the previous interpolation-based method. The optimized approach was applied to correct LBCs of PM2.5 concentrations for simulations in the Pearl River Delta (PRD) region as a case study. Evaluations show that the LBCs corrected by data fusion improve in-domain PM2.5 simulations in terms of the magnitude and temporal variance. Correlation increases by 0.13–0.18 and fractional bias (FB) decreases by approximately 3%–15%. This study demonstrates the feasibility of applying data fusion to improve regional air quality modeling. © 2018 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82933
Appears in Collections: 气候变化事实与影响
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作者单位: Institute for Environmental and Climate Research, Jinan University, Guangzhou, China; School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, United States
Recommended Citation:
Huang Z,, Hu Y,, Zheng J,et al. An optimized data fusion method and its application to improve lateral boundary conditions in winter for Pearl River Delta regional PM2.5 modeling, China[J]. Atmospheric Environment,2018-01-01,180