DOI: 10.1016/j.atmosenv.2017.09.015
Scopus记录号: 2-s2.0-85029725969
论文题名: Minimization of model representativity errors in identification of point source emission from atmospheric concentration measurements
作者: Sharan M ; , Singh A ; K ; , Singh S ; K
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 169 起始页码: 267
结束页码: 277
语种: 英语
英文关键词: Inverse modelling
; Least square
; Linear regression
; Model representativity errors
; Renormalization
; Source identification
Scopus关键词: Inverse problems
; Linear regression
; Inverse modelling
; Least Square
; Renormalization
; Representativity
; Source identification
; Errors
; concentration (composition)
; consumer-resource interaction
; error analysis
; estimation method
; experimental study
; inverse analysis
; least squares method
; measurement method
; parameterization
; point source pollution
; regression analysis
; source parameters
; Article
; atmospheric deposition
; carbon footprint
; concentration (parameters)
; diffusion
; Idaho
; least square analysis
; linear regression analysis
; mathematical computing
; measurement
; methodology
; minimization of model representativity error
; nonpoint source pollution
; priority journal
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: Estimation of an unknown atmospheric release from a finite set of concentration measurements is considered an ill-posed inverse problem. Besides ill-posedness, the estimation process is influenced by the instrumental errors in the measured concentrations and model representativity errors. The study highlights the effect of minimizing model representativity errors on the source estimation. This is described in an adjoint modelling framework and followed in three steps. First, an estimation of point source parameters (location and intensity) is carried out using an inversion technique. Second, a linear regression relationship is established between the measured concentrations and corresponding predicted using the retrieved source parameters. Third, this relationship is utilized to modify the adjoint functions. Further, source estimation is carried out using these modified adjoint functions to analyse the effect of such modifications. The process is tested for two well known inversion techniques, called renormalization and least-square. The proposed methodology and inversion techniques are evaluated for a real scenario by using concentrations measurements from the Idaho diffusion experiment in low wind stable conditions. With both the inversion techniques, a significant improvement is observed in the retrieval of source estimation after minimizing the representativity errors. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82689
Appears in Collections: 气候变化事实与影响
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作者单位: Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India; LMEE, Universite d'Evry-Val d'Essonne, 40 Rue Du Pelvoux, Evry Cedex, France
Recommended Citation:
Sharan M,, Singh A,K,et al. Minimization of model representativity errors in identification of point source emission from atmospheric concentration measurements[J]. Atmospheric Environment,2017-01-01,169