DOI: 10.1016/j.atmosenv.2017.05.046
Scopus记录号: 2-s2.0-85020269629
论文题名: Bayesian identification of a single tracer source in an urban-like environment using a deterministic approach
作者: Xue F ; , Li X ; , Zhang W
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 164 起始页码: 128
结束页码: 138
语种: 英语
英文关键词: Bayesian inference
; CFD
; Performance evaluation
; Source term estimation
; Urban dispersion
Scopus关键词: Bayesian networks
; Benchmarking
; Computational fluid dynamics
; Dispersions
; Inference engines
; Location
; Particulate emissions
; Probability distributions
; Stochastic systems
; Waste disposal
; Wind tunnels
; Bayesian identification
; Bayesian inference
; Computational fluid dynamics modeling
; Marginal posterior distribution
; Performance evaluation
; Source term estimation
; Source-receptor relationships
; Urban dispersion
; Probability density function
; atmospheric pollution
; Bayesian analysis
; computational fluid dynamics
; concentration (composition)
; dispersion
; emission
; environmental conditions
; error analysis
; geometry
; performance assessment
; point source pollution
; probability density function
; sampling
; source apportionment
; stochasticity
; tracer
; urban area
; wind tunnel
; Article
; atmospheric dispersion
; Bayes theorem
; calculation
; computational fluid dynamics
; geometry
; Markov chain
; priority journal
; urban area
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: This paper presents a two-step deterministic approach for identifying an unknown point source with a constant emission rate in built-up urban areas. The analytic form of the marginal posterior probability density function of the source location is derived to estimate the source location. The emission rate is then estimated using the conditional posterior distribution. Such a procedure deconstructs the calculation of the joint posterior distribution of the source parameters into calculations of two separate distributions and can thus be easily calculated directly and accurately without stochastic sampling. The proposed method is tested using real data obtained in two wind tunnel scenarios of contaminant dispersion in typical urban geometries represented by block arrays. Computational fluid dynamics (CFD) modeling and the adjoint equations are used to calculate the building-resolving source-receptor relationship required in the identification. The estimated source parameters in both cases are close to true values. In both cases, the source locations are identified with errors less than half of the block size, and the emission rates are well estimated, with only slight overestimation. Moreover, in this paper, we test two potential performance indicators for a posteriori evaluation of the credibility of a certain estimation. One indicator is the size of the highest probability density region, and the other is the angle between the observed and predicted concentration vectors, which is derived from the analytic form of the marginal posterior distribution of the source location. Synthetic concentration data are generated to test the validity of both indicators. It is found that the former is not appropriate for denoting the credibility of estimations but that the latter shows a strong correlation with estimation performance and is likely to be an effective performance indicator for Bayesian source term estimation. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82741
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Building Science, School of Architecture, Tsinghua University, Beijing, China; Beijing Key Lab of Indoor Air Quality Evaluation and Control, Beijing, China; Department of Architecture, Tokyo Polytechnic University, Japan
Recommended Citation:
Xue F,, Li X,, Zhang W. Bayesian identification of a single tracer source in an urban-like environment using a deterministic approach[J]. Atmospheric Environment,2017-01-01,164