Sequential Monte Carlo in Bayesian Assessment of Contaminant Source Localization Based on the Sensors Concentration Measurements

Wawrzyńczak, A.; Kopka, Piotr; Borysiewicz, M.

doi:10.1007/978-3-642-55195-6_38

Cited by 14 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Back-calculation methods in the literature were investigated to find a proper method for our case study. Many inverse methods have been reported in the literature for the determination of pollution source parameters, such as genetic algorithms [1,8,9], simulated annealing algorithms [2,10], pattern search method [11], least square method [4,12], and probability modeling methods, including Markov chain Monte Carlo [3,7], and Sequential Monte Carlo [13]. These algorithms were used to find an optimal result of unknown parameters by looping over their solution domain, although they were with heavy computational loads.…”

Section: Introductionmentioning

confidence: 99%

“…There were many candidate models, such as the Gaussian dispersion model, Lagrangian stochastic model, and computational fluid dynamics (CFD) model. Gaussian dispersion models, especially Gaussian plume model [13][14][15] and Gaussian puff model [7,16] were widely used as the forward dispersion simulation of air pollutants. Najafi and Gilbert and Annunzio et al employed the Lagrangian puff models as the forward models [17,18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Liu

Huang

et al. 2018

Atmosphere

View full text Add to dashboard Cite

Precise source identification for ambient pollution incidents in industrial parks were often difficult due to limited measurements. Source area analysis method was one of the applicable source identification methods, which could provide potential source areas under these circumstances. However, a source area usually covered several sources and the method was unable to identify the real one. This article introduces a case study on the statistical source identification of methyl mercaptan based on the long-term measurements, in 2014, in an industrial park. A procedure for statistical source area analysis was established, which contains independent pollution episode extraction, source area calculation scenario definition, meteorological data selection, and source area statistical analysis. A total of 414 violation records were detected by five monitors inside the park. Three kinds of calculation scenarios were found and, finally, three key source areas were revealed. The typical scenarios of source area calculations were described in detail. The characteristics of the statistical source areas for all pollution episodes were examined. Finally, the applicability of the method, as well as the source of uncertainties, was discussed. This study shows that more concentrated source areas can be identified through the statistical source area method if several excessive emission sources exist in an industrial park.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Liu

Huang

et al. 2018

Atmosphere

View full text Add to dashboard Cite

show abstract

“…In e.g. [10] authors presented the reconstruction of the airborne contaminant source utilizing the Bayesian approach in conjunction with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC). A comprehensive literature review of past works on solutions of the inverse problem for atmospheric contaminant releases can be found in (e.g.…”

Section: Introductionmentioning

confidence: 99%

Testing the Algorithm of Area Optimization by Binary Classification with Use of Three State 2D Cellular Automata in Layers

Szaban

Wawrzyńczak

2018

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

The paper is dedicated to a new algorithm of optimization in the sense of the area. Proposed method joins a few issues. First one is utilizing data from the set of sensors monitoring the area put into optimization. The second one is using the classification method based on two-dimensional threestate cellular automata, working on the data reported by the sensors. This method classifies all points of the area based on the data received from the sensors and designates optimal subarea. The third issue is applying the categorization layers to the data received from sensors. Such, approach gives a possibility to specify the areas in the different levels and, in consequence, after analysis, optimal subarea or subarea including the optimal point can be designated. This method can be used in different optimization tasks, starting from simple one as optimization of ndimensional function, through specifying the contaminated area utilizing data from mobile sensors and finally estimating the contamination source-term. In this paper are presented results of testing for the proposed algorithm on a few selected functions from the set of dedicated for this purpose.

show abstract

“…The effectiveness of MCMC in the localization of the atmospheric contamination source based on the synthetic experiment data was presented in [4], [5]. The advantage of the Sequential Monte Carlo over the MCMC in the estimation of the probable values of the source coordinates was presented in [6].…”

Section: Introductionmentioning

confidence: 99%

Data-driven Genetic Algorithm in Bayesian estimation of the abrupt atmospheric contamination

Wawrzyńczak

Jaroszyński

Borysiewicz

2014

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

We have applied the methodology combining Bayesian inference with Genetic algorithm (GA) to the problem of the atmospheric contaminant source localization. The algorithms input data are the on-line arriving information about concentration of given substance registered by sensors' network. To achieve rapid-response event reconstructions the fast-running Gaussian plume dispersion model is adopted as the forward model. The proposed GA scan 5-dimensional parameters' space searching for the contaminant source coordinates (x,y), release strength (Q) and atmospheric transport dispersion coefficients. Based on the synthetic experiment data the GA parameters, best suitable for the contamination source localization algorithm performance were identified. We demonstrate that proposed GA configuration can successfully point out the parameters of abrupt contamination source. Results indicate the probability of a source to occur at a particular location with a particular release strength. We propose the termination criteria based on the probabilistic requirements regarding the parameters' value.

show abstract

Sequential Monte Carlo in Bayesian Assessment of Contaminant Source Localization Based on the Sensors Concentration Measurements

Cited by 14 publications

References 7 publications

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Identifying Key Potential Source Areas for Ambient Methyl Mercaptan Pollution Based on Long-Term Environmental Monitoring Data in an Industrial Park

Testing the Algorithm of Area Optimization by Binary Classification with Use of Three State 2D Cellular Automata in Layers

Data-driven Genetic Algorithm in Bayesian estimation of the abrupt atmospheric contamination

Contact Info

Product

Resources

About