Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique

Turbelin, Grégory; Singh, Sarvesh Kumar; Issartel, Jean-Pierre

doi:10.1002/2014ms000385

Cited by 14 publications

(11 citation statements)

References 23 publications

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“…Equation () shows a linear inverse solution to the underdetermined linear inverse problem (equation ) [ Menke , ]. This is also called as a minimum‐weighted norm solution to equation () [ Turbelin et al , ]), which

Minimize ∥ bolds ∥_{boldW} = \sqrt{bolds boldW {bolds}^{T}}, subject to the constraint μ = boldAs .

…”

Section: Inversion Techniquementioning

confidence: 99%

Reconstruction of an atmospheric tracer source in Fusion Field Trials: Analyzing resolution features

et al. 2015

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Reconstruction of unknown atmospheric releases using measured concentrations is an ill-posed inverse problem. Due to insufficient measurements and dispersion model uncertainties, reliable interpretation of a retrieved source is limited by lack of resolution, nonuniqueness, and instability in the inverse solution. The study presents an optimality analysis, in terms of resolution, stability, and reliability, of an inverse solution given by a recently proposed inversion technique, called as renormalization. The inversion technique is based on an adjoint source-receptor framework and construction of a weight function which interprets a priori information about the unknown release apparent to the monitoring network. The properties of weight function provide a perfect data resolution, maximum model resolution, and minimum variance (or stability) for the retrieved source. The reliability of the retrieved source is interpreted in view of the information derived from the geometry of the monitoring network. The inversion technique and resolution features are evaluated for a point source reconstruction using measurements from a recent dispersion experiment (Fusion Field Trials 2007) conducted at Dugway Proving Ground, Utah. With the real measurements, the point release is reconstructed within an average distance of 23 m from the true release where the average distance of the nearest receptor from the true source was 32 m. In all the trials, the point release is retrieved within 3-60 m Euclidean distance from their true location. The source strength is retrieved within a factor of 1.5 to the true release mass. The posterior uncertainty in the release parameters is observed to be within 20% of their mean value. The source localization features are resolved to its maximum extent feasible with the design of the monitoring network. The sensitivity studies are conducted to highlight the importance of receptors reporting zero concentration measurements and variations in the resolution features of the source retrieval with respect to the various arrangements of the receptors.

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Minimize ∥ bolds ∥_{boldW} = \sqrt{bolds boldW {bolds}^{T}}, subject to the constraint μ = boldAs .

…”

Section: Inversion Techniquementioning

confidence: 99%

Reconstruction of an atmospheric tracer source in Fusion Field Trials: Analyzing resolution features

et al. 2015

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“…However, few have focused on the source reconstruction performance of AQMN in optimal design. Recently, many studies in the literature have explored how to reconstruct source characteristics based on the measurements from a dense AQMN and have analyzed the influence of the AQMN distribution on the back-calculation [17][18][19][20], while only single emission episodes were considered as the concerned objectives in these studies.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Huang

Liu

et al. 2019

Atmosphere

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Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H 2 S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (r d ) and source identification efficiency (r b ). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of r d and r b were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

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“…The technique requires minimal a priori information about the unknown releases and is shown efficient in reconstructing both point as well as areal distributed emission sources [ Sharan et al , ; Kumar et al , , and others]. A unique feature is the utilization of a weight matrix derived from the geometry of the monitoring network which helps to (i) provide an apparent a priori information about unknown releases to the monitoring network [ Sharan et al , ] and (ii) optimize the localization features of the retrieved source [ Turbelin et al , ; Singh et al , ]. The traditional presentation of the technique (see section 2) is different from the other inversion techniques and thus lacks a straightforward comparison with them in terms of a priori information, constraints, and parameters utilized.…”

Section: Introductionmentioning

confidence: 99%

Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations

Singh

Kumar

Rani

et al. 2017

Earth and Space Science

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The study highlights a theoretical comparison and various interpretations of a recent inversion technique, called renormalization, developed for the reconstruction of unknown tracer emissions from their measured concentrations. The comparative interpretations are presented in relation to the other inversion techniques based on principle of regularization, Bayesian, minimum norm, maximum entropy on mean, and model resolution optimization. It is shown that the renormalization technique can be interpreted in a similar manner to other techniques, with a practical choice of a priori information and error statistics, while eliminating the need of additional constraints. The study shows that the proposed weight matrix and weighted Gram matrix offer a suitable deterministic choice to the background error and measurement covariance matrices, respectively, in the absence of statistical knowledge about background and measurement errors. The technique is advantageous since it (i) utilizes weights representing a priori information apparent to the monitoring network, (ii) avoids dependence on background source estimates, (iii) improves on alternative choices for the error statistics, (iv) overcomes the colocalization problem in a natural manner, and (v) provides an optimally resolved source reconstruction. A comparative illustration of source retrieval is made by using the real measurements from a continuous point release conducted in Fusion Field Trials, Dugway Proving Ground, Utah.

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Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique

Cited by 14 publications

References 23 publications

Reconstruction of an atmospheric tracer source in Fusion Field Trials: Analyzing resolution features

Reconstruction of an atmospheric tracer source in Fusion Field Trials: Analyzing resolution features

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations

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