An Inverse Method for Determining Source Characteristics for Emergency Response Applications

Rudd, Alison; Robins, Alan; Lepley, J.J.; Belcher, Stephen E.

doi:10.1007/s10546-012-9712-y

Cited by 23 publications

(33 citation statements)

References 14 publications

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“…All the data supporting Figures 1 and 2 have been published in a research article [Rudd et al, 2012] http://dx.doi. org/10.1007/s10546-012-9712-y.…”

Section: Acknowledgmentsmentioning

confidence: 99%

“…The goal is not to analyze the data set but rather to highlight the optimal reconstruction features of the method. [Rudd et al, 2012]: source and receptors positions for configurations 2 and 4 (green filled circles and blue filled triangles). For determining the source characteristics, the full computational domain (not represented here) extends 23H downstream from the actual source location.…”

Section: Introductionmentioning

confidence: 99%

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

Turbelin

Singh

Issartel³

2014

J Adv Model Earth Syst

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In the event of an accidental or intentional contaminant release in the atmosphere, it is imperative, for managing emergency response, to diagnose the release parameters of the source from measured data. Reconstruction of the source information exploiting measured data is called an inverse problem. To solve such a problem, several techniques are currently being developed. The first part of this paper provides a detailed description of one of them, known as the renormalization method. This technique, proposed by Issartel (2005), has been derived using an approach different from that of standard inversion methods and gives a linear solution to the continuous Source Term Estimation (STE) problem. In the second part of this paper, the discrete counterpart of this method is presented. By using matrix notation, common in data assimilation and suitable for numerical computing, it is shown that the discrete renormalized solution belongs to a family of well-known inverse solutions (minimum weighted norm solutions), which can be computed by using the concept of generalized inverse operator. It is shown that, when the weight matrix satisfies the renormalization condition, this operator satisfies the criteria used in geophysics to define good inverses. Notably, by means of the Model Resolution Matrix (MRM) formalism, we demonstrate that the renormalized solution fulfils optimal properties for the localization of single point sources. Throughout the article, the main concepts are illustrated with data from a wind tunnel experiment conducted at the Environmental Flow Research Centre at the University of Surrey, UK.

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“…All the data supporting Figures 1 and 2 have been published in a research article [Rudd et al, 2012] http://dx.doi. org/10.1007/s10546-012-9712-y.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Turbelin

Singh

Issartel³

2014

J Adv Model Earth Syst

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“…There has been renewed interest in recent years, in using inverse atmospheric dispersion methods as part of a top-down analysis, to estimate the source location and strength of greenhouse gas (GHG) emissions [1]- [16]. Development of accurate instruments [17]- [19] for measuring temporally resolved greenhouse gas concentrations and the ability to mount them in mobile vans, airplanes and towers has enabled us to conduct such inversion analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, Rudd et al [16], studied the sensitivities of predicting source strength and location to the number of downwind sensors, sensor configuration, averaging time and noise in the input data. Data from wind tunnel experiments, in which a tracer gas (propane) was released into the air from a single source location, were used to test their inverse model.…”

Section: Introductionmentioning

confidence: 99%

“…Using data from the wind tunnel experiment, dispersion coefficients and wind speed necessary in the Gaussian plume model were customized to provide the best possible model of downwind concentrations. Rudd et al estimated that the Gaussian plume model introduced an error of 1.5 ppmv into estimates of downwind concentration when the wind tunnel experiment was assumed to have a scale of 1:500, corresponding to a reference wind speed of 10 m s −1 at 32 m, a boundary layer height of 500 m and � � a source strength of 0.1 m 3 s −1 [16]. It was noted that uncertainties in wind speed and dispersion coefficients will be greater in field applications than in the controlled wind tunnel experiment, and therefore greater model error would be expected [16].…”

Section: Introductionmentioning

confidence: 99%

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Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models

Prasad¹,

Pintar²,

Hu³

et al. 2015

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Recent development of accurate instruments for measuring greenhouse gas concentrations and the ability to mount them in ground-based vehicles has provided an opportunity to make temporally and spatially resolved measurements in the vicinity of suspected source locations, and for subsequently estimating the source location and strength. The basic approach of us ing downwind atmospheric measurements in an inversion methodology to predict the source strength and location is an ill-posed problem and results in large uncertainty. In this report, we present a new measurement methodology for reducing the uncertainty in predicting source strength from downwind measurements associated with inverse modeling. In order to demon strate the approach, an inversion methodology built around a plume dispersion model is de veloped. Synthetic data derived from an assumed source distribution is used to compare and contrast the predicted source strength and location. The effect of introducing various levels of noise in the synthetic data or uncertainty in meteorological variables on the inversion method ology is studied. Results indicate that the use of noisy measurement data had a small effect on the total predicted source strength, but gave rise to several spurious sources (in many cases 8-10 sources were detected, while the assumed source distribution only consisted of 2 sources). Use of noisy measurement data for inversion also introduced large uncertainty in the location of the predicted sources. A mathematical model for estimating an upper bound on the un certainty, and a bootstrap statistical approach for determining the variability in the predicted source distribution is demonstrated. The new measurement methodology, which involves using measurement data from two or more wind directions, combined together as part of a single inversion process is presented. Results of the bootstrap process indicated that the uncertainty in locating sources reduced significantly when measurements are made using the new proposed measurement approach. The proposed measurement system can be significant in determining emission inventories in urban domains at a high level of reliability, and for studying the role of remediation measures.

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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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An Inverse Method for Determining Source Characteristics for Emergency Response Applications

Cited by 23 publications

References 14 publications

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

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

Greenhouse Gas Emissions and Dispersion 3. Reducing Uncertainty in Estimating Source Strength and Location through Plume Inversion Models

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

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