Inverse calculation approaches for source determination in hazardous chemical releases

Zheng, Xiaoping; Chen, Zengqiang

doi:10.1016/j.jlp.2011.01.002

Cited by 44 publications

(19 citation statements)

References 35 publications

(57 reference statements)

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“…Basically, this function must be a norm function F : ℝ m → ℝ describing the degree of similarity between estimated measurements given by the model at each potential source location, that is, the components of the vector μ est ( q , x ) = q a ( x ), and observed values measured at the monitoring locations, that is, the components of the vector μ obs . In order to minimize such a cost function, a variety of methods has been proposed (see Hutchinson et al, ; Zheng & Chen, ). The next section focuses, and generalizes, the one used by Issartel et al (), Sharan et al (, ), or Singh and Rani ().…”

Section: Problem Statementmentioning

confidence: 99%

“…In the context of atmospheric releases, numerous techniques have been proposed to produce solutions to STE problems. They can be broadly classified as either Bayesian‐based or optimization‐based methods (Hutchinson et al, ; Zheng & Chen, ). Bayesian‐based approaches (Winiarek et al, ; Xue et al, ; Yee et al, , among others) provide posterior probability density functions of the source parameters.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization‐Based Approach for Source Term Estimations of Atmospheric Releases

Turbelin

Singh

Ngae

et al. 2018

Earth and Space Science

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This article highlights an optimization‐based approach to estimate the main characteristics (i.e., location and intensity) that describe the source of a release of a hazardous material (e.g., a chemical or biological agent) in the atmosphere. The method, which uses, as input data, concentration measurements provided by a network of sensors (and some additional meteorological data), is first formalized in the general framework of parametric source term estimation inverse problems. Some simple and new geometric interpretations of the method are proposed. Next, it is shown that the approach can also provide a realistic understanding of the uncertainties in the estimation of the characteristics of the source. Then, a strategy for an optimal deployment of mobile sensors in hazardous materials operations is proposed. To conclude, the source estimation approach and the network design strategy are evaluated with data from a wind tunnel experiment conducted at the Environmental Flow Research Center at the University of Surrey, UK.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimization‐Based Approach for Source Term Estimations of Atmospheric Releases

Turbelin

Singh

Ngae

et al. 2018

Earth and Space Science

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“…Chai et al (2015) developed the emission inversion method based on a Lagrangian model and a cost function using the transfer coefficient matrix, and conducted sensitivity analysis for the inverse estimation by using the original concentration c and ln(c) differences between model and observations in the cost function. By comparison, Zheng and Chen (2011) suggest that optimization modeling methods has advantages over probability modeling methods on source inversion. However, many estimates have been shown to be quite sensitive to the prior information on the source term used in the inversion.…”

Section: Identification Of Radioactive Sourcesmentioning

confidence: 99%

Predicting and Controlling Nuclear Accident Hazards: Issues and Challenges

Huang

Zhang

Yang³

et al. 2016

Aerosol Air Qual. Res.

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Global nuclear security is threatened by nuclear accidents and the purposeful use of nuclear weapons. Accordingly, atmospheric pollution prediction and control for nuclear accidents, including identifying sources in nuclear or radiological incidents, predicting hazards to persons and environments, and optimally controlling accident hazards, are current areas of nuclear security research. Source inversion, hazard prediction, and optimal control are three interrelated key issues for nuclear accident emergencies. Although progress has been made in hazard prediction for nuclear accidents since the 1970s and some source inversion methods were presented after the Fukushima nuclear accident, optimal control methods are rarely reported, source inversion methods are less practical, and prediction accuracy remains unsatisfactory. Thus, novel theories are required for optimal control and source inversion for nuclear accidents, and to develop methods for simulating the influences of radioactive plume dispersion and deposition under complex meteorological and terrain conditions. This work reviews the current progress, uncertainties, and research needs in nuclear security. In addition, a rapid source inversion method based on the Lagrangian model is developed and implemented in a test case. To address future challenges, an innovative architecture for Atmospheric Pollution Prediction and Optimal Control System for nuclear accidents (APPOCS) is proposed, and the perspectives are generalized to promote future research on nuclear accident hazard prediction and optimal control. At this time, forward-looking ideas and revolutionary perspectives are required to foster nuclear security research in the academic community.

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“…Emissions and winds are constrained in an inverse method by minimizing differences between dispersion model predictions and observations of materials transported and deposited downwind from the source location (e.g., Davoine and Bocquet, 2007;Zheng and Chen, 2011). Building upon our previous work using Bayesian inverse modeling to estimate regionalscale greenhouse gas emissions and meteorological uncertainty in an urban-scale dispersion experiment , we developed an ensemble-based inverse modeling system for analyzing nuclear power plant dispersion events.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

et al. 2017

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Abstract. Probability distribution functions (PDFs) of model inputs that affect the transport and dispersion of a trace gas released from a coastal California nuclear power plant are quantified using ensemble simulations, machine-learning algorithms, and Bayesian inversion. The PDFs are constrained by observations of tracer concentrations and account for uncertainty in meteorology, transport, diffusion, and emissions. Meteorological uncertainty is calculated using an ensemble of simulations of the Weather Research and Forecasting (WRF) model that samples five categories of model inputs (initialization time, boundary layer physics, land surface model, nudging options, and reanalysis data). The WRF output is used to drive tens of thousands of FLEXPART dispersion simulations that sample a uniform distribution of six emissions inputs. Machine-learning algorithms are trained on the ensemble data and used to quantify the sources of ensemble variability and to infer, via inverse modeling, the values of the 11 model inputs most consistent with tracer measurements. We find a substantial ensemble spread in tracer concentrations (factors of 10 to 10 3 ), most of which is due to changing emissions inputs (about 80 %), though the cumulative effects of meteorological variations are not negligible. The performance of the inverse method is verified using synthetic observations generated from arbitrarily selected simulations. When applied to measurements from a controlled tracer release experiment, the inverse method satisfactorily determines the location, start time, duration and amount. In a 2 km × 2 km area of possible locations, the actual location is determined to within 200 m. The start time is determined to within 5 min out of 2 h, and the duration to within 50 min out of 4 h. Over a range of release amounts of 10 to 1000 kg, the estimated amount exceeds the actual amount of 146 kg by only 32 kg. The inversion also estimates probabilities of different WRF configurations. To best match the tracer observations, the highest-probability cases in WRF are associated with using a late initialization time and specific reanalysis data products.

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Inverse calculation approaches for source determination in hazardous chemical releases

Cited by 44 publications

References 35 publications

An Optimization‐Based Approach for Source Term Estimations of Atmospheric Releases

An Optimization‐Based Approach for Source Term Estimations of Atmospheric Releases

Predicting and Controlling Nuclear Accident Hazards: Issues and Challenges

Bayesian inverse modeling of the atmospheric transport and emissions of a controlled tracer release from a nuclear power plant

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