Uncertainty Analysis in Atmospheric Dispersion Modeling

Rao, K. Shankar

doi:10.1007/s00024-005-2697-4

Cited by 71 publications

(28 citation statements)

References 58 publications

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“…Noise can arise from errors in meteorological data, sensing, atmospheric turbulence or modelling discrepancies (Rao (2005)). In this work, meteorological data and errors due to atmospheric turbulence are neglected.…”

Section: Observationmentioning

confidence: 99%

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Hutchinson

Wen-hua

2017

IFAC-PapersOnLine

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There has been a strong interest in emergency planning in response to an attack or accidental release of harmful chemical, biological, radiological or nuclear substances. Under such circumstances, it is of paramount importance to determine the location and release rate of the hazardous source to forecast the future harm it may cause and employ methods to minimize the disturbance. In this paper, a sensor data collection strategy is proposed whereby an autonomous mobile sensor is guided to address such a problem with a high degree of accuracy and in a short amount of time. First, the parameters of the release source are estimated using the Markov chain Monte Carlo sampling approach. The most informative manoeuvre from the set of possible choices is then selected using the concept of maximum entropy sampling. Numerical simulations demonstrate the superior performance of the proposed algorithm compared to traditional approaches in terms of estimation accuracy and the number of measurements required.

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Section: Observationmentioning

confidence: 99%

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Hutchinson

Wen-hua

2017

IFAC-PapersOnLine

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“…Finally, η (4) J describes the noise inherent in the sensor (essentially measurement or instrument error). Rao (2005) discusses the nature of these four types of error with respect to characterization of uncertainties in atmospheric dispersion models, and provides a comprehensive review of sensitivity and/or uncertainty analysis methods that have been used to quantify and reduce them. Henceforth, all the various error contributions to the noise term are simply lumped together and denoted by e J [see Eqs.…”

Section: Model For Concentration Observationsmentioning

confidence: 99%

“…In general, e J consist of errors (e.g., input, stochastic, and measurement) and any real signal in the data that cannot be explained by the model. The random error e J can be split into four terms as discussed by Rao (2005), so…”

Section: Model For Concentration Observationsmentioning

confidence: 99%

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

Yee

2008

Boundary-Layer Meteorol

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We address the inverse problem of source reconstruction for the difficult case of multiple sources when the number of sources is unknown a priori. The problem is solved using a Bayesian probabilistic inferential framework in which Bayesian probability theory is used to derive the posterior probability density function for the number of sources and for the parameters (e.g., location, emission rate, release time and duration) that characterize each source. A mapping (source-receptor relationship) that relates a multiple source distribution to the concentration measurements made by an array of detectors is formulated based on a forward-time Lagrangian stochastic model. A computationally efficient methodology for determination of the likelihood function for the problem, based on an adjoint representation of the source-receptor relationship and realized in terms of a backward-time Lagrangian stochastic model, is described. An efficient computational algorithm based on a parallel tempered Metropolis-coupled reversible-jump Markov chain Monte Carlo (MCMC) method is formulated and implemented to draw samples from the posterior probability density function of the source parameters. This methodology allows the MCMC method to initiate jumps between the hypothesis spaces corresponding to different numbers of sources in the source distribution and, thereby, allows a sample from the full joint posterior distribution of the number of sources and the parameters for each source to be obtained. The proposed methodology for source reconstruction is tested using synthetic concentration data generated for cases involving two and three unknown sources.

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“…Meteorological data are the cause of more than half of the uncertainty in predicting hourly concentrations with dispersion models (Rao, 2005). In addition to the natural variability of the atmosphere, the use of meteorological data taken at non-representative locations, the use of inappropriate instruments or non-systematic recording and data storage are some of the most influential factors affecting a dispersion model's uncertainty.…”

Section: Characterization Of Meteorologymentioning

confidence: 99%

Uncertainty propagation of meteorological and emission data in modeling pollutant dispersion in the atmosphere

Diez¹,

Barra²,

Crespo³

et al. 2014

ing.inv.

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Propagación de la incertidumbre de los datos meteorológicos y de emisión en el modelado de la dispersión de contaminantes en la atmósfera S. Diez 1 , E. Barra 2 , F. Crespo 3 and J. Britch 4 ABSTRACTVariability is true heterogeneity existing within a population that cannot be reduced or eliminated by more or better determinations. Uncertainty represents ignorance about poorly characterized phenomena, but it can be reduced by collecting more data. The aim of this paper was to study the impact of the variability and uncertainty of the main variables, i.e., emissions and meteorology, of the PM10 concentration caused by a point source located at Malagueño (Córdoba, Argentina). To perform this analysis, a scheme was developed using the USEPA Industrial Source Complex model algorithms with a Monte Carlo methodology. Using a simulation with one hundred thousand iterations, the concentration distribution was obtained and showed that the uncertainty in wind direction had the greatest impact on the estimates.Keywords: Uncertainty, Variability, Monte Carlo, PM10. RESUMENLa variabilidad es la heterogeneidad real dentro de una población, que no puede ser reducida ni eliminada por más o mejores determinaciones. La incertidumbre representa la ignorancia acerca de un fenómeno pobremente caracterizado, pero que puede reducirse mediante la recopilación de más datos. El objetivo de este trabajo es estimar la concentración de PM10 provocada por las emisiones de una fuente puntual ubicada en Malagueño (Córdoba, Argentina), considerando la variabilidad y la incertidumbre de la meteorología y las emisiones. Para abordar este análisis fue desarrollado un método que utiliza los algoritmos del modelo Industrial Source Complex de la USEPA junto a la metodología Monte Carlo. Con cien mil iteraciones se obtuvo la distribución de concentraciones, encontrándose que la incertidumbre en la dirección del viento es la de mayor incidencia sobre las estimaciones.

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Uncertainty Analysis in Atmospheric Dispersion Modeling

Cited by 71 publications

References 58 publications

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Theory for Reconstruction of an Unknown Number of Contaminant Sources using Probabilistic Inference

Uncertainty propagation of meteorological and emission data in modeling pollutant dispersion in the atmosphere

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