Keywords
point source, puff model, bayesian statistics, probability density function
Start Date
1-7-2008 12:00 AM
Abstract
Detection of a possible source of air pollution as a combination of measurementsand inverse modelling, based on Bayesian statistics, has been proposed. The simplicity ofthe approach and its numerical efficiency qualifies this approach for the problem, especiallywhen it is used in the operational mode. The method has been examined in its simplestform, with a single source and the implicit assumption that we know the moment of therelease. The position of the possible source has been found as the maximum of theprobability density function from an ensemble of possible sources that cover substantial partof the model domain. Members of the ensemble have been generated using a puff model.Search for the position consists of series of iterations converging toward the position of thesource.
Iterative Inverse Modelling Method for Locating a Source of Air Pollution and Its Robustness
Detection of a possible source of air pollution as a combination of measurementsand inverse modelling, based on Bayesian statistics, has been proposed. The simplicity ofthe approach and its numerical efficiency qualifies this approach for the problem, especiallywhen it is used in the operational mode. The method has been examined in its simplestform, with a single source and the implicit assumption that we know the moment of therelease. The position of the possible source has been found as the maximum of theprobability density function from an ensemble of possible sources that cover substantial partof the model domain. Members of the ensemble have been generated using a puff model.Search for the position consists of series of iterations converging toward the position of thesource.
