Keywords
non-linear ill posed problem, inverse air pollution model, parameter estimation
Start Date
1-7-2004 12:00 AM
Abstract
This paper presents the development of an inverse model that may be used to estimatethe source term parameters for a polluting gas released into the atmosphere from a point above theground. The model uses measured pollution concentrations at observation sites on the ground aswell as meteorological data such as wind speed and cloud cover. The inverse model is formulated asa least- squares minimisation problem coupled with the solution of an advection-dispersion equation.The minimisation problem where the pollutants are released instantaneously is well-posed andthe source term is calculated with reasonable accuracy. However, the problem with a non-steadyextended release source is ill-posed; consequently, its solution is extremely sensitive to errors inthe measurement data. Tikhonov’s regularisation, which stabilises the solution process, is used toovercome the ill-posedness of this problem and the regularisation parameter is estimated using theproperties of the non-linear L-curve, and Wahhba’s leaving-out-one lemma. Finally, the accuracyof the model is examined by imposing normally-distributed relative noise into concentration datagenerated by the forward model.
Source Parameter Estimation of Atmospheric Pollution from Accidental Gas Releases
This paper presents the development of an inverse model that may be used to estimatethe source term parameters for a polluting gas released into the atmosphere from a point above theground. The model uses measured pollution concentrations at observation sites on the ground aswell as meteorological data such as wind speed and cloud cover. The inverse model is formulated asa least- squares minimisation problem coupled with the solution of an advection-dispersion equation.The minimisation problem where the pollutants are released instantaneously is well-posed andthe source term is calculated with reasonable accuracy. However, the problem with a non-steadyextended release source is ill-posed; consequently, its solution is extremely sensitive to errors inthe measurement data. Tikhonov’s regularisation, which stabilises the solution process, is used toovercome the ill-posedness of this problem and the regularisation parameter is estimated using theproperties of the non-linear L-curve, and Wahhba’s leaving-out-one lemma. Finally, the accuracyof the model is examined by imposing normally-distributed relative noise into concentration datagenerated by the forward model.
