Research Letters in the Information and Mathematical Sciences

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/4332

Research Letters welcomes papers from staff and graduate students at Massey University in the areas of: Computer Science, Information Science, Mathematics, Statistics and the Physical and Engineering Sciences. Research letters is a preprint series that accepts articles of completed research work, technical reports, or preliminary results from ongoing research. After editing, articles are published online and can be referenced, or handed out at conferences. Copyright remains with the authors and the articles can be used as preprints to academic journal publications or handed out at conferences. Editors Dr Elena Calude Dr Napoleon Reyes The guidelines for writing a manuscript can be accessed here.

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Author: search.filters.author.McKibbin, R.Subject: search.filters.subject.Pollution measurement

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    Source release-rate estimation of atmospheric pollution from a non-steady point source - Part 2: Source at an unknown location
    (Massey University, 2003) Kathirgamanathan, P.; McKibbin, R.; McLachlan, R.I.
    This paper presents an inverse modelling procedure to estimate the location and release rate of atmospheric pollution. The input to this model requires measured pollution concentration at a minimum of three observation sites on the ground and meteorological conditions such as wind speed and cloud cover. The inverse model is formulated as a least squares minimisation problem coupled with the solution of an advection-diffusion equation for a non-steady point source model. Since the minimisation problem has a combination of linear and non-linear parameters the problem is solved in two steps. Non-linear parameters are found by constructing an iterative procedure using an optimisation routine such as MATLAB’s lsqnonlin and at each iteration, the linear subproblem is solved to estimate the linear parameters. Finding the linear parameters is an ill-posed problem and consequently its solution is extremely sensitive to errors in the data. Tikhonov regularisation, which stabilises the process of the solution, is used to overcome the ill posedness of the problem and the regularisation parameter is estimated using the properties of the non-linear L-curve, linear L-curve and generalised cross validation. Finally, the accuracy of the model is examined by imposing normally-distributed relative noise into concentration data generated by the forward model.
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    Source release-rate estimation of atmospheric pollution from a non-steady point source - Part I: Source at a known location
    (Massey University, 2003) Kathirgamanathan, P.; McKibbin, R.; McLachlan, R.I.
    The goal is to build up an inverse model capable of finding the release history of atmospheric pollution by using measured gas concentration data at just one location on the ground and identify the factors which affects the accuracy of the model predictions. The problem involves a non-steady point source of pollution at a known location in the atmosphere. This problem of finding the release rate is an ill-posed inverse problem and its solution is extremely sensitive to errors in the measurement data. Special regularisation methods, which stabilise the process of the solution, must be used to solve the problem. The method described in this paper is based on linear least-squares regression and Tikhonov regularisation, coupled with the solution of an advection-diffusion equation for a non-steady point source. The accuracy of the method is examined by imposing normally-distributed relative noise into the concentration data generated by the forward model as well as some real experimental data.
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    Source term estimation of pollution from an instantaneous point source
    (Massey University, 2002) Kathirgamanathan, P.; McKibbin, R.; McLachlan, R.I.
    The goal is to develop an inverse model capable of simultaneously estimating the parameters appearing in an air pollution model for an instantaneous point source, by using measured gas concentration data. The approach taken was to develop the inverse model as a non-linear least squares estimation problem in which the source term is estimated using measurements of pollution concentration on the ground. The statistical basis of the least squares inverse model allows quantification of the uncertainty of the parameter estimates, which in turn allows estimation of the uncertainty of the simulation model predictions.