A least-squares inversion technique for identification of a point release: Application to Fusion Field Trials 2007

Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

“…Significant efforts in this direction have been done by Pudykiewicz (1998), Penenko et al (2002), Issartel et al (2007), Allen et al (2007b), Senocak et al (2008), Sharan et al (2009), Cervone et al (2010), Cervone and Franzese (2011), Singh and Rani (2014) and Cantelli et al (2012). All the above studies consider the identification of just one single-point source.…”

Source area identification with observation from limited monitor sites for air pollution episodes in industrial parks

“…Even more monitors were recommended by other researchers. For example, Rudd et al (2012) and Singh and Rani (2014) suggested that the number of monitors should be at least higher than the number of unknowns because of uncertainties in the measurements, the model and the variable quality in sensor placement; Allen et al (2007b) and Haupt et al (2007) demonstrated that at least an 8-by-8 grid of receptors was necessary to find the combination of source location, source strength, and wind direction by a GA system. However, such demand for monitors can seldom be satisfied because the scales of the monitoring networks in industrial parks are generally smaller than required.…”

“…Inverse methods for back-tracking the sources for such episodes were applied to do the source area identification. There are many inverse methods for seeking an optimal approximation of the source parameters such as genetic algorithm (Haupt, 2005;Haupt et al, 2007;Allen et al, 2007b), simulated annealing algorithm (Thomson et al, 2007), NewtoneRaphson method (Najafi and Gilbert, 2003), least square method (Singh et al, 2013;Singh and Rani, 2014), pattern search (Zheng and Chen, 2010) and etc. It is applicable to obtain a collection of optimal solutions by doing the back-calculation of source parameters by such method repeatedly.…”

Comparative interpretations of renormalization inversion technique for reconstructing unknown emissions from measured atmospheric concentrations

“…Several inversion techniques have been implemented and explored to address the source estimation problem. The well‐known methods are Backus and Gilbert [ Backus and Gilbert , ], maximum entropy [ Bocquet , ], Bayesian estimation [ Keats et al , ; Yee et al , ; Ganesan et al , ], renormalization [ Issartel , ], least squares [ Krysta et al , ; Sharan et al , ; Singh and Rani , ], genetic search algorithms [ Allen et al , ], regularized optimization [ Sikorski et al , ; Addepalli et al , ], etc. These techniques differ from each other in terms of their mathematical formulations, prior assumptions, constraints, parameters, etc., and provide a reconstructed source with some predefined global properties, like maximum a posteriori, maximum likelihood, maximum resolution, minimum variance, minimum norm, weighted average, smoothness, etc.…”