Inverse Modelling for Identification of Multiple-Point Releases from Atmospheric Concentration Measurements

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

“…where N is the total number of grid points and m is the number of sources. Even with a 3-D mesh size of N ¼ 1000 Â 1000 Â 50 and with only two sources, m ¼ 2, the total number of combinations is 1.25 Â 10 15 , as already pointed out by Singh et al (2013). In that paper the authors propose an inverse modeling methodology for the identification of multiple-point sources based on the geometry of the monitoring network in terms of weight functions employing a bracketing strategy to reduce the computational time due to the very large number of the grid points to be visited (Singh et al, 2013).…”

“…Even with a 3-D mesh size of N ¼ 1000 Â 1000 Â 50 and with only two sources, m ¼ 2, the total number of combinations is 1.25 Â 10 15 , as already pointed out by Singh et al (2013). In that paper the authors propose an inverse modeling methodology for the identification of multiple-point sources based on the geometry of the monitoring network in terms of weight functions employing a bracketing strategy to reduce the computational time due to the very large number of the grid points to be visited (Singh et al, 2013). In the present work, in order to limit the computational efforts we have developed an inverse model based on the GAs features that employs the J function reported in equation (2) as the cost function and that avoids its evaluation for all the possible combinations C N,m .…”

“…On the other hand, the simultaneous identification of several sources is a more challenging task as the measured concentrations are not uniquely due to a single source of pollutant. As reported by Singh et al (2013), the identification of multiple-point releases has been addressed on techniques based on deterministic (Penenko et al (2002), Krysta et al (2006), Sharan et al (2009Sharan et al ( , 2012) and probabilistic (Keats et al (2007), Yee (2008)) methods. In particular, Yee (2008) has used a probabilistic approach for identifying multiple-point emissions employing a reversible jump Markov process technique that efficiently samples the number of point sources, their origin and corresponding sources intensity.…”

Reconstructing source terms from atmospheric concentration measurements: Optimality analysis of an inversion technique

“…Steady retroplumes, drawn with four colors, for configuration 2 of DYCE experiment (focus around the source position). The Gaussian dispersion model proposed by Sharan et al [1996] in the forward mode is utilized here for the computation of these sensitivity functions by rotating the wind direction by 180 and assuming an unit release at the FFIDs' locations [Singh et al, 2013].…”

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

“…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.…”