Abstract
A computational model is developed for retrieving the positions and the emission rates of unknown pollution sources, under steady state conditions, starting from the measurements of the concentration of the pollutants. The approach is based on the minimization of a fitness function employing a genetic algorithm paradigm. The model is tested considering both pollutant concentrations generated through a Gaussian model in 25 points in a 3-D test case domain (1000m × 1000m × 50 m) and experimental data such as the Prairie Grass field experiments data in which about 600 receptors were located along five concentric semicircle arcs and the Fusion Field Trials 2007. The results show that the computational model is capable to efficiently retrieve up to three different unknown sources.
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