Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms |
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| Authors: | Guido Cervone Pasquale Franzese Adrian Grajdeanu |
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| Institution: | 1. Dept. of Geography and Geoinformation Science, George Mason University, United States;2. Center for Earth Observing and Space Research, George Mason University, United States;3. Dept. of Computer Science, George Mason University, United States;1. National Isotope Centre, GNS Science, 30 Gracefield Rd, Lower Hutt, New Zealand;2. School of Environment, University of Auckland, New Zealand;1. Fuli School of Food Equipment Engineering and Science, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049, P.R. China;2. State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049, PR China;3. School of Chemical Engineering and Technology, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049, PR China;1. Laboratory of Mechanics and Energy, Universite d''Evry-Val d''Essonne, 40 Rue Du Pelvoux, 91080 Courcouronnes, Evry Cedex, France;2. Centre for Atmospheric Sciences, Indian Institute of Technology Delhi 110016, India |
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| Abstract: | The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source.The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks. |
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