Improving pollutant source characterization by better estimating wind direction with a genetic algorithm |
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| Institution: | 1. Institut de radioprotection et de sûreté nucléaire, 31, avenue de la Division Leclerc, 92260, Fontenay-aux-Roses, France;2. Inria, Domaine de Voluceau, BP 105, 78153, Le Chesnay Cedex, France;3. CEREA, Joint Laboratory École des Ponts ParisTech/EDF R&D, Université Paris Est, Marne-la-Vallée, France;1. Shell Projects and Technology, Rijswijk, The Netherlands;2. Shell Projects and Technology, Thornton, UK;3. Shell Projects and Technology, Houston, USA;4. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, USA;1. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China;2. School of Chemical Engineering and Technology, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China;1. Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing, China;2. School of Engineering, RMIT University, Melbourne, Australia;1. State Key Laboratory of Multiphase Flow in Power Engineering, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China;2. School of Chemical Engineering and Technology, Xi''an Jiaotong University, No. 28 Xianning West Road, Xi''an 710049, PR China |
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| Abstract: | In homeland security applications, it is often necessary to characterize the source location and strength of a potentially harmful contaminant. Correct source characterization requires accurate meteorological data such as wind direction. Unfortunately, available meteorological data is often inaccurate or unrepresentative, having insufficient spatial and temporal resolution for precise modeling of pollutant dispersion. To address this issue, a method is presented that simultaneously determines the surface wind direction and the pollutant source characteristics. This method compares monitored receptor data to pollutant dispersion model output and uses a genetic algorithm (GA) to find the combination of source location, source strength, and surface wind direction that best matches the dispersion model output to the receptor data. A GA optimizes variables using principles from genetics and evolution.The approach is validated with an identical twin experiment using synthetic receptor data and a Gaussian plume equation as the dispersion model. Given sufficient receptor data, the GA is able to reproduce the wind direction, source location, and source strength. Additional runs incorporating white noise into the receptor data to simulate real-world variability demonstrate that the GA is still capable of computing the correct solution, as long as the magnitude of the noise does not exceed that of the receptor data. |
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