Pollution source identification using a coupled diffusion model with a genetic algorithm |
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Authors: | Anis Khlaifi Anda Ionescu Yves Candau |
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Affiliation: | aCERTES, Paris XII University, Creteil, France |
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Abstract: | A new approach for the source quantification has been developed on the basis of real air pollutant hourly concentrations of SO2, measured by three monitoring stations, during 9 h, around a group of three industrial sources. This inverse problem has been solved by coupling a direct model of diffusion (Pasquill’s Gaussian model) with a genetic algorithm, to search solutions leading to a minimum error between model outputs and measurements. The inversion performance depends on the relationship between the wind field and the configuration sources–receptors: good results are obtained when the monitoring stations are downwind from the sources, and in these cases, the order of magnitude of emissions is retrieved, sometimes with less than 10% error for at least two sources; there are some configurations (wind direction versus source and receptor locations) which do not permit to restore emissions. The latter situations reveal the need to conceive a specific network of sensors, taking into account the source locations and the most frequent weather patterns. |
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Keywords: | Inverse modeling Source identification Gaussian model Genetic algorithm SO2 |
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