Source characterization of airborne pollutant emissions by hybrid metaheuristic/gradient-based optimization techniques
We propose a methodology to estimate single and multiple emission sources of atmospheric contaminants. It combines hybrid metaheuristic/gradient-descent optimization techniques and Tikhonov-type regularization. The dispersion problem is solved by the Galerkin/Least-squares finite element formulation...
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Veröffentlicht in: | Environmental pollution (1987) 2020-12, Vol.267, p.115618, Article 115618 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | We propose a methodology to estimate single and multiple emission sources of atmospheric contaminants. It combines hybrid metaheuristic/gradient-descent optimization techniques and Tikhonov-type regularization. The dispersion problem is solved by the Galerkin/Least-squares finite element formulation, which allows more realistic modeling. The accuracy of the proposed inversion model is tested under different contexts with experimental data. To identify single and multiple emissions, we use experimental field data. We consider different configurations for both the Tikhonov-type functional and optimization techniques. Several single and composite data misfit functions are tested. We also use a discrepancy-based choice rule for the regularization parameter. The resulting inversion tool is highly versatile and presents accurate results under different contexts with a competitive computational cost.
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•The source identification is solved by Tikhonov-type regularization.•We use single and composite data misfit functions.•Minimization is performed by metaheuristic/gradient-descent techniques.•The methodology is tested with single and multiple emissions from experimental data.•The Algorithm provides accurate estimations of source parameters in both cases.
A new Algorithm combining accurate dispersion models, Tikhonov regularization and hybrid optimization tools estimates pollutant emission sources. |
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ISSN: | 0269-7491 1873-6424 |
DOI: | 10.1016/j.envpol.2020.115618 |