Simultaneous Model Calibration and Source Inversion in Atmospheric Dispersion Models
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We present a cost-effective method for model calibration and solution of source inversion problems in atmospheric dispersion modelling. We use Gaussian process emulations of atmospheric dispersion models within a Bayesian framework for solution of inverse problems. The model and source parameters are treated as unknowns and we obtain point estimates and approximation of uncertainties for sources while simultaneously calibrating the forward model. The method is validated in the context of an industrial case study involving emissions from a smelting operation for which cumulative monthly measurements of zinc particulate depositions are available.
KeywordsAtmospheric dispersion particle deposition inverse source identification Bayesian estimation uncertainty quantification
Mathematics Subject Classification62F15 65M08 65M32 86A10
This work was partially supported by the Natural Sciences and Engineering Research Council of Canada through a Postdoctoral Fellowship (BH) and a Discovery Grant (JMS). We are grateful to the Environmental Management Group at Teck Resources Ltd. (Trail, BC) for providing data and for many useful discussions.
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