Simultaneous Model Calibration and Source Inversion in Atmospheric Dispersion Models

  • Juan G. García
  • Bamdad HosseiniEmail author
  • John M. Stockie


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.


Atmospheric dispersion particle deposition inverse source identification Bayesian estimation uncertainty quantification 

Mathematics Subject Classification

62F15 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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Juan G. García
    • 1
  • Bamdad Hosseini
    • 3
    Email author
  • John M. Stockie
    • 2
  1. 1.3DM Devices Inc.AldergroveCanada
  2. 2.Department of MathematicsSimon Fraser UniversityBurnabyCanada
  3. 3.Computing and Mathematical SciencesCalifornia Institute of TechnologyPasadenaUSA

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