Published March 2021
| public
Journal Article
Simultaneous Model Calibration and Source Inversion in Atmospheric Dispersion Models
Abstract
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.
Additional Information
© Springer Nature Switzerland AG 2019. Received 14 June 2018; Revised 18 July 2019; Accepted 17 October 2019; Published 31 October 2019. 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.Additional details
- Eprint ID
- 99585
- DOI
- 10.1007/s00024-019-02348-4
- Resolver ID
- CaltechAUTHORS:20191031-124926478
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Created
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2019-10-31Created from EPrint's datestamp field
- Updated
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2023-10-02Created from EPrint's last_modified field