Simultaneous model calibration and source inversion in atmospheric dispersion models

TL;DR

This paper introduces a Bayesian approach using Gaussian process emulations for simultaneous calibration of atmospheric dispersion models and source inversion, improving accuracy and uncertainty quantification in environmental monitoring.

Contribution

It presents a novel, cost-effective method combining Gaussian process emulation with Bayesian inference for joint model calibration and source inversion in atmospheric dispersion modeling.

Findings

Validated in an industrial case study with zinc emissions

Achieved accurate source estimates with quantified uncertainties

Demonstrated efficiency of the proposed approach

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.