Bayesian estimation of airborne fugitive emissions using a Gaussian plume model

TL;DR

This paper introduces a Bayesian approach using a Gaussian plume model to estimate and quantify uncertainties of airborne fugitive emissions from multiple sources, demonstrated through a case study of a lead smelter.

Contribution

It presents a novel Bayesian inversion framework with three prior models for estimating time-dependent emission rates from measurements.

Findings

Successfully estimated fugitive emissions of lead particulates.

Quantified uncertainty in emission estimates.

Assessed environmental impact through uncertainty propagation.

Abstract

A new method is proposed for estimating the rate of fugitive emissions of particulate matter from multiple time-dependent sources via measurements of deposition and concentration. We cast this source inversion problem within the Bayesian framework, and use a forward model based on a Gaussian plume solution. We present three alternate models for constructing the prior distribution on the emission rates as functions of time. Next, we present an industrial case study in which our framework is applied to estimate the rate of fugitive emissions of lead particulates from a smelter in Trail, British Columbia, Canada. The Bayesian framework not only provides an approximate solution to the inverse problem, but also quantifies the uncertainty in the solution. Using this information we perform an uncertainty propagation study in order to assess the impact of the estimated sources on the area surrounding the industrial site.