Bayesian inversion of emissions from large urban fire using in situ observations

Abstract

Large-scale fires in urban areas have highlighted the need to develop ways of assessing the risks posed by smoke plumes to people and the environment. One of the challenges is to quickly provide the authorities with information on the areas affected by the plume and the levels of pollutant concentration to which people may have been exposed. In this work, we develop an inverse modelling method to find the smoke source term of a large-scale fire by assimilating in-situ pollutant concentration measurements. A Bayesian method based on a Markov Chain Monte Carlo (MCMC) technique is considered to determine the source characteristics and their uncertainties. The source is described by a time-varying emission rate and an emission height. The latter, linked to the phenomenon of plume rise, is an important parameter for assessing the pollution impact in the vicinity of the fire. An inversion proposal that forces the system to choose a single emission height is introduced. These inverse methodologies are applied to the real case study of a major warehouse fire in Aubervilliers, near Paris, in 2021. In most cases of application, certain information, such as the pollution already present before the fire, may be difficult to estimate, particularly in an operational situation. Fine-tuning of the inverse method to make it more robust for transposition to future case studies are then discussed.