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.

中文翻译：

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使用现场观测对大型城市火灾排放进行贝叶斯反演

城市地区的大规模火灾凸显了开发评估烟雾对人类和环境风险的方法的必要性。挑战之一是迅速向当局提供有关受羽流影响的区域以及人们可能接触到的污染物浓度水平的信息。在这项工作中，我们开发了一种逆向建模方法，通过同化现场污染物浓度测量值来查找大规模火灾的烟源项。考虑使用基于马尔可夫链蒙特卡罗（MCMC）技术的贝叶斯方法来确定源特征及其不确定性。源通过随时间变化的发射速率和发射高度来描述。后者与羽流上升现象有关，是评估火灾附近污染影响的重要参数。引入了强制系统选择单一发射高度的反演方案。这些逆向方法适用于 2021 年巴黎附近奥贝维利埃重大仓库火灾的真实案例研究。在大多数应用案例中，某些信息（例如火灾前已存在的污染）可能难以估计，特别是在可操作的情况下。然后讨论了逆向方法的微调，以使其更稳健地转用于未来的案例研究。