Bayesian inverse modelling and data assimilation of atmospheric emissions.

气体排放的指数增长是造成地球全球变暖的部分原因。今天，我们每年排放约500亿吨温室气体，其中大部分是由燃烧化石燃料、工业生产和土地利用变化产生的。在石油和天然气开采过程中，甲烷会被释放出来，这通常被称为“逃逸排放”。甲烷的寿命很短，这意味着减少甲烷的排放会迅速降低其在大气中的浓度。因此，解决甲烷排放问题可能是缓解气候变化影响的一种有效而快速的方法。随机性是本研究的首要问题，因为随机性的来源很多。我的研究重点是定位人为温室气体的来源和量化排放率；重点是甲烷。为此，我正在模拟大气中的气体分散，并实现源特征的概率反演。我正在利用基于Navier-Stokes方程的计算流体动力学中的高斯羽流和其他模型预测时空气体弥散，并评估它们在不同大气条件下的计算成本和准确性。此外，我正在开发涉及基于梯度的MCMC算法和高斯过程的新方法，以执行有效的概率反演，根据气体浓度测量确定源的位置。由于问题的高维性质，MCMC反演的计算成本很高。因此，进行这项研究的目的是创建计算速度快且适用的模型，包括无人机或卫星的实时跟踪排放。与壳牌公司合作。

The exponential increase of gas emissions is in part responsible for Earth's global warming. Today, we emit around 50 billion tonnes of greenhouse gases each year, with the majority produced by the burning of fossil fuels, industrial production, and land use change. Methane can be released during oil and gas extraction, this is often referred as "fugitive emissions". The short lifetime of methane implies that reductions in its emissions rapidly results in lowering its concentration in the atmosphere. Hence, tackling methane emissions could be an effective and rapid way to mitigate some of the impacts of climate change. Stochasticity is an overarching problem in this research as there are many sources of randomness.My research focuses on locating source(s) and quantifying emission rate(s) of anthropogenic greenhouse gases; with a focus on methane. To do so, I am modelling gas dispersion in the atmosphere and implementing probabilistic inversion for source characterisation. I am predicting spatio-temporal gas dispersion using Gaussian plume and other models from computational fluid dynamics based on Navier-Stokes equations and assessing their computational cost and accuracy under different atmospheric conditions. Additionally, I am developing novel methodologies involving gradient-based MCMC algorithms and Gaussian Processes to perform efficient probabilistic inversion, which identifies source(s) location based on gas concentration measurements. Due to the high-dimensional nature of the problem, MCMC inversion is computationally expensive. Hence, this research is undertaken with the aim to create models which are computationally fast and applicable, including for live tracking of emissions by drones or satellites. In partnership with Shell.