使用高斯烟羽模式的空气无组织排放的贝叶斯估计
Bayesian estimation of airborne fugitive emissions using a Gaussian plume model
摘要：
提出了一种新的方法，通过测量沉积和浓度来估算多个时间依赖源的颗粒物无组织排放速率。我们投这个源反演问题的贝叶斯框架内，并使用基于高斯羽流解决方案的正演模型。我们提出了三种替代模型，用于构建作为时间函数的排放率的先验分布。接下来，我们提出了一个工业案例研究中，我们的框架被应用到估计率的铅颗粒从特雷尔，加拿大不列颠哥伦比亚省，冶炼厂的逃逸排放。贝叶斯框架不仅提供了一个近似的解决方案，反问题，但也量化的解决方案中的不确定性。利用这些信息，我们进行了不确定性传播研究，以评估工业现场周围地区的估计源的影响。
英文摘要：
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.
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