The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.

中文翻译：

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颗粒数尺寸分布源解析的依赖贝叶斯狄利克雷过程模型


近年来，颗粒物暴露与健康风险之间的关系已得到充分证实。颗粒物 (PM) 由多个来源的不同成分组成，可能具有不同程度的毒性。因此，确定这些来源是实施有效政策改善空气质量和人口健康的一项重要任务。文献中已经研究了确定颗粒物污染源的问题。然而，当前的方法需要先验指定源的数量，并且不包括源分配中协变量的信息。在这里，我们提出了一种新颖的贝叶斯非参数方法来克服这些限制。特别是，我们使用狄利克雷过程作为源剖面的先验来对源贡献进行建模，这使我们能够估计对颗粒浓度有贡献的成分数量，而不是预先固定该数字。为了更好地表征它们，我们还通过灵活的高斯内核将气象变量（风速和风向）作为分配过程中的协变量。我们应用该模型来分配 2019 年在伦敦盖特威克机场（英国）附近测量的颗粒物数量尺寸分布。在分析这些数据时，我们能够识别最常见的 PM 来源，以及尚未通过常见方法识别的新来源。使用的方法。