A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.
A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.
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DOI：
10.1002/env.2763
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发表时间：
2023-02
期刊：
影响因子：
1.7
通讯作者：
Blangiardo, Marta
中科院分区：
文献类型：
--
作者：
Baerenbold, Oliver;Meis, Melanie;Martinez-Hernandez, Israel;Euan, Carolina;Burr, Wesley S.;Tremper, Anja;Fuller, Gary;Pirani, Monica;Blangiardo, Marta
近年来，颗粒物暴露与健康风险之间的关系已经得到很好的确立。颗粒物（PM）是由不同来源的不同成分组成的，它们可能具有不同的毒性。因此，确定这些来源是一项重要任务，以便实施有效的政策，改善空气质量和人口健康。识别微粒污染源的问题已经在文献中进行了研究。然而，目前的方法需要一个先验规范的源的数量，并不包括在源分配的协变量的信息。在这里，我们提出了一种新的贝叶斯非参数方法来克服这些限制。特别是，我们使用Dirichlet过程作为源配置文件的先验模型源贡献，这使我们能够估计有助于颗粒浓度的组件的数量，而不是预先固定这个数字。为了更好地描述它们，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。我们将该模型应用于2019年在伦敦盖特威克机场（英国）附近测量的颗粒数量尺寸分布。在分析这些数据时，我们能够识别最常见的PM来源，以及尚未使用常用方法识别的新来源。
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.
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作者：
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DOI：
10.1080/10618600.2016.1172487
发表时间：
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