Fine suspended particulates (FSP), commonly known as PM2.5, are among the most harmful air pollutants, posing serious risks to population health and environmental integrity. As such, accurately identifying latent clusters of FSP is essential for effective air quality and public health management. This task, however, is notably nontrivial as FSP clusters may depend on various regional and temporal factors, which should be incorporated in the modeling process. Thus, we capitalize on Bayesian nonparametric dynamic clustering ideas, in which clustering structures may be influenced by complex dependencies. Existing implementations of dynamic clustering, however, rely on copula-based dependent Dirichlet processes (DPs), presenting considerable computational challenges for real-world deployment. With this in mind, we propose a more efficient alternative for dynamic clustering by incorporating the novel ideas of logistic-beta dependent DPs. We also adopt a Stirling-gamma prior, a novel distribution family, on the concentration parameter of our underlying DP, easing the process of incorporating prior knowledge into the model. Efficient computational strategies for posterior inference are also presented. We apply our proposed method to identify dynamic FSP clusters across Chile and demonstrate its superior performance over existing approaches.

翻译：细颗粒物（FSP），通常称为PM2.5，是最有害的空气污染物之一，对人口健康和环境完整性构成严重威胁。因此，准确识别FSP的潜在聚类对于有效的空气质量和公共卫生管理至关重要。然而，这项任务尤为复杂，因为FSP聚类可能依赖于各种区域和时间因素，这些因素应在建模过程中予以考虑。为此，我们利用贝叶斯非参数动态聚类思想，其中聚类结构可能受到复杂依赖关系的影响。然而，现有的动态聚类实现依赖于基于copula的依赖狄利克雷过程（DPs），在实际部署中带来了巨大的计算挑战。鉴于此，我们通过引入新颖的逻辑-贝塔依赖DPs思想，提出了一种更高效的动态聚类替代方案。我们还在基础DP的浓度参数上采用了斯特林-伽马先验——一种新颖的分布族，从而简化了将先验知识融入模型的过程。本文还提出了有效的后验推断计算策略。我们将所提方法应用于智利全境的动态FSP聚类识别，并证明了其相对于现有方法的优越性能。