A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.

ABSTRACT: 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

SUBMITTER: Baerenbold O 

PROVIDER: S-EPMC10077992 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature