A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution.
Oliver Baerenbold, Melanie Meis, Israel Mart��nez-Hern��ndez, Carolina Eu��n, Wesley S Burr, Anja Tremper, Gary Fuller, Monica Pirani, Marta Blangiardo
Author Information
Oliver Baerenbold: Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health Imperial College London UK. ORCID
Melanie Meis: Department of Atmospheric and Oceanic Sciences Consejo Nacional de Investigaciones Cientinficas y Tecnologicas (CONICET), Centro del Mar y la Atm��sfera y los Oc��anos (CIMA-UBA-CONICET), Universidad de Buenos Aires Buenos Aires Argentina. ORCID
Israel Mart��nez-Hern��ndez: Department of Mathematics and Statistics Lancaster University Lancaster UK.
Carolina Eu��n: Department of Mathematics and Statistics Lancaster University Lancaster UK. ORCID
Wesley S Burr: Department of Mathematics Trent University Peterborough Ontario Canada. ORCID
Anja Tremper: Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health Imperial College London UK. ORCID
Gary Fuller: Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health Imperial College London UK.
Monica Pirani: Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health Imperial College London UK.
Marta Blangiardo: Department of Epidemiology and Biostatistics, MRC Centre for Environment and Health Imperial College London UK.
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.
