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 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.

Environmental data science: Part 1

“…Often, the decisions made in environmental applications are derived from statistical models that are fitted to observational data. For example, in the case of Baerenbold et al (2023) (a contribution from the working group “Functional analysis for correlated time‐series” of TIES) a Dirichlet process model is used to predict what the sources and the respective contributions are of observed particulate matter; a decision on which are the largest contributors could be made in a relatively straightforward manner using the model predictions. Cripps and Durrant‐Whyte (2023) discuss four sources of uncertainty that ultimately affect decisions (inherent, parametric, model, and knowledge), and give some examples of how these manifest themselves in environmental applications.…”