Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles

Abstract

Identifying realistic pollution source profiles and quantifying the contributions of atmospheric particulate matter are crucial for the development of pollution mitigation strategies to protect public health. In this paper, we proposed a multivariate source apportionment model by using a Bayesian framework for latent source profiles to incorporate expert knowledge regarding emissions that can facilitate source profile estimation, and atmospheric effects, such as meteorological conditions, can improve source concentration estimations. This approach can maintain positivity and summation constraints for source contributions and profiles.Furthermore, available expert knowledge regarding source profiles is incorporated as prior knowledge to avoid restrictive assumptions regarding the presence or absence of chemical constituent tracers in source profile modeling. We used long-term PM2.5 measurements collected from two locations with different environmental characteristics in northern Taiwan to demonstrate the feasibility of the proposed model and evaluated its performance by using simulated data.