Modeling and Regionalization of China's PM2.5 Using Spatial-Functional Mixture Models

Severe air pollution affects billions of people around the world, particularly in developing countries such as China. Effective emission control policies rely primarily on a proper assessment of air pollutants and accurate spatial clustering outcomes. Unfortunately, emission patterns are difficult to observe as they are highly confounded by many meteorological and geographical factors. In this study, we propose a novel approach for modeling and clustering PM2.5 concentrations across China. We model observed concentrations from monitoring stations as spatially dependent functional data and assume latent emission processes originate from a functional mixture model with each component as a spatio-temporal process. Cluster memberships of monitoring stations are modeled as a Markov random field, in which confounding effects are controlled through energy functions. The superior performance of our approach is demonstrated using extensive simulation studies. Our method is effective in dividing China and the Beijing-Tianjin-Hebei region into several regions based on PM2.5 concentrations, suggesting that separate local emission control policies are needed. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.