Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model

Man-Suk Oh; Chee Kyung Park

doi:10.1080/02664763.2020.1822305

Regional source apportionment of PM2.5 in Seoul using Bayesian multivariate receptor model

J Appl Stat. 2020 Sep 22;49(3):738-751. doi: 10.1080/02664763.2020.1822305. eCollection 2022.

Authors

Man-Suk Oh¹, Chee Kyung Park¹

Affiliation

¹ Department of Statistics, Ewha Womans University, Seoul, Korea.

Abstract

Seoul, the capital city of Korea with over 10 million residents, has been experiencing serious air pollution problems. Previous studies on source apportionment of PM2.5 in Seoul are based on measurements of chemical compositions of PM2.5 from a single monitoring site. In this paper, we analyse PM2.5 concentration data collected from multiple sites in 24 districts of Seoul and estimate regional source profiles using Bayesian multivariate receptor model. The regional source profiles provide information for the identification of major PM2.5 sources as well as the regions relatively more seriously affected by each source than other regions. These regional characteristics relevant to PM2.5 can help establish effective, customised, region-specific PM2.5 control strategies for each region rather than general strategies that apply to every region of Seoul.

Keywords: Bayesian analysis; Markov chain Monte Carlo; air pollution; factor analysis; particulate matter.

Grants and funding

This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2016R1A2B4008914).