Predicting latent source-specific PM2.5 pollution from regional sources at unmonitored sites by Bayesian spatial multivariate receptor modeling. (1st May 2023)

• Record Type: Journal Article
• Authors: Lee, Young Su; Kim, Jae Young; Yi, Seung-Muk; Kim, Ho; Park, Eun Sug
• Is Part Of: Environmental pollution. Volume 324(2023)
• Publication Date: 2023-05-01
• Subjects: Bayesian source apportionment -- Fine particulate matter -- Multivariate receptor modeling -- Source-specific exposures -- Spatial prediction; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2023.121389
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Fine particulate matter (PM2.5 ) has been a pollutant of main interest globally for more than two decades, owing to its well-known adverse health effects. For developing effective management strategies for PM2.5, it is vital to identify its major sources and quantify how much they contribute to ambient PM2.5 concentrations. With the expanded monitoring efforts established during recent decades in Korea, speciated PM2.5 data needed for source apportionment of PM2.5 are now available for multiple sites (cities). However, many cities in Korea still do not have any speciated PM2.5 monitoring station, although quantification of source contributions for those cities is in great need. While there have been many PM2.5 source apportionment studies throughout the world for several decades based on monitoring data collected from receptor site(s), none of those receptor-oriented studies could predict unobserved source contributions at unmonitored sites. This study predicts source contributions of PM2.5 at unmonitored locations using a recently developed novel spatial multivariate receptor modeling (BSMRM) approach, which incorporates spatial correlation in data into modeling and estimation for spatial prediction of latent source contributions. The validity of BSMRM results is also assessed based on the data from a test site (city), not used in model development and estimation. Graphical abstract: Image 1 Highlights: Latent source contributions of PM2.5 along with uncertainty were estimated by BSMRM. BSMRM performed spatial prediction of source contributions at unmonitored sites. BSMRM results were verified using held-out test data. Maps of source-specific pollution surfaces over South Korea were constructed.