Incorporating Time-Dependent Source Profiles Using the Dirichlet Distribution in Multivariate Receptor Models

Abstract

Multivariate receptor modeling is used to estimate profiles and contributions of pollution sources from concentrations of pollutants such as particulate matter in the air. The majority of previous approaches to multivariate receptor modeling assume pollution source profiles are constant through time. In an effort to relax this assumption, this article uses the Dirichlet distribution in a dynamic linear receptor model for pollution source profiles. The receptor model developed herein is evaluated using simulated datasets and then applied to a physical dataset of chemical species concentrations measured at the U.S. Environmental Protection Agency's St. Louis Midwest supersite. Supplemental materials to this articles are available online.