The use of Positive Matrix Factorization (PMF) in pollution source apportionment (PSA) is examined and illustrated. A study of its settings is conducted in order to optimize them in the context of PSA. The use of a priori information in PMF is examined, in the form of target factor profiles and pulling profile elements to zero. A Bayesian model using lognormal prior distributions for source profiles and source contributions is fit and examined.

We describe a method to estimate pollution profiles and contribution levels for distinct prominent pollution sources in a region based on daily pollutant concentration measurements from multiple measurement stations over a period of time. In an extension of existing work, we will estimate common source profiles but distinct contribution levels based on measurements from each station. In addition, we will explore the possibility of extending existing work to allow adjustments for synoptic regimes—large scale weather patterns which may effect the amount of pollution measured from individual sources as well as for particular pollutants. For both extensions we propose Bayesian methods to estimate pollution source profiles and contributions.

Understanding the effect of human-induced pollution on the environment is an important precursor to promoting public health and environmental stability. One aspect of understanding pollution is understanding pollution sources. Various methods have been used and developed to understand pollution sources and the amount of pollution those sources emit. Multivariate receptor modeling seeks to estimate pollution source profiles and pollution emissions from concentrations of pollutants such as particulate matter (PM) in the air. Previous approaches to multivariate receptor modeling make the following two key assumptions: (1) PM measurements are independent and (2) source profiles are constant through time. Notwithstanding these assumptions, the existence of temporal correlation among PM measurements and time-varying source profiles is commonly accepted. In this thesis an approach to multivariate receptor modeling is developed in which the temporal structure of PM measurements is accounted for by modeling source profiles as a time-dependent Dirichlet process. The Dirichlet process (DP) pollution model developed herein is evaluated using several simulated data sets. In the presence of time-varying source profiles, the DP model more accurately estimates source profiles and source contributions than other multivariate receptor model approaches. Additionally, when source profiles are constant through time, the DP model outperforms other pollution receptor models by more accurately estimating source profiles and source contributions.