Addressing Temporal Correlation, Incomplete Source Profile Information, and Varying Source Profiles in the Source Apportionment of Particulate Matter

EPA Grant Number: R832160
Title: Addressing Temporal Correlation, Incomplete Source Profile Information, and Varying Source Profiles in the Source Apportionment of Particulate Matter
Investigators: Christensen, William F. , Reese, C. Shane
Institution: Brigham Young University
EPA Project Officer: Chung, Serena
Project Period: December 1, 2004 through November 30, 2007
Project Amount: $238,721
RFA: Source Apportionment of Particulate Matter (2004) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air


Most pollution source apportionment studies utilize ambient measurements that are gathered consecutively. Notwithstanding, most source apportionment (SA) approaches neither account for the impact of this correlation on statistical estimation and inference nor exploit the additional information available in correlated data. Additional complications in SA studies occur when only partial source profile information is available and when the source profiles evolve or vary over the measurement period. The proposed research has three objectives in addressing these issues.

1. Address both the challenges and advantages presented by temporally correlated ambient data, and address the opportunity for improved source contribution estimates when the temporal resolution of ambient measures is improved.

2. Develop the iterated confirmatory factor analysis (ICFA) approach, which can utilize partial source profile information and take on aspects of CMB analysis, confirmatory factor analysis (CFA), and exploratory factor analysis (EFA) by assigning varying degrees of constraint to each element of the estimated source profile matrix during the estimation process.

3. Develop a Bayesian hierarchical model for source apportionment, and present an approach for evaluating not only the change in source contributions over time, but also the change in source profiles.


For the first objective, we will use data available from the St. Louis Supersite to address the impact of temporal correlation and temporal sampling resolution on source contribution estimation and associated inference. After developing and refining the ICFA approach mentioned in the second objective, we will use computer simulations and real data from the St. Louis Supersite to compare ICFA with CMB, UNMIX, and PMF under a wide variety of scenarios. The final objective will be addressed by formulating a hierarchical model for the SA of PM data. Uncertainty values associated with ambient measurements and source profiles will be used to propose prior distributions for unknown model parameters.

Expected Results:

The expected results from this research include: additional understanding about the effects of temporal correlation and temporal sampling resolution in SA studies; the development of ICFA, which can utilize the strengths of CMB and factor analysis approaches; practical guidance for researchers when selecting a SA approach among the variety of CMB and multivariate receptor modeling methods; and a comprehensive SA approach based on Bayesian hierarchical modeling of air quality observations when pollution source profiles vary over the study period.

Publications and Presentations:

Publications have been submitted on this project: View all 36 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 8 journal articles for this project

Supplemental Keywords:

receptor model, chemical mass balance model, Bayesian analysis, statistics, modeling, decision making, air quality models, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Atmospheric Sciences, Environmental Engineering, atmospheric dispersion models, particulate organic carbon, atmospheric measurements, model-based analysis, area of influence analysis, source receptor based methods, chemical characteristics, environmental measurement, source apportionment, emissions monitoring, air quality models, airborne particulate matter, air sampling, air quality model, analytical chemistry, speciation, particulate matter mass, Bayesian hierarchical model, iterated confirmatory factor analysis, modeling studies, aerosol analyzers, atmospheric chemistry, chemical speciation sampling, real-time monitoring

Progress and Final Reports:

  • 2005 Progress Report
  • 2006 Progress Report
  • Final Report