Grantee Research Project Results
Addressing Temporal Correlation, Incomplete Source Profile Information, and Varying Source Profiles in the Source Apportionment of Particulate Matter
EPA Grant Number: R832160Title: 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: Particulate Matter , Air Quality and Air Toxics , Air
Objective:
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.
Approach:
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 projectJournal Articles:
Journal Articles have been submitted on this project: View all 8 journal articles for this projectSupplemental Keywords:
receptor model, chemical mass balance model, Bayesian analysis, statistics, modeling, decision making, air quality models, RFA, Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, Air, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Atmospheric Sciences, Environmental Engineering, Environmental Monitoring, atmospheric dispersion models, source receptor based methods, air sampling, chemical characteristics, airborne particulate matter, emissions monitoring, atmospheric chemistry, real-time monitoring, speciation, modeling studies, environmental measurement, area of influence analysis, iterated confirmatory factor analysis, particulate matter mass, air quality model, particulate organic carbon, aerosol analyzers, source apportionment, chemical speciation sampling, particle size measurement, air quality models, analytical chemistry, atmospheric measurements, Bayesian hierarchical modelProgress and Final Reports:
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.