Grantee Research Project Results
2005 Progress Report: 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 Period Covered by this Report: December 1, 2004 through November 30, 2005
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
Objective:
Most pollution source apportionment (SA) studies utilize ambient measurements that are gathered consecutively. Notwithstanding, most 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 objectives of the research project are to: (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 chemical mass balance (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; and (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.
Progress Summary:
Progress this year has occurred in three main areas of development: iterated confirmatory factor analysis, Bayesian CMB modeling, and supplementary tools.
Iterated Confirmatory Factor Analysis
During Year 1 of the project, the ICFA approach has been developed and evaluated more thoroughly. In an Environmetrics article to appear in 2006, we discuss the ICFA approach. ICFA can take on aspects of CMB analysis, EFA, and CFA by assigning varying degrees of constraint to the elements of the source profile matrix when iteratively adapting the hypothesized profiles to conform to the data.
Bayesian CMB Modeling
In the second area of development, we have begun developing a Bayesian approach for CMB analysis. This new approach is a Bayesian alternative to the “effective variance (EV) solution” proposed by Watson, Cooper, and Huntzicker (1984) and implemented in the widely used U.S. Environmental Protection Agency (EPA) CMB8.2 software (EPA, 2004). Whereas the EV solution is tied to the given profiles regardless of their appropriateness for the airshed of interest, the Bayesian approach allows the data to move the profile values away from the potentially erroneous initial values in a coherent manner.
Supplementary Tools
The basic positive matrix factorization (PMF) approach of Paatero and Tapper (1994) has proven to be a seminal tool in source apportionment research. Implemented in PMF2 and more recently in EPA-PMF1.1, the PMF algorithm has become widely used. To facilitate better comparison of competing approaches, effort was spent in assessing and optimizing the performance of PMF using synthetic (but reasonably realistic) data. Associated with this project is the development of a PMF interface for the R programming language. The other supplementary tool under development relates to the differentiability of pollution source profiles. Specifically, we are working to extend current cluster analysis methods to measurement uncertainty. In a soon to be submitted manuscript, it is noted that when profile uncertainty vectors associated with each profile vector is available, it is clear that one would be benefited almost always by using a newly proposed modified Mahalanobis distance metric instead of the standard Euclidean distance.
Future Activities:
During Years 2 and 3 of the project, we will continue to focus on the Bayesian hierarchical model as our ultimate goal. Our primary goal is to begin to incorporate flexibility in the nature of the a priori pollution source profile information, including partially or completely unspecified profiles. Additional issues we plan to address in the next 2 years include the following:
- Temporally varying profiles. In the final stage of this research, we will develop coherent approaches for understanding the evolution of source profiles over time.
- Informative prior distributions on source contribution processes. For many pollution sources, knowledge about the general shape and temporal correlation structure of the contribution process is known and can be incorporated in prior distributions.
- Constraints on rows of the profile matrix. Whereas we usually think of identifying and estimating individual columns of the source profile matrix, often there is need to constrain rows of the profile matrix to conform to some known structure. We plan to consider how to utilize optimally such information in the model.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
Other project views: | All 36 publications | 10 publications in selected types | All 8 journal articles |
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Type | Citation | ||
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Christensen WF, Schauer JJ, Lingwall JW. Iterated confirmatory factor analysis for pollution source apportionment. Environmetrics 2006;17(6):663-681. |
R832160 (2005) R832160 (2006) R832160 (Final) |
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Supplemental Keywords:
receptor model, chemical mass balance model, Bayesian analysis, statistics, modeling, decision making, air quality models, air, ecosystem protection/environmental exposure and risk, air quality, atmospheric sciences, environmental chemistry, environmental engineering, environmental monitoring, monitoring/modeling, particulate matter, Bayesian hierarchical model, aerosol analyzers, air quality model, air sampling, airborne particulate matter, analytical chemistry, area of influence analysis, atmospheric chemistry, atmospheric dispersion models, atmospheric measurements, chemical characteristics, chemical speciation sampling, emissions monitoring, environmental measurement, iterated confirmatory factor analysis, model-based analysis, modeling studies, particle size measurement, particulate matter mass, particulate organic carbon, real-time monitoring, source apportionment, source receptor based methods,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Atmospheric Sciences, Environmental Engineering, particulate organic carbon, atmospheric dispersion models, atmospheric measurements, model-based analysis, area of influence analysis, source receptor based methods, source apportionment, chemical characteristics, emissions monitoring, environmental measurement, airborne particulate matter, air quality models, air quality model, air sampling, speciation, particulate matter mass, Bayesian hierarchical model, analytical chemistry, iterated confirmatory factor analysis, modeling studies, real-time monitoring, aerosol analyzers, chemical speciation sampling, particle size measurementProgress and Final Reports:
Original AbstractThe 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.