Final Report: Development of Advanced Factor Analysis Methods for Carbonaceous PM Source Identification and Apportionment

EPA Grant Number: R831078
Title: Development of Advanced Factor Analysis Methods for Carbonaceous PM Source Identification and Apportionment
Investigators: Hopke, Philip K. , Henry, Ronald C. , Paatero, P. , Spiegelman, C.
Institution: Clarkson University , Texas A & M University , University of Helsinki , University of Southern California
EPA Project Officer: Chung, Serena
Project Period: October 1, 2003 through September 30, 2006 (Extended to December 31, 2006)
Project Amount: $450,000
RFA: Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter (PM2.5) (2003) RFA Text |  Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Particulate Matter

Objective:

Because of controls on precursor gases that lead to sulfate and nitrate formation, carbonaceous particles are becoming a larger fraction of the fine particle aerosol. Accurate source identification and apportionment will be important for developing effective control strategies for areas found to be out of attainment of the PM2.5standard. In addition, there is increasing interest in epidemiological studies to relate adverse health effects to apportioned source contributions. Thus, the objective of this project is to combine the best features of the two advanced factor analysis models, UNMIX and Positive Matrix Factorization (PMF), and to test the effectiveness of this improved factor analysis methodology by analysis of the data developed in the various supersites with an emphasis on data from the New York City supersite and other data from New York State.

Summary/Accomplishments (Outputs/Outcomes):

Previously, we developed a new geometrical view of factor analysis in either the source profile or contribution space (Henry, 2005) and a graphical method to examine the results of the rotation in PMF (Paatero et al., 2005). To assist with the identification of local sources, the use of the conditional probability function (CPF) and non–parametric regression (NPR) have been compared by Kim and Hopke (2004).

Another aspect of the problem of rotational ambiguity is the use of expanded models to resolve factors. This approach uses two sets of modeling equations. We are exploring if the use of the additional equations begin to provide a measure of identifiability in the solution. We have now applied the expanded model to a number of Speciation Trends Network data sets from the Midwestern US. In general, the expanded model has not provided increased resolution of sources nor better specificity in point source identification. We have been generally disappointed in the limited utility that this model has shown and we continue to explore the reasons as to why we are not getting the results that were anticipated.

We are continuing to work on error estimation methods. Dr. Spiegelman together with B.Gajewski have developed a new jackknife approach that should accurately evaluate estimation uncertainties for all rotationally invariant pollution sources that are found by receptor models. The main exceptions are those pollution sources that require tracer species to be identified. A manuscript is under preparation. The method was given during an invited discussion at the Joint Statistical Meetings in Minneapolis in August 2005.

A new approach to understanding the fundamental basis of all receptor models was developed and published (Henry R. C. 2005. Duality in multivariate receptor models, Chemometrics and Intelligent Lab. Sys, 77: 59-63). This work led to a method of increasing the speed of EPA Unmix multivariate receptor model by 5 to 10 times. The same method can be used to speed up other multivariate receptor models.

Another problem basic to all multivariate receptor models is the accurate estimation of uncertainties in the results in the presence of serial correlation in the data. The first step in addressing this problem in a systematic way is to identify the source of the serial correlation. Fourier analysis and the classical or Schuster periodogram are the standard way to identify the periodicities in data. However, air quality data often cannot be analyzed by standard algorithms as these require evenly spaced data with no missing data. The use of the Lomb periodogram and related methods to address this problem is discussed in “A method Spectral Analysis of Environmental Time Series with Missing Data or Irregular Sample Spacing” by Shabnam Dilmaghani, Isaac C. Henry, and Ronald C. Henry (communicating author), in final preparation for submission to Environmental Science & Technology.

If possible, the best way to deal with missing data is to make an effort to replace the missing data with an estimate based on other accompanying species concentration data that is always available when doing multivariate receptor modeling. Methods to do this would be helpful to all multivariate receptor modeling efforts. A new, computationally efficient method was developed to replace missing data based on ratios of concentrations for the entire data set. The method is implemented in EPA Unmix and is described in: “A Self-Modeling Algorithm For Replacing Missing Data” by Shaohua Hu, Pratim Biswas and Ronald Henry, in final preparation for submission to Chemometrics and Intelligent Laboratory Systems.

We have expanded our studies to further examine the factor analysis of particle size distribution data, particularly in conjunction with semicontinuous speciated particulate and gaseous pollutant concentrations. We have examined particle size distribution data from Baltimore (Ogulei, Hopke, Zhou, Pancras, Nair, Ondov, 2006), from Rochester, NY (Ogulei, Hopke, Chalupa, and Utell, 2007) and Buffalo, NY (Ogulei, Hopke, Feno, and Jaques, 2007). In addition data from inside and outside a home in Reston, VA have been examined for source identification and apportionment (Ogulei, Hopke, and Wallace, 2006).

In addition to the factor analysis studies, we have also explored several trajectory ensemble methods for their ability to identify source locations. Zhao et al. (2007) explore two back trajectory analysis methods designed to be used with multiple site data, simplified quantitative transport bias analysis (SQTBA) and residence time weighted concentration (RTWC). These techniques were applied to nitrate and sulfate concentration data from two rural sites (the Mammoth Cave National Park and the Great Smoky Mountain National Park) and five urban sites (Chicago, Cleveland, Detroit, Indianapolis, St. Louis) for an intensive investigation on the spatial patterns of origins for these two species in the upper Midwestern area. The study was made by dividing the data into five categories: all sites and all seasons, rural sites in summer, rural sites in winter, urban sites in summer, and urban sites in winter. A general conclusion was that the origins of the nitrate in these seven sites were mainly in the upper Midwestern areas while the sulfate in these seven sites were mainly from the Ohio River Valley and Tennessee River Valley areas. The upper Midwestern areas are regions of high ammonia emissions rather than high NOx emissions. In the winter, metropolitan areas showed the highest nitrate emission potential suggesting the importance of local NOx emissions. In the summer, ammonia emissions from fertilizer application in the lower Midwestern area made a significant contribution to nitrate in the rural sites of this study. The impact of the wind direction prevalence on the source spatial patterns was observed by comparing the urban and rural patterns of the summer. The differences between the results of two methods are discussed and suggestions for applying these methods are also provided.


Journal Articles on this Report : 14 Displayed | Download in RIS Format

Other project views: All 30 publications 14 publications in selected types All 14 journal articles
Type Citation Project Document Sources
Journal Article Henry RC. Duality in multivariate receptor models. Chemometrics and Intelligent Laboratory Systems 2005;77(1-2):59-63. R831078 (2004)
R831078 (2005)
R831078 (Final)
  • Full-text: ScienceDirect - Full Text HTML
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  • Abstract: ScienceDirect-Abstract
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  • Other: ScienceDirect - Full Text PDF
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  • Journal Article Hopke PK. The use of source apportionment for air quality management and health assessments. Journal of Toxicology and Environmental Health, Part A: Current Issues 2008;71(9-10):555-563. R831078 (Final)
  • Abstract from PubMed
  • Abstract: Taylor&Francis-Abstract
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  • Journal Article Kasumba J, Hopke PK, Chalupa DC, Utell MJ. Comparison of sources of submicron particle number concentrations measured at two sites in Rochester, NY. Science of The Total Environment 2009;407(18):5071-5084. R831078 (Final)
  • Abstract from PubMed
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  • Abstract: ScienceDirect-Abstract
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  • Other: ScienceDirect - Full Text PDF
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  • Journal Article Kim E, Hopke PK. Comparison between conditional probability function and nonparametric regression for fine particle source directions. Atmospheric Environment 2004;38(28):4667-4673. R831078 (2005)
    R831078 (Final)
  • Full-text: ScienceDirect - Full Text HTML
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  • Abstract: ScienceDirect-Abstract
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  • Journal Article Kim E, Hopke PK, Qin Y. Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment. Journal of the Air & Waste Management Association 2005;55(8):1190-1199. R831078 (2005)
    R831078 (Final)
  • Abstract from PubMed
  • Full-text: Taylor & Francis-Full Text PDF
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  • Abstract: Taylor & Francis-Abstract
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  • Other: ResearchGate - Full Text PDF
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  • Journal Article Kim E, Hopke PK. Identification of fine particle sources in Mid-Atlantic US area. Water, Air, & Soil Pollution 2005;168(1-4):391-421. R831078 (2005)
    R831078 (Final)
  • Abstract: Springer-Abstract
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  • Other: ResearchGate - Abstract
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  • Journal Article Kim E, Hopke PK. Improving source apportionment of fine particles in the eastern United States utilizing temperature-resolved carbon fractions. Journal of the Air & Waste Management Association 2005;55(10):1456-1463. R831078 (2005)
    R831078 (Final)
  • Abstract from PubMed
  • Full-text: Taylor&Francis-Full Text PDF
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  • Abstract: Taylor&Francis-Abstract
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  • Journal Article Kim E, Hopke PK. Comparison between sample-species specific uncertainties and estimated uncertainties for the source apportionment of the speciation trends network data. Atmospheric Environment 2007;41(3):567-575. R831078 (Final)
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  • Abstract: ScienceDirect-Abstract
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  • Journal Article Ogulei D, Hopke PK, Wallace LA. Analysis of indoor particle size distributions in an occupied townhouse using positive matrix factorization. Indoor Air 2006;16(3):204-215. R831078 (2005)
    R831078 (Final)
  • Abstract from PubMed
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  • Abstract: Wiley-Abstract
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  • Journal Article Ogulei D, Hopke PK, Zhou L, Pancras JP, Nair N, Ondov JM. Source apportionment of Baltimore aerosol from combined size distribution and chemical composition data. Atmospheric Environment 2006;40(Suppl 2):396-410. R831078 (2005)
    R831078 (Final)
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  • Abstract: ScienceDirect-Abstract
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  • Other: ScienceDirect - Full Text PDF
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  • Journal Article Ogulei D, Hopke PK, Ferro AR, Jaques PA. Factor analysis of submicron particle size distributions near a major United States-Canada trade bridge. Journal of the Air & Waste Management Association 2007;57(2):190-203. R831078 (Final)
  • Abstract from PubMed
  • Full-text: Taylor&Francis-Full Text PDF
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  • Abstract: Taylor&Francis-Abstract
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  • Journal Article Ogulei D, Hopke PK, Chalupa DC, Utell MJ. Modeling source contributions to submicron particle number concentrations measured in Rochester, New York. Aerosol Science and Technology 2007;41(2):179-201. R831078 (Final)
    R827354 (Final)
    R827354C001 (Final)
    R827354C003 (Final)
    R832415 (2010)
    R832415 (2011)
    R832415 (Final)
    R832415C001 (2011)
    R832415C003 (2011)
  • Full-text: Taylor&Francis - Full Text HTML
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  • Abstract: Taylor&Francis - Abstract
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  • Other: Taylor&Francis - Full Text PDF
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  • Journal Article Paatero P, Hopke PK, Begum BA, Biswas SK. A graphical diagnostic method for assessing the rotation in factor analytical models of atmospheric pollution. Atmospheric Environment 2005;39(1):193-201. R831078 (2004)
    R831078 (2005)
    R831078 (Final)
  • Full-text: ScienceDirect - Full Text HTML
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  • Abstract: ScienceDirect-Abstract
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  • Journal Article Zhao W, Hopke PK, Zhou L. Spatial distribution of source locations for particulate nitrate and sulfate in the upper-midwestern United States. Atmospheric Environment 2007;41(9):1831-1847. R831078 (Final)
  • Full-text: ScienceDirect - Full Text HTML
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  • Abstract: ScienceDirect-Abstract
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  • Supplemental Keywords:

    receptor models, PM2.5, Positive Matrix Factorization, PMF, Unmix, advanced factor models, bootstrap, rotational ambiguity, source resolution, source apportionment,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, Air Quality, particulate matter, air toxics, Environmental Chemistry, Air Pollution Effects, Monitoring/Modeling, Environmental Monitoring, Engineering, Chemistry, & Physics, Environmental Engineering, air quality modeling, health effects, particle size, carbon aerosols, atmospheric dispersion models, particulate organic carbon, atmospheric particulate matter, atmospheric measurements, model-based analysis, chemical characteristics, environmental measurement, PM 2.5, positive matrix factorization, atmospheric particles, aerosol particles, mass spectrometry, motor vehicle emissions, emissions monitoring, air quality models, airborne particulate matter, diesel exhaust, emissions, thermal desorption, air sampling, carbon particles, air quality model, mobile sources, ultrafine particulate matter, particulate matter mass, PM2.5, modeling studies, aersol particles, aerosol analyzers, chemical speciation sampling, measurement methods, particle size measurement, carbonaceous particulate matter

    Progress and Final Reports:

    Original Abstract
  • 2004 Progress Report
  • 2005 Progress Report
  • 2006