title: Methods for estimating uncertainty in PMF solutions: examples with ambient air and water quality data and guidance on reporting PMF results.
abstract: The new version of EPA's positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP). These methods capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. To demonstrate the utility of the EE methods, results are presented for three data sets: (1) speciated PM2.5 data from a chemical speciation network (CSN) site in Sacramento, California (2003-2009); (2) trace metal, ammonia, and other species in water quality samples taken at an inline storage system (ISS) in Milwaukee, Wisconsin (2006); and (3) an organic aerosol data set from high-resolution aerosol mass spectrometer (HR-AMS) measurements in Las Vegas, Nevada (January 2008). We present an interpretation of EE diagnostics for these data sets, results from sensitivity tests of EE diagnostics using additional and fewer factors, and recommendations for reporting PMF results. BS-DISP and BS are found useful in understanding the uncertainty of factor profiles; they also suggest if the data are over-fitted by specifying too many factors. DISP diagnostics were consistently robust, indicating its use for understanding rotational uncertainty and as a first step in assessing a solution's viability. The uncertainty of each factor's identifying species is shown to be a useful gauge for evaluating multiple solutions, e.g., with a different number of factors.
authors.affiliation: Sonoma Technology, Inc. 1455N. McDowell Blvd, Suite D, Petaluma, CA 94954, United States.
authors.forename: Steven G
authors.initials: SG
authors.lastname: Brown
authors.affiliation: Geometric Tools, LLC, 2909 181st Ave NE, Redmond, WA 98052, United States.
authors.forename: Shelly
authors.initials: S
authors.lastname: Eberly
authors.affiliation: University of Helsinki, Dept. of Physics, Rikalantie 6, 00970, Helsinki, Finland.
authors.forename: Pentti
authors.initials: P
authors.lastname: Paatero
authors.affiliation: U.S. EPA, Office of Research and Development, Research Triangle Park, NC, United States.
authors.forename: Gary A
authors.initials: GA
authors.lastname: Norris
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis / Q000706:statistics & numerical data*; D004784:Environmental Monitoring / Q000379:methods*; D052638:Particulate Matter / Q000032:analysis*; D035501:Uncertainty
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Air pollution; EPA PMF; Positive matrix factorization; Receptor modeling; Water pollution