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RECEPTOR MODELING OF AMBIENT PARTICULATE MATTER DATA USING POSITIVE MATRIX FACTORIZATION REVIEW OF EXISTING METHODS
Citation:
REFF, A. H., S. I. EBERLY, AND P. BHAVE. RECEPTOR MODELING OF AMBIENT PARTICULATE MATTER DATA USING POSITIVE MATRIX FACTORIZATION REVIEW OF EXISTING METHODS. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION. Air & Waste Management Association, Pittsburgh, PA, 57(2):146-154, (2007).
Impact/Purpose:
The objectives of this task are to continue development and improvement of EPA's mesoscale (regional through urban scale) air quality modeling systems, such as the Community Multiscale Air Quality (CMAQ) model, as air quality management and NAAQS implementation tools. This task focuses on needed research and development of air quality models targeted for a major CMAQ model release in FY08. Model development for a broad scope of application is envisioned. For example, CMAQ will need to be able to simulate air quality feedbacks to meteorology and climate as well as intercontinental transport. The 2008 release of CMAQ is timed to coincide with EPA/OAR's and the states' needs for an improved model for assessments of progress (mid-course corrections) in the post-SIP submittal timeframe.
Description:
Methods for apportioning sources of ambient particulate matter (PM) using the positive matrix factorization (PMF) algorithm are reviewed. Numerous procedural decisions must be made and algorithmic parameters selected when analyzing PM data with PMF. However, few publications document enough of these details for readers to evaluate, reproduce, or compare results between different studies. For example, few studies document why some species were used and others not used in the modeling, how the number of factors was selected, or how much uncertainty exists in the solutions. More thorough documentation will aid the development of standard protocols for analyzing PM data with PMF, and will reveal more clearly where research is needed to help future analysts select from the various possible procedures and parameters available in PMF. For example, research likely is needed to determine optimal approaches for handling data below detection limits, ways to apportion PM mass among sources identified by PMF, and to estimate uncertainties in the solution. The review closes with recommendations for documenting the methodological details of future PMF analyses.