Iterated confirmatory factor analysis for pollution source apportionment

Abstract

Many approaches for pollution source apportionment have been considered in the literature, most of which are based on the chemical mass balance equations. The simplest approaches for identifying the pollution source contributions require that the pollution source profiles are known. When little or nothing is known about the nature of the pollution sources, exploratory factor analysis, confirmatory factor analysis, and other multivariate approaches have been employed. In recent years, there has been increased interest in more flexible approaches, which assume little knowledge about the nature of the pollution source profiles, but are still able to produce nonnegative and physically realistic estimates of pollution source contributions. Confirmatory factor analysis can yield a physically interpretable and uniquely estimable solution, but requires that at least some of the rows of the source profile matrix be known. In the present discussion, we discuss the iterated confirmatory factor analysis (ICFA) approach. ICFA can take on aspects of chemical mass balance analysis, exploratory factor analysis, and confirmatory factor analysis 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. ICFA is illustrated using PM_2.5 data from Washington D.C., and a simulation study illustrates the relative strengths of ICFA, chemical mass balance approaches, and positive matrix factorization (PMF). Copyright © 2006 John Wiley & Sons, Ltd.