Hopke and co-workers (6), Heidam (7), Henry (8), and Barrie and Barrie (9) applied principal component analysis (PCA) to source identification, but Paatero and Tapper (10, 11) showed that PCA cannot provide a trueminimal variance solution since they are based on an incorrect weighting. In view of the limitations of PCA, a new technique, positive matrix factorization (PMF), was developed for sources identification and apportionment (12). The distinct advan- tages of PMF over PCA are that non-negative constraints are built in PMF models and PMF does not rely on the information fromthe correlationmatrix but utilizes a point- by-point least-squaresminimization scheme (12). It has been reported (13) that the source profiles produced by.
