Laboratory-based or in situ PM2.5 source profiles may not represent the pollutant composition for the sources in a different study location due to spatially and temporally varying characteristics, such as fuel or crustal element composition, or due to differences in emissions behavior under ambient versus laboratory conditions. In this work, PM2.5 source profiles were estimated for 20 sources using a novel optimization approach that incorporates observed concentrations with source impacts from a chemical transport model (CTM) to capture local pollutant characteristics. Nonlinear optimization was used to minimize the error between source profiles, CTM source impacts, and observations. In a 2006 U.S. application, spatial and seasonal variability was seen for coal combustion, dust, fires, metals processing, and other source profiles when compared to the reference profiles, with variability in species fractions over 400% (calcium in dust) compared to mean contributions of the same species. Revised profiles improved the spatial and temporal bias in modeled concentrations of several trace metal species, including Na, Al, Ca, Mn, Cu, As, Se, Br, and Pb. In an application of the CMB-iteration model for two U.S. cities, revised profiles estimated higher biomass burning and dust impacts for summer compared with previous studies. Source profile optimization can be useful for source apportionment studies that have limited availability of source profile data for the location of interest.