An expanded factor analysis model (ME-2) that is capable of taking into account the influence of independent variables such as wind speed, wind direction, time of year and other variables of the measured fine particle matter (PM2.5) concentration data was utilized for identifying sources of airborne pollutants and providing quantitative estimations of the contribution of each source. The chemical composition data used in this study were obtained from PM2.5 samples collected using the Interagency Monitoring of Protected Visual Environments samplers from August 1999 to December 2001 at an urban monitoring site in Washington, DC. The expanded model has been applied to two different data sets based on the particulate carbon variables. Such an approach had been successfully applied previously and provided improved source resolution in simulated and ambient concentration data. Initially, total OC and EC were used in the expanded model and were compared to the results using conventional positive matrix factorization that had been done previously using the individual carbon fractions data. In the other expanded model analysis, the eight carbon fractions were used during the modeling in order to ascertain if additional source information could be extracted from the data. In both cases, it was possible to separate diesel from spark-ignition vehicles. The use of the individual carbon fractions in the model provides information on what appears to be secondary organic aerosol formation.