A Positive Matrix Factorization receptor model for aerosol pollution source apportionment was fit to a synthetic dataset simulating one year of daily measurements of ambient PM_2.5 concentrations. A novel method was developed to estimate model fit uncertainty and bias at the daily time scale, as related to factor contributions. A balanced bootstrap is used to create replicate datasets, with the same model then fit to the data. Neural networks are trained to classify factors based upon chemical profiles, as opposed to correlating contribution time series. This classification is used to align factor orderings across results associated with the replicate datasets. Factor contribution uncertainty is assessed from the distribution of results associated with each factor. Comparing modeled factors with input, synthetic factors assesses bias. The results indicate that variability in factor contribution estimates may not encompass model error: contribution estimates can have small associated variability yet also be very biased.