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A Positive Matrix Factorization receptor model for aerosol pollution sourceapportionment was fit to a synthetic dataset simulating one year of dailymeasurements of ambient PM<sub>2.5</sub> concentrations, comprised of 39 chemicalspecies from nine pollutant sources. A novel method was developed toestimate model fit uncertainty and bias at the daily time scale, as relatedto factor contributions. A circular block bootstrap is used to createreplicate datasets, with the same receptor model then fit to the data.Neural networks are trained to classify factors based upon chemicalprofiles, as opposed to correlating contribution time series, and thisclassification is used to align factor orderings across the model resultsassociated with the replicate datasets. Factor contribution uncertainty isassessed from the distribution of results associated with each factor.Comparing modeled factors with input factors used to create the syntheticdata assesses bias. The results indicate that variability in factorcontribution estimates does not necessarily encompass model error:contribution estimates can have small associated variability across resultsyet also be very biased. These findings are likely dependent oncharacteristics of the data

Assessing positive matrix factorization model fit: a new method to estimate uncertainty and bias in factor contributions at the measurement time scale