This paper presents a quantitative comparison of the four most commonly used receptor models, namely absolute principal component scores (APCS), pragmatic mass closure (PMC), chemical mass balance (CMB) and positive matrix factorization (PMF). The models were used to predict the contributions of a wide variety of sources to PM2.5 mass in Halifax, Nova Scotia during the experiment to quantify the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS). However, particular emphasis was placed on the capacity of the models to predict the boreal wildfire smoke contributions during the BORTAS experiment. The performance of the four receptor models was assessed on their ability to predict the observed PM2.5 with an R2 close to 1, an intercept close to zero, a low bias and low RSME. Using PMF, a new woodsmoke enrichment factor of 52 was estimated for use in the PMC receptor model. The results indicate that the APCS and PMC receptor models were not able to accurately resolve total PM2.5 mass concentrations below 2 μg m-3. CMB was better able to resolve these low PM2.5 concentrations, but it could not be run on 9 of the 45 days of PM2.5 samples. PMF was found to be the most robust of the four models since it was able to resolve PM2.5 mass below 2 μg m-3, predict PM2.5 mass on all 45 days and utilise an unambiguous woodsmoke chemical tracer. The median woodsmoke relative contributions to PM2.5 estimated using PMC, APCS, CMB and PMF were found to be 0.08, 0.09, 3.59 and 0.14 μg m-3 respectively. The contribution predicted by the CMB model seemed to be clearly too high based on other observations. The use of levoglucosan as a tracer for woodsmoke was found to be vital for identifying this source.