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The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises\ud employing real-world and synthetic input datasets. To that end, the results obtained by different\ud practitioners using ten different RMs were compared with a reference. In order to explain the differences\ud in the performances and uncertainties of the different approaches, the apportioned mass, the number of\ud sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all\ud evaluated using the methodology described in Belis et al. (2015).\ud In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47\ud different source apportionment model results met the 50% standard uncertainty quality objective\ud established for the performance test. In addition, 68% of the SCE uncertainties reported in the results\ud were coherent with the analytical uncertainties in the input data.\ud The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances\ud in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in\ud the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined\ud chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those\ud better quantified by the models while those with contributions to the PM mass close to 1% represented a\ud challenge.\ud The results of the assessment indicate that RMs are capable of estimating the contribution of the major\ud pollution source categories over a given time window with a level of accuracy that is in line with the\ud needs of air quality management