A new methodology to assess the performance and uncertainty of source apportionment models II: The results of two European intercomparison exercises

Abstract

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