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

C. A. Belis*, F. Karagulian, F. Amato, M. Almeida, P. Artaxo, D. C S Beddows, V. Bernardoni, M. C. Bove, S. Carbone, D. Cesari, D. Contini, E. Cuccia, E. Diapouli, K. Eleftheriadis, O. Favez, I. El Haddad, Roy Harrison, S. Hellebust, J. Hovorka, E. JangH. Jorquera, T. Kammermeier, M. Karl, F. Lucarelli, D. Mooibroek, S. Nava, J. K. Nøjgaard, P. Paatero, M. Pandolfi, M. G. Perrone, J. E. Petit, A. Pietrodangelo, P. Pokorná, P. Prati, A. S H Prevot, U. Quass, X. Querol, D. Saraga, J. Sciare, A. Sfetsos, G. Valli, R. Vecchi, M. Vestenius, E. Yubero, P. K. Hopke

*Corresponding author for this work

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

Original languageEnglish
Pages (from-to)240-250
Number of pages11
JournalAtmospheric Environment
Issue numberPart A
Early online date3 Nov 2015
Publication statusPublished - 1 Dec 2015


  • Intercomparison exercise
  • Model performance indicators
  • Model uncertainty
  • Particulate matter
  • Receptor models
  • Source apportionment

ASJC Scopus subject areas

  • Atmospheric Science
  • Environmental Science(all)


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