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

Research output: Contribution to journalArticlepeer-review


  • C. A. Belis
  • F. Karagulian
  • F. Amato
  • M. Almeida
  • P. Artaxo
  • V. Bernardoni
  • M. C. Bove
  • S. Carbone
  • D. Cesari
  • D. Contini
  • E. Cuccia
  • E. Diapouli
  • K. Eleftheriadis
  • O. Favez
  • I. El Haddad
  • S. Hellebust
  • J. Hovorka
  • E. Jang
  • H. 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

Colleges, School and Institutes

External organisations

  • European Commission Joint Research Centre, Ispra
  • Spanish Research Council (IDÆA-CSIC)
  • Universidade de Lisboa
  • Universidade de Sao Paulo - USP
  • Università degli Studi di Milano and oINFN-Milan
  • University of Genoa
  • Finnish Meteorological Institute
  • ISAC-CNR Str.
  • Institute of Nuclear and Radiological Science and Technology
  • Institut National de l'Environnement Industriel et des Risques (INERIS)
  • Paul Scherrer Institut
  • University College Cork
  • Pontificia Universidad Católica de Chile
  • Institut für Energie- und Umwelttechnik e. V.
  • Norwegian Institute for Air Research (NILU)
  • Department of Physics and Astronomy and INFN
  • National Institute for Public Health and the Environment (RIVM)
  • Aarhus Universitet
  • University of Helsinki
  • University of Milano-Bicocca
  • Istituto per i Processi Chimico-Fisici del Consiglio Nazionale delle Ricerche (IPCF-CNR)
  • King Abdulaziz University
  • Miguel Hernández University
  • Clarkson University
  • Charles University in Prague


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