Multivariate receptor models and model uncertainty

Eun Sug Park, Man Suk Oh, Peter Guttorp

Research output: Contribution to journalArticlepeer-review

45 Scopus citations


Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be done without some additional assumptions. A common approach to this problem is to estimate the number of sources, q, at the first stage, and then estimate source profiles and contributions at the second stage, given additional constraints (identifiability conditions) to prevent source rotation/transformation and the assumption that the q-source model is correct. These assumptions on the parameters (the number of sources and identifiability conditions) are the main source of model uncertainty in multivariate receptor modeling. In this paper, we suggest a Bayesian approach to deal with model uncertainties in multivariate receptor models by using Markov chain Monte Carlo (MCMC) schemes. Specifically, we suggest a method which can simultaneously estimate parameters (compositions and contributions), parameter uncertainties, and model uncertainties (number of sources and identifiability conditions). Simulation results and an application to air pollution data are presented.

Original languageEnglish
Pages (from-to)49-67
Number of pages19
JournalChemometrics and Intelligent Laboratory Systems
Issue number1-2
StatePublished - 28 Jan 2002

Bibliographical note

Funding Information:
Although the research described in this article has been funded by the United States Environmental Protection Agency through agreement CR825173-01-0 to the University of Washington, it has not been subjected to the agency's required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.


  • Factor analysis models
  • Latent variable models
  • Marginal likelihood
  • Model identifiability
  • Model uncertainty
  • Number of sources
  • Posterior model probability


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