Robust Bayesian multivariate receptor modeling

Eun Sug Park, Man Suk Oh

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Multivariate receptor modeling aims to unfold the multivariate air pollution data into components associated with different sources of air pollution based on ambient measurements of air pollutants. It is now a widely accepted approach in source identification and apportionment. An evolving area of research in multivariate receptor modeling is to quantify uncertainty in estimated source contributions as well as model uncertainty caused by the unknown identifiability conditions, sometimes referred to as rotational ambiguity. Unlike the uncertainty estimates for the source composition profiles that have been available in commonly used receptor modeling tools such as positive matrix factorization, little research has been conducted on the uncertainty estimation for the source contributions or the identifiability conditions. Bayesian multivariate receptor modeling based on Markov chain Monte Carol methods is an attractive approach as it offers a great deal of flexibility in both modeling and estimation of parameter uncertainty and model uncertainty. In this paper, we propose a robust Bayesian multivariate receptor modeling approach that can simultaneously estimate uncertainty in source contributions as well as in compositions and uncertainty due to the unknown identifiability conditions by extending the previous Bayesian multivariate receptor modeling in two ways. First, we explicitly account for nonnegativity constraints on the source contributions, in addition to the nonnegativity constraints on the source compositions, in both parameter estimation and model uncertainty estimation. Second, we account for outliers that may often exist in the air pollution data in estimation by considering a heavy-tailed error distribution. The approach is illustrated with both simulated data and real PM2.5 speciation data from Phoenix, Arizona, USA.

Original languageEnglish
Pages (from-to)215-226
Number of pages12
JournalChemometrics and Intelligent Laboratory Systems
StatePublished - 15 Dec 2015

Bibliographical note

Funding Information:
Park's research was supported in part by the contract to the Health Effects Institute (HEI) , an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. R-82811201 ) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the EPA or motor vehicle and engine manufacturers. Oh's research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( 2013R1A1A2005481 ). The authors gratefully acknowledge many helpful comments from the reviewer which significantly improved this paper.

Publisher Copyright:
© 2015 Elsevier B.V.


  • Model uncertainty
  • Outliers
  • Source apportionment
  • Uncertainty estimation


Dive into the research topics of 'Robust Bayesian multivariate receptor modeling'. Together they form a unique fingerprint.

Cite this