Multivariate Receptor Models for Spatially Correlated Multipollutant Data

Abstract

The goal of multivariate receptor modeling is to estimate the profiles of major pollution sources and quantify their impacts based on ambient measurements of pollutants. Traditionally, multivariate receptor modeling has been applied to multiple air pollutant data measured at a single monitoring site or measurements of a single pollutant collected at multiple monitoring sites. Despite the growing availability of multipollutant data collected from multiple monitoring sites, there has not yet been any attempt to incorporate spatial dependence that may exist in such data into multivariate receptor modeling. We propose a spatial statistics extension of multivariate receptor models that enables us to incorporate spatial dependence into estimation of source composition profiles and contributions given the prespecified number of sources and the model identification conditions. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. More importantly, it enables predictions of source contributions at unmonitored sites as well as when there are missing values at monitoring sites. The method is illustrated with simulated data and real multipollutant data collected from eight monitoring sites in Harris County, Texas. Supplementary materials for this article, including data and R code for implementing the methods, are available online on the journal web site.

KEY WORDS:

• Multiple air pollutants
• Multiple monitoring sites
• Source apportionment
• Source composition profile
• Source contributions
• Spatial correlation

ACKNOWLEDGMENTS

Mikyoung Jun’s research was supported by NSF grant DMS-0906532. Eun Sug Park’s research was supported by contract with 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. This publication is based in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors gratefully acknowledge the help of Ms. Melanie Hotchkiss, Dr. Jim Price, and Dr. Clifford Spiegelman with the acquisition of the 24-hr canister VOC data. The authors also thank the editor, associate editor, and two referees for valuable comments and suggestions that led to substantial improvements in the article.