For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multisite multipollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multisite, multipollutant data. We present a Bayesian spatial multivariate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multisite, multipollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.

For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multisite multipollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multisite, multipollutant data. We present a Bayesian spatial multivariate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multisite, multipollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.

For the development of effective air pollution control strategies, it is crucial to identify the sources that are the principal contributors to air pollution and estimate how much each source contributes. Multivariate receptor modeling aims to address these problems by decomposing ambient concentrations of multiple air pollutants into components associated with different source types. With the expanded monitoring efforts that have been established over the past several decades, extensive multivariate air pollution data obtained from multiple monitoring sites (multi-site multi-pollutant data) are now available. Although considerable research has been conducted on modeling multivariate space-time data in other contexts, there has been little research on spatial multivariate receptor models for multi-site, multi-pollutant data. We present a Bayesian spatial multivairate receptor modeling (BSMRM) approach that can incorporate spatial correlations in multi-site, multi-pollutant data into the estimation of source composition profiles and contributions, based on discrete process convolution models for multivariate spatial processes. The new BSMRM approach enables predictions of source contributions at unmonitored sites as well as simultaneously dealing with model uncertainty caused by the unknown number of sources and identifiability conditions. The new approach can also provide uncertainty estimates for the predicted source contributions at any location, which was not possible in previous multivariate receptor modeling approaches. The proposed approach is applied to 24-hour ambient air concentrations of 17 Volatile Organic Compounds (VOCs) measured at nine monitoring sites in Harris County, Texas between 2003 and 2005. Supplementary materials for this article, including real data and MATLAB codes for implementing BSMRM, are available online on the journal web site.