Multivariate Receptor Models for Spatially Correlated Multi-Pollutant Data

• Activity Number: 84
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Eun Sug Park*+ and Mikyoung Jun
• Companies: Texas Transportation Institute and Texas A&M University
• Keywords: Multiple air pollutants ; Multiple monitoring sites ; Source apportionment ; Source composition profile ; Source contributions ; Spatial correlation

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 multi-pollutant 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. The proposed method yields more precise estimates of source profiles by accounting for spatial dependence in the estimation. In addition, 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 the simulated data and real multi-pollutant data collected from 8 monitoring sites in Harris County