Assessment of heterogeneity of metal composition of fine particulate matter collected from eight U.S. counties using principal component analysis

The main objectives of this study are to (1) characterize chemical constituents of particulate matter (PM) and (2) compare overall differences in PM collected from eight U.S. counties. This project was undertaken as a part of a larger research program conducted by the Johns Hopkins Particulate Matter Research Center (JHPMRC). The goal of the JHPMRC is to explore the relationship between health effects and exposure to ambient PM of differing composition. The JHPMRC collected weekly filter-based ambient fine particle samples from eight U.S. counties between January 2008 and January 2010. Each sampling effort consisted of a 5-6- week sampling period. Filters were analyzed for 25 metals using inductively coupled plasma mass spectrometry (ICP-MS). Overall compositional differences were ranked by principal component analysis (PCA). The results showed that weekly concentrations of each element varied 3-40 times between the eight counties. PCA showed that the first five principal components explained 85% of the total variance. The authors found significant overall compositional differences in PM as the average of standardized principal component scores differed between the counties. These findings demonstrate PCA is a useful tool to identify the differences in PM compositional mixtures by county. These differences will be helpful for epidemiological and toxicological studies to help explain why health risks associated with PM exposure are different in locations with similar mass concentrations of PM. Implications: Previous studies have demonstrated associations between health effects and particulate matter (PM) using a single component or a combination of few components. Other studies have shown constituents of PM can vary greatly by location and that these differences may explain why the health effects associated with PM exposure are different by location. However, a single or a combination of a few components cannot represent PM as a whole. To address the need for evaluating PM as a complex mixture, the authors demonstrated the utility of principal component analysis to assess heterogeneity of PM.