Multivariate spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts

Understanding the factors that affect spatial differences in PM2.5 composition is crucial for implementing emissions control and health policies. Although previous studies have explored modeling of spatial patterns as a tool to improve human exposure assessment, little work has employed a multivariate clustering approach to identify spatial patterns in particle composition. In this study, we used this approach to assess the spatial patterns of ambient PM2.5 elemental concentrations in Eastern Massachusetts in the United States. To distinguish one cluster of sites from another, we considered air pollution sources and geodemographic variables. We evaluated spatial patterns for 11 elemental components of ambient PM2.5, which included S, K, Ca, Fe, Zn, Cu, Ti, Al, Pb, V, and Ni. The analyses for S, Ca, Cu, Ti, Al, and Pb resulted in: 2 clusters for Fe, Zn, V, and Ni; 3 clusters; and for 12 clusters for K. Overall, our findings suggest substantial variation of clusters among PM2.5 components. In addition, land use, population density, and daily traffic were used as variables to more effectively characterize clusters of sites. We used R2 values to estimate the effectiveness of each variable in characterizing clusters. Larger R2 values indicate better the discrimination among the sites. For example, population density had the highest R2 value when the analysis was performed for S, Ca, Zn, Ti, Al, Pb, and V; land use presented the highest R2 value for Cu, V, and Ni; and, traffic showed the highest R2 value for PM2.5 mass concentration. This study improves the ability to model both the between- and within-area variability of source emissions and pollution regime, using concentrations of PM2.5 components.