Frequency Distributions and Spatial Analysis of Fine Particle Measurements in St. Louis During the Regional Air Pollution Study/Regional Air Monitoring System
Community, time-series epidemiology typically uses either 24-hour integrated particulate matter (PM) concentrations averaged across several monitors in a city or data obtained at a central monitoring site to relate PM concentrations to human health effects. If 24-hour integrated concentration differ substantially across an urban area, there is a significant potential for exposure misclassification since a limited number of ambient PM monitors are used to represent population-average ambient exposures. The spatial variability in PM2.5 (particulate matter <2.5 um in aerodynamic diameter), its elemental components, and source contributions at ten monitoring sites in St. Louis, Missouri were characterized using the ambient PM2.5 compositional data set of the Regional Air Pollution Study / Regional Air Monitoring System (RAPS/RAMS) conducted between 1975 and 1977. To estimate source category contributions, Positive Matrix Factorization (PMF) was applied to each ambient PM2.5 compositional data sets. The spatial distributions of elemental components of PM2.5 and source category contributions at the ten St. Louis sites were characterized using geometric means and standard deviations. Pearson correlation coefficients, and coefficients of divergence.
Sulfur is the most highly correlated element, PM2.5 concentrations are moderately correlated between all site pairs, and there are large differences in the spatial variability of component species. Although the secondary sulfate is the most highly correlated and shows the smallest spatial variability, there is a factor of two difference in secondary sulfate contributions between the highest and lowest sites. Motor vehicles represent the next most highly correlated source category. However, there is over a factor of three difference in motor vehicle contributions between the highest and lowest sites. The contributions from point source categories are much mor variable. For example, the contributions from incinerators show a difference of over a factor of ten between the sites with the lowest and highest contributions. This study demonstrates that the spatial distributions of elemental components of PM2.5 and source category contributions can be highly heterogeneous within a given airshed and thus, there can be significant exposure misclassification when a limited number of ambient PM monitors are used to represent population-average ambient exposures.