Development and evaluation of a daily temporal interpolation model for fine particulate matter species concentrations and source apportionment

Abstract

The impacts of emissions sources on air quality in St. Louis, Missouri are assessed for use in acute health effects studies. However, like many locations in the United States, the speciated particulate matter (PM) measurements from regulatory monitoring networks in St. Louis are only available every third day. The power of studies investigating acute health effects of air pollution is reduced when using one-in-three day source impacts compared to daily source impacts. This paper presents a temporal interpolation model to estimate daily speciated PM2.5 mass concentrations and source impact estimates using one-in-three day measurements. The model is used to interpolate 1-in-3 day source impact estimates and to interpolate the 1-in-3 day PM species concentrations prior to source apportionment (SA). Both approaches are compared and evaluated using two years (June 2001–May 2003) of daily data from the St. Louis Midwest Supersite (STL-SS). Data withholding is used to simulate a 1-in-3 day data set from the daily data to evaluate interpolated estimates. After evaluation using the STL-SS data, the model is used to estimate daily source impacts at another site approximately seven kilometers (7 km) northwest of the STL-SS (Blair); results between the sites are compared. For interpolated species concentrations, the model performs better for secondary species (sulfate, nitrate, ammonium, and organic carbon) than for primary species (metals and elemental carbon), likely due to the greater spatial autocorrelation of secondary species. Pearson correlation (R) values for sulfate, nitrate, ammonium, elemental carbon, and organic carbon ranged from 0.61 (elemental carbon, EC2) to 0.97 (sulfate). For trace metals, the R values ranged from 0.31 (Ba) to 0.81 (K). The interpolated source impact estimates also indicated a stronger correlation for secondary sources. Correlations of the secondary source impact estimates based on measurement data and interpolation data ranged from 0.68 to 0.97, whereas for primary source contribution estimates the correlations ranged from −0.042 to 0.95. Comparison of daily source impact estimates with source impacts from the interpolation models indicated that interpolation of source contributions was preferable over interpolating species concentrations then applying a SA model. This was based on better agreement in the predicted source impact concentrations and higher correlation with daily SA results. Overall, this study indicates that the temporal interpolation model produces results that may be used to estimate source impacts for health studies, though the additional uncertainty should be considered.