Using aerosol optical thickness to predict ground-level PM 2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS

Abstract

Using two general linear regression models, we compared the ability of the aerosol optical thickness (AOT) retrieved by the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to predict ground-level PM 2.5 concentrations in St. Louis, MO and its surrounding areas . The models included meteorological parameters obtained from the National Oceanic and Atmospheric Administration (NOAA)'s Rapid Update Cycle (RUC20) model as covariates. Both MISR and MODIS AOT values were highly significant predictors of PM 2.5 concentrations. The MISR and MODIS models have overall comparable predictability of ground-level PM 2.5 concentrations. The MISR model explained a slightly greater percentage (62%) of the variability in PM 2.5 concentrations than the MODIS model (51%), and thus was a better fit. Over the entire data range, the MISR model underpredicts PM 2.5 concentrations by approximately 12%, whereas the MODIS model underpredicts PM 2.5 concentrations by approximately 18%. This underestimation occurred primarily at higher PM 2.5 concentrations in both models. The regression coefficients from two models were highly comparable, suggesting that combining MISR and MODIS AOT data might benefit from the higher predicting accuracy of MISR and the better spatial coverage of MODIS. The newly developed particle size/shape indicators in MISR and MODIS aerosol product did not significantly improve our ability to predict PM 2.5 concentrations using AOT measurements. Finally, using hourly PM 2.5 concentrations did not seem to improve its association with AOT for the current study region.