Estimating ground-level PM_(2.5) concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model

摘要

Previous studies showed that fine particulate matter (PM_(2.5), particles smaller than 2.5μm in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM_(2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM_(2.5) exposure estimates. However, the coarse resolutions (e.g., 10km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM_(2.5) characteristics that are crucial to population-based PM_(2.5) health effects research. In this paper, a new aerosol product with 1km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM_(2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R~2 of 0.83, mean prediction error (MPE) of 1.89μg/m~3, and square root of the mean squared prediction errors (RMSPE) of 2.73μg/m3 in model fitting, and R~2 of 0.67, MPE of 2.54μg/m~3, and RMSPE of 3.88μg/m~3 in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1km spatial resolution MAIAC AOD can be used to estimate PM_(2.5) concentrations.