A Time Series Analysis of PM2.5 Concentrations in the Southeastern U.S. Using MAIAC AOD in a Two-stage Spatial Statistical Model

Abstract

Background. Long term PM2.5 exposure has been associated with various health outcomes. However, ground monitoring networks leave large rural and suburban areas uncovered even in developed countries.In recent years, satellite-retrieved aerosol optical depth (AOD) has been used for PM2.5 concentration estimation due to its large spatial coverage. A limitation of the current AOD products is their coarse spatial resolution (10 – 20 km). Aims. We examined the PM2.5 predicting power of a new AOD product with 1 km spatial resolution retrieved by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. Methods. We developed a two stage spatial model with MAIAC AOD, meteorological and land use variables as covariates. The first stage linear mixed effects model accounts for the day-to-day variability of the PM2.5-AOD relationship, and the second stage geographically weighted regression model reduces the spatially varying residuals from the first stage. A 10-year model simulation was performed in the southeastern U.S. centered at the Atlanta Metro area. Results. Annual model R2 ranged from 0.52 to 0.83, and annual mean prediction error ranged from 1.97 to 2.58 ?g/m3. Model performance is significantly better than a similar model developed without MAIAC AOD. Our time series analysis results showed that PM2.5 concentration level in the study area was in a general declining trend from 2001 to 2010 with the exception of 2005, which could be attributed to higher sulfate concentrations related to increased power production during the warm season. Conclusions. MAIAC AOD is a strong predictor of ground level PM2.5 when used together with effect modifiers such as temperature and wind speed. The prediction error of our MAIAC AOD model at 1 km resolution is comparable with that of a similar MODIS AOD model at 10 km resolution.