Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease

Abstract

Background: Numerous studies have found adverse health effects of acute and chronic exposure to fine particulate matter (PM2.5). Air pollution epidemiological studies relying on ground measurements provided by monitoring networks are often limited by sparse and unbalanced spatial distribution of the monitors. Studies have found correlations between satellite aerosol optical depth (AOD) and PM2.5 in some land regions. Satellite aerosol data may be used to extend the spatial coverage of PM2.5 exposure assessment. This study was to investigate correlation between PM2.5 and AOD in the conterminous USA, to derive a spatially complete PM2.5 surface by merging satellite AOD data and ground measurements based on the potential correlation, and to examine if there is an association of coronary heart disease with PM2.5.

Results: Years 2003 and 2004 daily MODIS (Moderate Resolution Imaging Spectrometer) Level 2 AOD images were collated with US EPA PM2.5 data covering the conterminous USA. Pearson's correlation analysis and geographically weighted regression (GWR) found that the relationship between PM2.5 and AOD is not spatially consistent across the conterminous states. The average correlation is 0.67 in the east and 0.22 in the west. GWR predicts well in the east and poorly in the west. The GWR model was used to derive a PM2.5 grid surface using the mean AOD raster calculated using the daily AOD data (RMSE = 1.67 microg/m3). Fitting of a Bayesian hierarchical model linking PM2.5 with age-race standardized mortality rates (SMRs) of chronic coronary heart disease found that areas with higher values of PM2.5 also show high rates of CCHD mortality: = 0.802, posterior 95% Bayesian credible interval (CI) = (0.386, 1.225).

Conclusion: There is a spatial variation of the relationship between PM2.5 and AOD in the conterminous USA. In the eastern USA where AOD correlates well with PM2.5, AOD can be merged with ground PM2.5 data to derive a PM2.5 surface for epidemiological study. The study found that chronic coronary heart disease mortality rate increases with exposure to PM2.5.

Figure 1

Monitoring sites with top 20 correlations of PM_2.5 with AOD.

Figure 2

Surface of Pearson's correlation between PM_2.5 and AOD.

Figure 3

Local R square of geographically weighted regression.

Figure 4

Coefficient raster surface for AOD from geographically weighted regression.

Figure 5

Trace plots of the 80,000 Markov Chain Monte Carlo (MCMC) updates. Simulation trace plots for the intercept and the effect of PM_2.5 on chronic coronary heart disease from the Bayesian hierarchical model with a convolution prior. Horizontal axis represents iteration number and vertical axis represents simulated parameter value. The red trace is for one Markov chain, and the blue for the other.

Figure 6

Kernel estimates of the posterior densities of the fixed effects in the Bayesian hierarchical model. Horizontal axis represents simulated parameter values and vertical axis represents the density of each value.