A large number of epidemiological studies have reported associations between ambient PM2.5 concentrations and adverse health outcomes. In most of these investigations, subject-specific PM2.5 exposures were assessed by measuring ambient PM2.5 concentrations at one or more outdoor monitoring sites. However, sparse PM2.5 monitoring spatial networks may limit the ability to accurately assess human exposures to PM2.5. Satellite-based PM2.5 monitoring has the potential to complement ground PM2.5 monitoring networks, especially for regions with sparsely distributed monitors. Satellite remote sensing provides data on aerosol optical depth (AOD) which reflects particle abundance in the atmospheric column. Thus AOD has been used in statistical models to predict ground-level PM2.5 concentrations. However, previous studies have shown that AOD is not a strong predictor of PM2.5 ground levels. Another shortcoming of remote sensing is the large number of non-retrieval days due to clouds and snow.
Recently, we have developed statistical approaches to overcome these two shortcomings. First, we rendered AOD a robust predictor of PM2.5 mass concentration by introducing an AOD daily calibration approach through the use of mixed effects models. Second, we have developed models that combine AOD and ground monitoring data to predict PM2.5 concentrations during non-retrieval days. We have applied these methodologies to assess human exposures to PM2.5 in the New England region. Subsequently, these assessments were used to investigate the effects of PM2.5 hospital admissions and mortality. During my presentation I will present results from these studies and demonstrated that remote sensing can have a tremendous impact on the fields of environmental monitoring and human exposure assessment.