Murray et al.: new method on PM2.5 Bayesian Ensemble Models

We develop a method to combine PM2.5 estimated from satellite-retrieved aerosol optical depth (AOD) and chemical transport model (CTM) simulations using statistical models. While most previous methods utilize AOD or CTM separately, we aim to leverage advantages offered by both data sources in terms of resolution and coverage using Bayesian ensemble averaging. Our approach differs from previous ensemble approaches in its ability to not only incorporate uncertainties in PM2.5 estimates from individual models but also to provide uncertainties for the resulting ensemble estimates. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms previously developed spatial-temporal statistical models that use either AOD or bias-corrected CTM simulations in cross-validation (CV) analyses.

Publication Link


Zhou et al.: Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the United States

Skin cancer is the most common type of cancer in the United States, the majority of which is caused by overexposure to ultraviolet (UV) irradiance, which is one component of sunlight. Our group worked with University of Iowa and the National Environmental Public Health Tracking Program at CDC to develop and disseminate county-level daily UV irradiance (2005 – 2015) and total solar irradiance (1991 – 2012) data for the contiguous United States (See intro on CDD Tracking webiste).  These datasets are freely available at the CDC Tracking Portal. Trend analysis also showed that national annual average daily solar and UV irradiances increased significantly over the years by about 0.3% and 0.5% per year, respectively. These datasets can help us understand the spatial distributions and temporal trends of solar and UV irradiances, and allow for improved characterization of UV and sunlight exposure in future studies.

The Full publication link is here.