Category: Air pollution exposure

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...

We developed a machine learning model to estimate daily PM2.5 levels in Lima Peru. This is the first advanced model to incorporate satellite remote sensing data as well as variables from chemical transport and forecast models to predict daily PM2.5 levels in South America. Our results indicates that concentrations are low in the coast and rises with elevation up to the Andes Mountains due to...

We developed a machine learning approach to estimate historical PM2.5 levels in China. It overcame several previous issues related to satellite-based PM2.5 exposure estimates such as data missingness due to cloud cover, model stability across a large spatial domain, and robust hindcasting capability beyond the model fitting period.  ES&T Link   PubMed Link  

We proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors IJERPH Link (open access)