Month: November 2018

Meng et al. Space-time trends of PM2.5 constituents in the Conterminous United States estimated by a machine learning approach, 2005-2015

Yang Liu PhD November 6, 2018 0 Comments
We developed national  machine-learning models  to provide interpretable results, to predict concentrations of PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level.  PubMed Link EI Link

Xiao et al: An ensemble machine-learning model to predict historical PM2.5 concentrations in China from satellite data

Yang Liu PhD November 1, 2018 0 Comments
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