Improving satellite-based PM_2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

ABSTRACT: Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM_2.5 is a promising way to fill the areas that are not covered by ground PM_2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM_2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM_2.5 concentration

SUBMITTER: Yu W 

PROVIDER: S-EPMC5539114 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature