Application of Bayesian Additive Regression Trees for Estimating Daily Concentrations of PM2.5 Components.

ABSTRACT: Bayesian additive regression tree (BART) is a recent statistical method that combines ensemble learning and nonparametric regression. BART is constructed under a probabilistic framework that also allows for model-based prediction uncertainty quantification. We evaluated the application of BART in predicting daily concentrations of four fine particulate matter (PM2.5) components (elemental carbon, organic carbon, nitrate, and sulfate) in California during the period 2005 to 2014. We demonstrate in this paper how BART can be tuned to optimize prediction performance and how to evaluate variable importance. Our BART models included, as predictors, a large suite of land-use variables, meteorological conditions, satellite-derived aerosol optical depth parameters, and simulations from a chemical

SUBMITTER: Zhang T 

PROVIDER: S-EPMC8315111 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature