An ensemble-based model of PM_2.5 concentration across the contiguous United States with high spatiotemporal resolution.

ABSTRACT: Various approaches have been proposed to model PM_2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM_2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM_2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, e

SUBMITTER: Di Q 

PROVIDER: S-EPMC7063579 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature