An Ensemble Spatiotemporal Model for Predicting PM2.5 Concentrations.

ABSTRACT: Although fine particulate matter with a diameter of <2.5 μm (PM2.5) has a greater negative impact on human health than particulate matter with a diameter of <10 μm (PM10), measurements of PM2.5 have only recently been performed, and the spatial coverage of these measurements is limited. Comprehensively assessing PM2.5 pollution levels and the cumulative health effects is difficult because PM2.5 monitoring data for prior time periods and certain regions are not available. In this paper, we propose a promising approach for robustly predicting PM2.5 concentrations. In our approach, a generalized additive model is first used to quantify the non-linear associations between predictors and PM2.5, the bagging method is used to sample the dataset and train different models to reduce the bias in pre

SUBMITTER: Li L 

PROVIDER: S-EPMC5451999 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature