Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations.

ABSTRACT: 3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km²) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpo

SUBMITTER: Ren X 

PROVIDER: S-EPMC9133919 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature