Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM_2.5) using satellite data over large regions.

ABSTRACT: Reconstructing the distribution of fine particulate matter (PM_2.5) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM_2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM_2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM_2.5 on a 1×1 km² resolution

SUBMITTER: Just AC 

PROVIDER: S-EPMC7591135 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature