Ensemble-based deep learning for estimating PM_2.5 over California with multisource big data including wildfire smoke.

ABSTRACT:

Introduction

Estimating PM_2.5 concentrations and their prediction uncertainties at a high spatiotemporal resolution is important for air pollution health effect studies. This is particularly challenging for California, which has high variability in natural (e.g, wildfires, dust) and anthropogenic emissions, meteorology, topography (e.g. desert surfaces, mountains, snow cover) and land use.

Methods

Using ensemble-based deep learning with big data fused from multiple sources we developed a PM_2.5 prediction model with uncertainty estimates at a high spatial (1 km × 1 km) and temporal (weekly) resolution for a 10-year time span (2008-2017). We leveraged autoencoder-based full residual deep networks to model complex nonlinear interrelationships among PM

SUBMITTER: Li L 

PROVIDER: S-EPMC7643812 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature