Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China.

ABSTRACT: Ambient PM_2.5 pollution poses a major risk to public health in China, contributing to significant mortality and morbidity. While overall PM_2.5 concentrations have declined in recent years, the changes in PM_2.5 chemical constituents remain inadequately understood due to limited ground monitoring networks. We developed a Super Learner model that integrates MISR satellite data, chemistry transport model simulations, and land use information to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. The model achieved high predictive accuracy with a cross-validation R² of 0.84 and an RMSE of 4.9 μg/m³. Our findings show elevated OC levels in Northern China, driven by industrial activities with concentr

SUBMITTER: Hang Y 

PROVIDER: S-EPMC11824872 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature