Knowledge-informed deep learning to mitigate bias in joint air pollutant prediction.

ABSTRACT: Accurate prediction of atmospheric air pollutants is critical for public health protection and environmental management. Traditional machine learning (ML) methods achieve high spatial resolution but lack physicochemical constraints, leading to systematic biases that compromise exposure estimates for epidemiological studies. Chemical transport models incorporate atmospheric physics but require expensive parameterization and often fail to capture local-scale variability crucial for health impact assessment. This gap between data-driven accuracy and physical realism presents a major obstacle to advancing air quality science. We address this challenge through a novel physics-informed deep learning framework that integrates advection-diffusion equations and fluid dynamics constraints directly i

SUBMITTER: Li L 

PROVIDER: S-EPMC12810719 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature