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

Lianfa Li, Roxana Khalili, Frederick Lurmann, Nathan Pavlovic, Jun Wu, Yan Xu, Yisi Liu, Karl O’Sharkey, Beate Ritz, Luke Oman, Meredith Franklin, Theresa Bastain, Shohreh F. Farzan, Carrie Breton, Rima Habre,
Knowledge-informed deep learning to mitigate bias in joint air pollutant prediction,
Environment International,
Volume 206,
2025,
109915,
ISSN 0160-4120,
https://doi.org/10.1016/j.envint.2025.109915.
(https://www.sciencedirect.com/science/article/pii/S016041202500666X)
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 into neural network architectures for multi-pollutant prediction. Our approach models air pollutant pairs across geographically distinct domains (NO2/NOx for California; PM2.5/PM10 for mainland China), providing a comprehensive framework for physics-constrained atmospheric modeling at high resolution. Through an efficient framework, our methodology demonstrates that incorporating proxy advection and diffusion fields as physical constraints fundamentally alters learning dynamics, reducing generalization error and eliminating systematic bias inherent in data-driven approaches while improving computational efficiency compared to graph networks. Site-based validation reveals unprecedented bias reduction: 21%–42% for nitrogen oxides and 16%–17% for particulate matter compared to the baseline deep learning methods. Our methodology uniquely generates physically interpretable parameters while providing explicit uncertainty quantification through ensemble techniques. The substantial bias reduction coupled with physically interpretable parameters has immediate implications for improving air pollutant exposure assessment and understanding in epidemiological research, potentially transforming health effect evaluations that rely on accurate spatial predictions.
Keywords: Deep learning; Physics-informed modeling; Air pollution; Knowledge fusion; Bias mitigation; Joint prediction