Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
Abstract
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction.
Keywords
Cite
@article{arxiv.2308.07441,
title = {Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides},
author = {Lianfa Li and Roxana Khalili and Frederick Lurmann and Nathan Pavlovic and Jun Wu and Yan Xu and Yisi Liu and Karl O'Sharkey and Beate Ritz and Luke Oman and Meredith Franklin and Theresa Bastain and Shohreh F. Farzan and Carrie Breton and Rima Habre},
journal= {arXiv preprint arXiv:2308.07441},
year = {2023}
}