Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of Nitrogen Oxides
This work addresses bias reduction in air quality prediction for health and policy applications, representing a novel hybrid approach rather than a foundational breakthrough.
The paper tackles the problem of high estimation bias in machine learning predictions of nitrogen oxides (NOx) concentrations by introducing a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints, resulting in a bias reduction of 21-42%.
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.