Physics-Informed Deep Learning to Reduce the Bias in Joint Prediction of
  Nitrogen Oxides
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
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.