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.

大气氮氧化物主要来自燃料燃烧，会对健康和环境造成急性和慢性影响。本研究提出了一种基于物理知识的深度学习框架，结合了传输模型和机器学习的优势，可以准确预测 NO2 和 NOx 的地面浓度，并降低估计偏差。该框架在空气质量暴露、健康和政策应用方面取得了显著的改进。