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.

翻译：大气氮氧化物（NOx）主要来源于燃料燃烧，已被公认对健康和环境具有急性和慢性影响。机器学习方法显著提升了我们以高时空分辨率预测地面NOx浓度的能力，但由于缺乏空气污染动态的物理和化学知识，可能面临较高的估计偏差。化学传输模型利用这些知识，然而要准确预测地面浓度通常需要进行广泛的后校准。本文提出了一种物理信息深度学习框架，该框架编码了对流-扩散机制和流体动力学约束，以联合预测NO2和NOx，并将机器学习模型偏差降低21-42%。我们的方法捕捉了NO2和NOx的精细尺度输运，生成了稳健的空间外推，并提供了明确的误差估计。该框架融合了化学传输模型的物理化学原理驱动力与机器学习的预测能力，适用于空气质量暴露、健康和政策应用。与纯数据驱动的机器学习方法相比，我们的方法在联合预测NO2和NOx方面实现了显著改进和前所未有的偏差降低。