[Daily NO Simulation Research Based on Automatic Machine Learning Ensemble Models].

To understand the spatial distribution of NO near the surface, we utilized measured data from NO monitoring stations and combined it with column concentration data from the Tropospheric Monitoring Instrument （TROPOMI）, taking the Yangtze River Delta region as the study area. We considered the impact of factors such as population, elevation, and meteorological conditions on NO levels. We used automated machine learning to select five machine-learning algorithms with high simulation accuracy, namely ET, RF, XGBoost, LightGBM, and Catboost, and then integrated these five algorithms using the Stacking model to simulate the daily NO concentration in the Yangtze River Delta region from March 2020 to February 2021.