[Daily NO2 Simulation Research Based on Automatic Machine Learning Ensemble Models].

Abstract

To understand the spatial distribution of NO₂ near the surface, we utilized measured data from NO₂ 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 NO₂ 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 NO₂ concentration in the Yangtze River Delta region from March 2020 to February 2021. The results indicated that the RMAE and MAE values of the Stacking ensemble model were 7.078 and 5.270, respectively, which outperformed the single algorithms of ET, RF, XGBoost, LightGBM, and Catboost. The spatial distribution of high NO₂ concentrations in the Yangtze River Delta region, consisting of three provinces and one municipality, exhibited a U-shaped pattern with the convergence point located at the intersection of the three provinces, extending towards the southwest. Notably, urban pollution was particularly significant in the urban agglomerations centered around Shanghai, Hangzhou, Nanjing, and Hefei. There were 27 cities that exceeded the national standard daily limit. Changzhou was the city with the most serious NO₂ pollution, with the NO₂ concentration exceeding the standard for 14 d, followed by Shanghai, with 13 d. In terms of seasonal variation, the order of severity was as follows： winter, autumn, spring, and summer, with the least NO₂ pollution occurring on July 9th during the summer, and the most severe NO₂ pollution was observed on December 23rd during the winter.