Abstract: Nitrogen dioxide (NO2) has many adverse impacts on human health and climate change. With the acceleration of urbanization and industrialization in China, NO2 pollution has become a growing concern. However, releveant research shows that the traditional monitoring results of a single site can only represent the concentration of pollutants within a few square kilometers, and cannot provide large-scale pollutant distribution information. Compared with site monitoring, satellite remote sensing can provide large-scale and spatiotemporal continuous data. Based on NO2 column densities of Sentinel-5 Precursor and other auxiliary data such as weather and population density, a deep learning model (DNN) to predict groundlevel NO2 concentration is built in this work, and then the model is verified by two cross-validation strategies. In the sample-based cross validation, the determination coefficient R2, root mean square error (RMSE) and mean absolute error (MAE) of the model are 0.80、7.72 μg/m3 and 5.31 μg/m3, respectively, while in the site-based cross validation, they are 0.74、8.95 μg/m3 and 6.01 μg/m3, respectively. Both of the two cross-validation results indicate that the DNN model has excellent overall predictive performance and spatial generalization ability. In addition, the comparisons with the other classic geostatistics and machine learning algorithms also show that the predictive performance of the deep learning algorithm is better than that of the other methods. Finally, the trained model is applied to generate NO2 distribution with 0.1° spatial resolution across Beijing-Tianjin-Hebei region.

Key words: nitrogen dioxide, machine learning, Sentinel 5P, remote sensing