Estimating NOx Emissions in China via Multisource Satellite Data and Deep Learning Model - Kun Cai; Yanfang Shao; Yinghao Lin; Shenshen Li; Minghu Fan (Remote Sensing)
Nitrogen oxides (NOx) are known to be irritant gases, which present considerable risks to human health. TROPOMI NO2 vertical column density (VCD) is commonly employed to estimate NOx emissions through the integration of complex models. However, satellite data often suffer from incompleteness, hindering the ability to achieve long-term and comprehensive estimates. In this study, we propose a reconstruction method to achieve comprehensive coverage of NO2 VCD in China by leveraging the relationship between satellite data and meteorological variables. In addition, the CNN-BiLSTM-ATT model was developed to estimate China’s monthly NOx emissions from 2021 to 2023 in combination with other ancillary data, such as ERA5 meteorological data, topographic data, and nighttime light data, achieving a correlation coefficient (R) of 0.83 and a root mean squared error (RMSE) of 9.05 tons (T). The factors influencing NO2 VCD were assessed using SHAP values, and the spatiotemporal characteristics and density distribution of NOx emissions were analyzed. Additionally, annual emission trends were evaluated. This study offers valuable insights for air quality management and policymaking, contributing to efforts focused on mitigating the adverse health and environmental impacts of NOx emissions.