Nitrogen dioxide pollution in 346 Chinese cities: Spatiotemporal variations and natural drivers from multi-source remote sensing data

Abstract

In this study, tropospheric column concentration of nitrogen dioxide (TNO 2 CC) were derived from Sentinel-5P data. We employed statistical and local spatial autocorrelation analyses to investigate the spatialtemporal distribution and variation of TNO 2 CC across 346 major Chinese cities from 2019 to 2023. Using Random Forest (RF) and Shapley Additive Explanations (SHAP), we analyzed the influence of 15 natural factors on ambient TNO 2 CC levels. The high R² values (0.92 and 0.76), along with the close adherence to the 1:1 line, demonstrate the model’s robustness. The most influential natural factors identified include atmospheric pressure, aerosol optical depth, Leaf Area Index, evapotranspiration, and dew point temperature. Additionally, a non-linear response curve approach was applied to examine the independent association between natural driving factors and pollutant concentrations. TNO 2 CC varied seasonally across the 346 cities, with the highest levels in winter and the lowest in summer. From 2019 to 2023, TNO 2 CC levels exhibited fluctuating trends, with notable regional disparities: higher concentrations were observed in capital cities and in northern and northeastern part of China. TNO 2 CC were significantly influenced by temperature-related variables, aerosol optical depth, and leaf area index. The findings of this study identify key natural influencing factors and provide a scientific basis for revealing the causes of urban air pollution in China, informing pollution control strategies, identifying priority areas for remediation, and supporting the natural formulation of protection policies.