Estimate of near-surface NO₂ concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model

Nitrogen dioxide (NO₂) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO₂ vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO₂ concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R²) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO₂ concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO₂ concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO₂ concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO₂ concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO₂ concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO₂ pollution management.