Twin-satellite constraints from GF-5B and DQ-1 enable hourly near-surface NO2 estimation across China

Nitrogen dioxide (NO 2 ) is a major air pollutant with significant impacts on human health, yet satellite-based estimates of surface NO 2 with full diurnal coverage remain limited. Here, we develop a machine learning framework to estimate hourly near-surface NO 2 concentrations across China by integrating complementary observations from two Chinese polar-orbiting satellites, GF-5B (~10:30 local time) and DQ-1 (~13:30 local time). A Light Gradient Boosting Machine (LightGBM) model driven by twin-satellite NO 2 vertical column densities (VCDs) and meteorological reanalysis achieves strong predictive performance in 10-fold cross-validation, with a coefficient of determination (R 2 ) of 0.91 and root mean square error (RMSE) of 5.40 μg/m 3 across all hours (4.46 μg/m 3 during daytime; 6.32 μg/m 3 at night), and retains reasonable skill under stricter site-based, time-based, and independent cross-year validation. The reconstructed dataset captures the characteristic diurnal cycle of NO 2 , with morning and evening peaks associated with traffic emissions and a midday minimum driven by photochemical loss. Compared to the single-satellite TROPOMI product, the twin-satellite configuration substantially improves the representation of diurnal variability, increasing R 2 from 0.81 to 0.91 and reducing RMSE by 31.60%, and corrects a distorted urban-rural NO 2 exposure ratio (1.70 for TROPOMI versus a ground-based 1.19), narrowing it to 1.30. Feature-importance analysis further shows that meteorological variables dominate model predictions during both day and night, with satellite VCDs providing a complementary, though indirect, constraint on nighttime concentrations. The resulting hourly NO 2 dataset demonstrates the value of synergistic multi-satellite observations for air quality monitoring and provides a robust basis for refined pollution control and population exposure assessment.