Hefei 100 m Hourly nitrogen dioxide (NO2) grid dataset

It is generated by a deep learning model based on multi-source observational data. This model integrates high-resolution traffic flow data and point-of-interest (POI) data at the 100-meter scale to characterize typical emission sources at fine spatial scales and adopts the Gaussian dispersion simulation method to explore the impacts of meteorological conditions and atmospheric transport processes on surface NO₂ concentrations.The reconstructed hourly 100-meter NO₂ datasets were validated using ground monitoring station networks, ground-based remote sensing observations and mobile field measurements, with corresponding correlation coefficients (R) of 0.81, 0.75 and 0.86 respectively.

This dataset can support the identification of subtle emission sources and the evaluation of inter-community NO₂ exposure disparities, providing a novel approach for urban air quality evaluation and pollution source apportionment research. Due to limitations of model input datasets, data gaps exist in certain periods, including May-June, October-November 2021, and February 2022.Unit: μg/m³; Spatial coverage: Hefei; Temporal resolution: Hourly; Spatial resolution: 100 m × 100 m.