A dataset of near-surface NO₂ concentrations with a 1km² resolution over mainland China (2014 to 2020).

This dataset is based on the fusion of multiple satellite NO₂ column concentration data, combined with meteorological reanalysis data, human activity data and other geographically related variables, and uses machine learning models to generate the surface NO₂ concentration and its distribution in mainland China from 2014 to 2020. Among them, the "2014.7z, 2015.7z, 2016.7z, 2017.7z, 2018.7z, 2019.7z, 2020.7z" compressed packages save the monthly average values ​​of surface NO2 in mainland China in the corresponding years; the "Monthly_average.7z" compressed package saves the monthly average values ​​of surface NO2 in mainland China from 2014 to 2020; the "Annual_average.7z" compressed package saves the annual average values ​​of surface NO2 in mainland China from 2014 to 2020.By integrating two satellite products and multi-source geographic data, combined with machine learning algorithms, this dataset fully utilizes the advantages of satellite data in long-term spatiotemporal scales, and provides important support for revealing the spatiotemporal evolution of NO₂ concentrations in mainland China. The spatial resolution of 1km² ensures its high accuracy in pollutant distribution studies at the city level or even smaller scales, and is particularly suitable for fine-grained environmental pollution analysis. This dataset not only provides a scientific basis for environmental monitoring and policy making, but also can deeply explore the complex relationship between air quality and human activities through spatiotemporal change analysis, providing data support for pollution control and air quality improvement.