ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)

ChinaHighNO2 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution. Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m-3, and a mean absolute error (MAE) of 3.48 µg m-3 on a daily basis. If you use the ChinaHighNO2 dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023): Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence. Environmental Science & Technology, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834 Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023 Note that the ChinaHighNO2 dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km: all (including daily) data for the years 2008–2018 is accessible at: https://doi.org/10.5281/zenodo.4641542 More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html