ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)
Description
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 10 km (i.e., D10K, M10K, and Y10K) ground-level NO2 dataset for China from 2008 to 2018. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m-3, and a mean absolute error (MAE) of 5.34 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., ACP, 2023; Wei et al., EST, 2022):
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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
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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
Note that the ChinaHighNO2 dataset was improved to a 1 km resolution after 2019:
all (including daily) data for the years after 2019 are accessible at: https://doi.org/10.5281/zenodo.4571660
More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
Notes
Files
Wei_et_al-ACP-2023.pdf
Files
(826.3 MB)
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Additional details
Related works
- Is referenced by
- Journal article: 10.5194/acp-23-1511-2023 (DOI)
Dates
- Available
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2021-03-27
References
- 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
- 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