ChinaHighNO2: Big Data Seamless 1 km Ground-level NO2 Dataset for China
Creators
- 1. University of Maryland
- 2. Southern University of Science and Technology
- 3. University of Science and Technology of China
- 4. China University of Mining and Technology
- 5. Center for Astrophysics | Harvard and Smithsonian
- 6. Peking University
- 7. Wageningen University
- 8. Shandong University of Science and Technology
- 9. University of California, Irvine
- 10. Qingdao University
- 11. Hohai University
- 12. University of Iowa
Description
ChinaHighNO2 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.
This 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 in China from 2019 to 2020. This dataset yields a 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.
Note that the ChinaHighNO2 dataset is 1 km after 2019, but 10 km before 2019, which is available at https://doi.org/10.5281/zenodo.4641542. If you use the ChinaHighNO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ES&T, 2023; Wei et al., ACP, 2022):
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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
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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
More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html
Notes
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Additional details
References
- 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.