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 cross-validation coefficient of determination (CV-R2) values of 0.93, 0.95, and 0.96, and root-mean-square error (RMSE) values of 4.89, 3.11, and 2.35 µg m-3 on the daily, monthly, and yearly basises, respectively.
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 cross-validation coefficient of determination (CV-R2) values of 0.93, 0.95, and 0.96, and root-mean-square error (RMSE) values of 4.89, 3.11, and 2.35 µg m-3 on the daily, monthly, and yearly basises, respectively.
| Date made available | 1 Mar 2021 |
|---|---|
| Publisher | University of Maryland |
| Temporal coverage | 2019 - 2020 |
| Geographical coverage | China |
Research output
- 1 Article
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Ground-Level NO2 Surveillance from Space Across China for High Resolution Using Interpretable Spatiotemporally Weighted Artificial Intelligence
Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R. T., Dickerson, R. R., Lin, J., Boersma, K. F., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C. & Wang, J., 19 Jul 2022, In: Environmental Science and Technology. 56, 14, p. 9988-9998Research output: Contribution to journal › Article › Academic › peer-review
Open Access278 Link opens in a new tab Citations (Scopus)
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