GlobalHighPM₂.₅ (2022)
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1.
University of Maryland, College Park
Description
Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) global ground-level PM2.5 dataset over land for the year 2022. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.91 and a root-mean-square error (RMSE) of 9.20 µg m-3 on a daily basis.
If you use the GlobalHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., NC, 2023):
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Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3
Notes
Note that the data are recorded in UTC time (i.e., GMT+0).
This dataset is continuously updated. If you require additional data for related scientific research, please contact us (weijing_rs@163.com or weijing.rs@gmail.com).
Files
Wei_et_al-NC-2023.pdf
Files
(36.1 GB)
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Additional details
Dates
- Available
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2022-04-11
References
- Wei, J., Li, Z., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T. First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications, 2023, 14, 8349. https://doi.org/10.1038/s41467-023-43862-3