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Description
No
Brief Dataset description:
ChinaHighPM2.5 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 PM2.5 dataset in China from 2000 to 2022. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.92, a root-mean-square error (RMSE) of 10.76 µg m-3, and a mean absolute error (MAE) of 6.32 µg m-3 on a daily basis.
Platform/Sensor:
MODIS Terra/Aqua
URL(s) of dataset description webpages:
URL to download data:
2000-2021:
2022:
Approximate size of the dataset:
74G
Dataset license:
Creative Commons Attribution 4.0 International
Intended use:
Additional information:
If you use the ChinaHighPM2.5 dataset for related scientific research, please cite the below-listed corresponding references first (Wei et al., RSE, 2021; Wei et al., ACP, 2020), and the reference will be updated once our new paper is accepted.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment, 2021, 252, 112136.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees. Atmospheric Chemistry and Physics, 2020, 20(6), 3273–3289.
Note that the data are recorded in local time (i.e., Beijing time: GMT+8). This dataset is continuously updated, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).