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Dataset . 2021
Data sources: ZENODO
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2021
Data sources: Datacite
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Dataset . 2021
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ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for China (Closed)

Authors: Wei, Jing; Li, Zhanqing;

ChinaHighPM2.5: MODIS/Terra+Aqua 1 km Ground-level PM2.5 Dataset for China (Closed)

Abstract

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 MODIS/Terra+Aqua daily 1 km (D1K) ground-level PM2.5 dataset in Eastern China from 2013 to 2020, and this dataset yields a high quality with average cross-validation coefficient of determination (CV-R2) values ranging from 0.86 to 0.90, and RMSE values ranging from 10.0 to 18.4 μg/m3 on a daily basis. Note that this dataset is closed access since a longer-term (2000-2021), seamless, high-resolution (1 km), and higher quality ChinaHighPM2.5 dataset (Version 4) is available at: http://doi.org/10.5281/zenodo.3539349 More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

{"references": ["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. https://doi.org/10.1016/j.rse.2020.112136", "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. https://doi.org/10.5194/acp-20-3273-2020"]}

Note that 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.rs@gmail.com).

Related Organizations
Keywords

Remote Sensing, ChinaHighPM2.5, Artificial intelligence, CHAP

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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