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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 ZENODOarrow_drop_down
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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ChinaHighPM1: Big Data Seamless 10 km Ground-level PM1 Dataset for China

Authors: Wei, Jing; Li, Zhanqing;

ChinaHighPM1: Big Data Seamless 10 km Ground-level PM1 Dataset for China

Abstract

ChinaHighPM1 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 10 km (i.e., D10K, M10K, and Y10K) ground-level PM1 dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.83 and a root-mean-square error (RMSE) of 9.25 µg m-3 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 ChinaHighPM1 dataset (Version 3) is available at: https://doi.org/10.5281/zenodo.3538555 More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

{"references": ["Wei, J., Li, Z., Guo, J., Sun, L., Huang, W., Xue, W., Fan, T., and Cribb, M. Satellite-derived 1-km-resolution PM1 concentrations from 2014 to 2018 across China. Environmental Science & Technology, 2019, 53(22), 13265\u201313274. https://doi.org/10.1021/acs.est.9b03258", "Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 269, 112775. https://doi.org/10.1016/j.rse.2021.112775"]}

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

Big data, Artificial intelligence, CHAP, ChinaHighPM1

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selected citations
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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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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