GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)
Creators
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1.
University of Maryland, College Park
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2.
National Aeronautics and Space Administration
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3.
University of Iowa
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4.
Université de Lille
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5.
Harvard University
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6.
Shandong University of Science and Technology
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7.
Washington University in St. Louis
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8.
Southern University of Science and Technology
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9.
Peking University
Description
GlobalHighPM2.5 is part of a series of long-term, seamless, global, high-resolution, and high-quality datasets of air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.
This dataset contains input data, analysis codes, and generated dataset used for the following article. 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
Input Data
Relevant raw data for each figure (compiled into a single sheet within an Excel document) in the manuscript.
Code
Relevant Python scripts for replicating and ploting the analysis results in the manuscript, as well as codes for converting data formats.
Generated Dataset
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 from 2017 to the present. This dataset exhibits high quality, with cross-validation coefficients of determination (CV-R2) of 0.91, 0.97, and 0.98, and root-mean-square errors (RMSEs) of 9.20, 4.15, and 2.77 µg m-3 on the daily, monthly, and annual bases, respectively.
Due to data volume limitations,
all (including daily) data for the year 2022 is accessible at: GlobalHighPM2.5 (2022)
all (including daily) data for the year 2021 is accessible at: GlobalHighPM2.5 (2021)
all (including daily) data for the year 2020 is accessible at: GlobalHighPM2.5 (2020)
all (including daily) data for the year 2019 is accessible at: GlobalHighPM2.5 (2019)
all (including daily) data for the year 2018 is accessible at: GlobalHighPM2.5 (2018)
all (including daily) data for the year 2017 is accessible at: GlobalHighPM2.5 (2017)
continuously updated...
More GHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html
Notes
Files
Wei_et_al-NC-2023.pdf
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Additional details
Identifiers
Related works
- Is published in
- Dataset: 10.1038/s41467-023-43862-3 (DOI)
Dates
- Created
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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