GHAP: Global High-resolution and High-quality Air Pollutant Dataset

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Published April 11, 2022 | Version 1
Dataset Restricted

GlobalHighPM₂.₅: Global Daily Seamless 1 km Ground-Level PM₂.₅ Dataset over Land (2017–Present)

Authors/Creators

  • 1. ROR icon University of Maryland, College Park
  • 2. ROR icon National Aeronautics and Space Administration
  • 3. ROR icon University of Iowa
  • 4. ROR icon Université de Lille
  • 5. ROR icon Harvard University
  • 6. ROR icon Shandong University of Science and Technology
  • 7. ROR icon Washington University in St. Louis
  • 8. ROR icon Southern University of Science and Technology
  • 9. ROR icon 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):

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

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

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Additional details

Identifiers

DOI
10.1038/s41467-023-43862-3

Related works

Is published in
Dataset: 10.1038/s41467-023-43862-3 (DOI)

Dates

Created
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