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Published April 11, 2022 | Version Beta
Dataset Restricted

GlobalHighPM2.5: Big Data Gapless 1 km Global Ground-level PM2.5 Dataset over Land

  • 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 one of the series of long-term, full-coverage, global high-resolution and high-quality datasets of ground-level air pollutants over land (i.e., GlobalHighAirPollutants, GHAP). It is generated from 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 dataset contains input data, analysis codes, and generated dataset used for the following article, and if you use the GlobalHighPM2.5 dataset for related scientific research, please cite the below-listed corresponding 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 gapless (spatial coverage = 100%) monthly and yearly 1 km (i.e., M1K, and Y1K) global ground-level PM2.5 dataset over land from 2017 to 2022. This dataset yields a high quality with cross-validation coefficient of determination (CV-R2) values 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 basises, 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 air quality datasets of different air pollutants can be found 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, and if you want to apply for more data or have any questions, please contact me (Email: weijing_rs@163.com; weijing@umd.edu).

Files

Restricted

The record is publicly accessible, but files are restricted to users with access.

Additional details

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