Published April 15, 2020 | Version 4
Dataset Open

ChinaHighPM₁₀: Daily Seamless 1 km Ground-Level PM₁₀ Dataset for China (2000–Present)

  • 1. ROR icon University of Maryland, College Park

Description

ChinaHighPM10 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). 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.

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM10 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m-3, and a mean absolute error (MAE) of 11.22 µg m-3 on a daily basis.

If you use the ChinaHighPM10 dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021):

The data is continuously updated, and

        all (including daily) data for the year 2022 is accessible at: ChinaHighPM10 (2022)

        all (including daily) data for the year 2023 is accessible at: ChinaHighPM10 (2023)

        all (including daily) data for the year 2024 is accessible at: ChinaHighPM10 (2024)

        more data is coming soon...

More CHAP datasets for different air pollutants are available at: https://weijing-rs.github.io/product.html

Notes

Note that the data are recorded in local time (i.e., Beijing time: GMT+8)

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

ATBD_ChinaHighPM10.pdf

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Additional details

References

  • Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290