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Published April 15, 2025 | Version 2
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ChinaHighCO: Daily Seamless 1 km Ground-Level CO Dataset for China (2019–Present)

  • 1. ROR icon University of Maryland, College Park

Description

ChinaHighCO 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 CO dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.80, a root-mean-square error (RMSE) of 0.29 mg m-3, and a mean absolute error (MAE) of 0.16 mg m-3 on a daily basis.

If you use the ChinaHighCO dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):

Note that the ChinaHighCO dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:

        all (including daily) data for the years 2013–2018 are accessible at: https://doi.org/10.5281/zenodo.4641530

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) and measured under room conditions (i.e., 298 K and 1013 hPa).

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).

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Additional details

Related works

Is referenced by
Dataset: 10.5194/acp-23-1511-2023 (DOI)

Dates

Available
2021-03-26

References

  • Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023