Published August 15, 2025 | Version 3
Dataset Open

ChinaHighO₃: Daily Seamless 1 km Ground-Level O₃ Dataset for China (2000–Present)

  • 1. ROR icon Beijing Normal University
  • 2. ROR icon University of Maryland, College Park

Description

ChinaHighO3 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 maximum daily 8-hour average (MDA8) O3 dataset for China from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.89, a root-mean-square error (RMSE) of 15.77 µg m-3, and a mean absolute error (MAE) of 10.48 µg m-3 on a daily basis.

If you use the ChinaHighO3 dataset in your scientific research, please cite the following references (Yang et al., RSE, 2025; Wei et al., RSE, 2022):

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 at the room condition (i.e., 298 K and 1013 hPa) (MEE, 2018).

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

Yang_et_al-RSE-2025.pdf

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Additional details

Related works

Is referenced by
Dataset: 10.1016/j.rse.2021.112775 (DOI)

Dates

Available
2020-12-30

References

  • Yang, Z., Li, Z., Cheng, F., Lv, Q., Li, K., Zhang, T., Zhou, Y., Zhao, B., Xue, W., and Wei, J. Two-decade surface ozone (O3) pollution in China: enhanced fine-scale estimations and environmental health implications. Remote Sensing of Environment, 2025, 317, 114459. https://doi.org/10.1016/j.rse.2024.114459
  • Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775