CHAP: High-resolution and High-quality Ambient Air Pollutants Dataset for China

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Published December 30, 2020 | Version Beta
Dataset Restricted

ChinaHighO3: Big Data Full-coverage Ground-level MDA8 O3 L2 Daily 0.1 Deg Product

Creators

  • 1. University of Maryland
  • 2. Nanjing University of Information Science and Technology
  • 3. University of Iowa
  • 4. Harvard Smithsonian Center for Astrophysics
  • 5. Shandong University of Science and Technology
  • 6. Qingdao University

Description

ChinaHighO3 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and emission inventory) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. 

This is the Big Data Level 2 (L2) daily 0.1 degree (≈ 10 km) gridded full-coverage ground-level maximum 8-hour average (MDA8) O3 products in China (CHAP_O3_D10K) from 2013 to 2020. This dataset has high accuracy with a cross-validation coefficient of determination (CV-R2) of 0.87 and a root-mean-square error (RMSE) of 17.10 µg m-3 on a daily basis.

If you use the ChinaHighO3 dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022). Note that this dataset is continuously updated, and if you need a longer period or higher temporal-resolution (e.g., daily, monthly) data, please contact the first author.

Our manuscript is just accepted in Remote Sensing of Environment and all the data will be made publicly available online once the paper is published.

Notes

Email: weijing_rs@163.com; weijing.rs@gmail.com

Files

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Additional details

Related works

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

References

  • 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, 268, 112775. https://doi.org/10.1016/j.rse.2021.112775