Published April 15, 2025 | Version 2
Dataset Open

ChinaHighSO₂: Daily Seamless 1 km Ground-Level SO₂ Dataset for China (2019–Present)

  • 1. ROR icon University of Maryland, College Park

Description

ChinaHighSO2 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 SO2 dataset for China from 2019 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset in your scientific research, please cite the following reference (Wei et al., ACP, 2023):

Note that the ChinaHighSO2 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.4641538

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).

Files

Wei_et_al-ACP-2023.pdf

Files (13.6 GB)

Name Size Download all
md5:4930367979fe304150bcc359086db1cb
2.3 GB Preview Download
md5:4683ab22884ab3c8193297512643cdca
2.3 GB Preview Download
md5:761492dd9b03bf56fabf3248c4fe2791
2.2 GB Preview Download
md5:f111a549865f4f5a42dbbe8c0d7fc534
2.2 GB Preview Download
md5:33cc27857590136c450eab3ac6bf8b78
2.1 GB Preview Download
md5:b62c356ec3d6636523bd91608988e7c5
2.1 GB Preview Download
md5:f88a5afcda5096a1206886ba0510cabe
65.1 MB Preview Download
md5:5d4feb1879c3783513d91147a27fde45
64.3 MB Preview Download
md5:94db9c4aa1532ef6ed523c2c7016ca43
61.1 MB Preview Download
md5:0acd18735f32a2be8719136d907ba672
61.6 MB Preview Download
md5:330442f6734d70efc20770c1b591d612
61.8 MB Preview Download
md5:bb0c342bae5d99bc68d27ced5aadd977
60.5 MB Preview Download
md5:c3adbb6d0686139668c6027c05c13679
5.5 MB Download
md5:f2b588ebee0595ba835fc5cddb6966b4
5.3 MB Download
md5:241a498dbf49c907cae5672d39750cd9
5.1 MB Download
md5:0d4857c155f19b37d88a1976995c3982
5.1 MB Download
md5:c5a7322a168cc51168891eb2bdf7fd51
5.1 MB Download
md5:245a026da4af766750ee610eed2d764e
5.1 MB Download
md5:6b152c60b85a2236762f0339676e31a5
3.0 kB Download
md5:39e31e794393fcfe42477f80f8c312a6
15.8 MB Preview Download

Additional details

Related works

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

Dates

Available
2021-03-27

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